diff --git a/examples/learning_rl/IsaacGym_RofuncRL/example_DexterousHands_RofuncRL.py b/examples/learning_rl/IsaacGym_RofuncRL/example_DexterousHands_RofuncRL.py index b4cbcce2..c31c79ca 100644 --- a/examples/learning_rl/IsaacGym_RofuncRL/example_DexterousHands_RofuncRL.py +++ b/examples/learning_rl/IsaacGym_RofuncRL/example_DexterousHands_RofuncRL.py @@ -99,7 +99,7 @@ def inference(custom_args): # BiShadowHandSwitch, BiShadowHandTwoCatchUnderarm # QbSoftHandGrasp, BiQbSoftHandGraspAndPlace, BiQbSoftHandSynergyGrasp, QbSoftHandSynergyGrasp # ShadowHandGrasp - parser.add_argument("--task", type=str, default="ShadowHandGrasp") + parser.add_argument("--task", type=str, default="QbSoftHandSynergyGrasp") parser.add_argument("--agent", type=str, default="ppo") # Available agents: ppo, sac, td3, a2c parser.add_argument("--num_envs", type=int, default=4096) parser.add_argument("--sim_device", type=int, default=0) diff --git a/examples/learning_rl/IsaacGym_RofuncRL/example_Grasp_RofuncRL.py b/examples/learning_rl/IsaacGym_RofuncRL/example_Grasp_RofuncRL.py new file mode 100644 index 00000000..25e7977a --- /dev/null +++ b/examples/learning_rl/IsaacGym_RofuncRL/example_Grasp_RofuncRL.py @@ -0,0 +1,105 @@ +""" +Dexterous Hands (RofuncRL) +=========================== + +Examples of learning hand (Shadow Hand, Allegro Hand, qbSofthand) tasks by RofuncRL +""" + +import isaacgym +import argparse + +from rofunc.config.utils import omegaconf_to_dict, get_config +from rofunc.learning.RofuncRL.tasks import Tasks +from rofunc.learning.RofuncRL.trainers import Trainers +from rofunc.learning.pre_trained_models.download import model_zoo +from rofunc.learning.utils.utils import set_seed + + +def train(custom_args): + # Config task and trainer parameters for Isaac Gym environments + custom_args.num_envs = 64 if custom_args.agent.upper() in ["SAC", "TD3"] else custom_args.num_envs + + args_overrides = ["task={}".format(custom_args.task), + "train={}{}RofuncRL".format(custom_args.task, custom_args.agent.upper()), + "device_id={}".format(custom_args.sim_device), + "rl_device=cuda:{}".format(custom_args.rl_device), + "headless={}".format(custom_args.headless), + "num_envs={}".format(custom_args.num_envs)] + cfg = get_config('./learning/rl', 'config', args=args_overrides) + cfg.train.Trainer.maximum_steps = 1000000 + cfg_dict = omegaconf_to_dict(cfg.task) + + set_seed(cfg.train.Trainer.seed) + + # Instantiate the Isaac Gym environment + env = Tasks().task_map[custom_args.task](cfg=cfg_dict, + sim_device=f'cuda:{cfg.device_id}', + graphics_device_id=cfg.device_id, + headless=cfg.headless) + + # Instantiate the RL trainer + trainer = Trainers().trainer_map[custom_args.agent](cfg=cfg.train, + env=env, + device=cfg.rl_device, + env_name=custom_args.task) + + # Start training + trainer.train() + + +def inference(custom_args): + # Config task and trainer parameters for Isaac Gym environments + args_overrides = ["task={}".format(custom_args.task), + "train={}{}RofuncRL".format(custom_args.task, custom_args.agent.upper()), + "device_id={}".format(custom_args.sim_device), + "rl_device=cuda:{}".format(custom_args.rl_device), + "headless={}".format(False), + "num_envs={}".format(16)] + cfg = get_config('./learning/rl', 'config', args=args_overrides) + cfg_dict = omegaconf_to_dict(cfg.task) + + set_seed(cfg.train.Trainer.seed) + + # Instantiate the Isaac Gym environment + infer_env = Tasks().task_map[custom_args.task](cfg=cfg_dict, + rl_device=cfg.rl_device, + sim_device=f'cuda:{cfg.device_id}', + graphics_device_id=cfg.device_id, + headless=cfg.headless, + virtual_screen_capture=cfg.capture_video, # TODO: check + force_render=cfg.force_render) + + # Instantiate the RL trainer + trainer = Trainers().trainer_map[custom_args.agent](cfg=cfg.train, + env=infer_env, + device=cfg.rl_device, + env_name=custom_args.task, + inference=True) + # load checkpoint + if custom_args.ckpt_path is None: + custom_args.ckpt_path = model_zoo(name=f"{custom_args.task}RofuncRLPPO.pth") + trainer.agent.load_ckpt(custom_args.ckpt_path) + + # Start inference + trainer.inference() + + +if __name__ == '__main__': + gpu_id = 1 + + parser = argparse.ArgumentParser() + # Available tasks: LiftObject + parser.add_argument("--task", type=str, default="LiftObject") + parser.add_argument("--agent", type=str, default="ppo") # Available agents: ppo, sac, td3, a2c + parser.add_argument("--num_envs", type=int, default=256) + parser.add_argument("--sim_device", type=int, default=1) + parser.add_argument("--rl_device", type=int, default=gpu_id) + parser.add_argument("--headless", type=str, default="True") + parser.add_argument("--inference", action="store_true", help="turn to inference mode while adding this argument") + parser.add_argument("--ckpt_path", type=str, default=None) + custom_args = parser.parse_args() + + if not custom_args.inference: + train(custom_args) + else: + inference(custom_args) diff --git a/rofunc/config/learning/rl/task/LiftObject.yaml b/rofunc/config/learning/rl/task/LiftObject.yaml new file mode 100644 index 00000000..6af562e8 --- /dev/null +++ b/rofunc/config/learning/rl/task/LiftObject.yaml @@ -0,0 +1,109 @@ +name: LiftObject +experiment_name: ${resolve_default:LiftObject,${..experiment}} +physics_engine: ${..physics_engine} +rl_device: ${..rl_device} + +task: + # List of objects to be included in the task. + ycbObjects: [ "014_lemon" ] + egadObjects: [ ] + # List of measures/states to be returned as the observation. + observationType: [ "handPose","handDofPos", "objectPos", "objectRot" ] + # Dimensionality of said measures + num_observations: + fingertipContactForces: 15 + fingertipPos: 15 + handDofPos: 5 + handPose: 7 + jointPos: 17 + jointVel: 17 + objectBboxBounds: 6 + objectBboxCorners: 24 + objectBboxPose: 7 + objectSurfaceSamples: 60 + objectPos: 3 + objectLinVel: 3 + objectRot: 4 + objectAngVel: 3 + previousAction: 11 + returnObsDict: False + +debug: + verbose: True + visualization: False + drawEefPose: False + drawTrackerPose: False + drawFingertipPose: False + drawObjectPose: False + drawObjectBbox: False + drawObjectSurfaceSamples: False + drawHandObjectDistance: False + drawObjectTargetDistance: False + drawFingertipContactForces: False + colorObjectsOnSuccess: False + colorFingertipContactForce: False + +#cameras: +# save_recordings: ${...save_recordings} +# convert_to_pointcloud: True +# convert_to_voxelgrid: True +# camera0: +# type: rgbd +# pos: [ 0.0, -0.5, 1.3 ] +# lookat: [ 0, 0, 0.8 ] +# horizontal_fov: 70 +# width: 128 +# height: 128 +# camera1: +# type: rgbd +# pos: [ 0.0, 0.5, 1.3 ] +# lookat: [ 0, 0, 0.8 ] +# horizontal_fov: 70 +# width: 128 +# height: 128 + +initState: + noise: + objectPos: 0.05 + robotDofPos: 0.01 + +asset: + robotAssetFile: "urdf/sih/ur5e_schunk_sih_right.urdf" + tableAssetFile: "urdf/table/table.urdf" + ycbObjectAssetRoot: "urdf/ycb" + egadObjectAssetRoot: "urdf/egad/train" + robotProps: + armStiffness: 1e4 + armDamping: 300 + handStiffness: 10 + handDamping: 0.5 + +reward: + scale: + actionPenalty: 0.0 #-0.002 + fingertipsToObjectDistanceReward: 1.0 + objectHeightReward: 0.5 + objectVelocityReward: 0.0 + targetHeightReward: 5000.0 + objectFallsOffTablePenalty: -500.0 + liftOffHeight: 0.1 + maxXyDrift: 0.2 + targetHeight: 0.2 + epsHeight: 0.02 + epsFingertips: 1.0 + xyCenter: [0., 0.] + sparse: False + returnRewardsDict: False + +reset: + maxEpisodeLength: 300 + objectFallsOffTable: True + objectLifted: True + +defaults: + - control: default + - domainRandomization: default + - env: parallel + - haptics: default + - sim: default + - viewer: first_person diff --git a/rofunc/config/learning/rl/task/control/default.yaml b/rofunc/config/learning/rl/task/control/default.yaml new file mode 100644 index 00000000..c63b07e8 --- /dev/null +++ b/rofunc/config/learning/rl/task/control/default.yaml @@ -0,0 +1,9 @@ +useRelativeControl: True +actionMovingAverage: 1.0 +controlFreqInv: 3 # 1 / (controlFreqInv * dt) Hz +clampActionDeviation: True +teleoperated: False # Whether the simulation is controlled via teleoperation +scale: + relativePosSpeed: 2.5 + relativeRotSpeed: 10 + handJointSpeed: 10.0 \ No newline at end of file diff --git a/rofunc/config/learning/rl/task/domainRandomization/default.yaml b/rofunc/config/learning/rl/task/domainRandomization/default.yaml new file mode 100644 index 00000000..555b0982 --- /dev/null +++ b/rofunc/config/learning/rl/task/domainRandomization/default.yaml @@ -0,0 +1,3 @@ +enable: False +randomizationParams: + none: 0 \ No newline at end of file diff --git a/rofunc/config/learning/rl/task/haptics/default.yaml b/rofunc/config/learning/rl/task/haptics/default.yaml new file mode 100644 index 00000000..81b91a26 --- /dev/null +++ b/rofunc/config/learning/rl/task/haptics/default.yaml @@ -0,0 +1,10 @@ +enable: True +buzz: + proportional_to: 'high_freq' # Which property is mapped to vibration of the fingers ['force', 'force_increase', 'collision'] + full_at: 3. # Which value is equivalent to maximum vibration + collision_vibration_time: 0.09 # Time vibration is active after the impact of a finger + low_pass_horizon: 0.3 # [s] +force_feedback: + proportional_to: 'force' + full_at: 1.0 # Which effective fingertip force is equivalent to maximum feedback force + binary: True # Whether to apply feedback force at 100% once full_at is reached and 0% else \ No newline at end of file diff --git a/rofunc/config/learning/rl/task/sim/default.yaml b/rofunc/config/learning/rl/task/sim/default.yaml new file mode 100644 index 00000000..597f16f2 --- /dev/null +++ b/rofunc/config/learning/rl/task/sim/default.yaml @@ -0,0 +1,22 @@ +dt: 0.011 # 1/90 [s] +substeps: 2 +up_axis: "z" +enableCameraSensors: False +useContactForces: True +useForceSensors: False +use_gpu_pipeline: ${eq:${...pipeline},"gpu"} +gravity: [0.0, 0.0, -9.81] +physx: + num_threads: ${....num_threads} + solver_type: ${....solver_type} + use_gpu: ${contains:"cuda",${....sim_device}} # set to False to run on CPU + num_position_iterations: 8 + num_velocity_iterations: 0 + max_gpu_contact_pairs: 8388608 # 8*1024*1024 + num_subscenes: ${....num_subscenes} + contact_offset: 0.002 + rest_offset: 0.0 + bounce_threshold_velocity: 0.2 + max_depenetration_velocity: 1000.0 + default_buffer_size_multiplier: 5.0 + contact_collection: 2 # 0: CC_NEVER (don't collect contact info), 1: CC_LAST_SUBSTEP (collect only contacts on last substep), 2: CC_ALL_SUBSTEPS (default - all contacts) \ No newline at end of file diff --git a/rofunc/config/learning/rl/task/viewer/first_person.yaml b/rofunc/config/learning/rl/task/viewer/first_person.yaml new file mode 100644 index 00000000..4bd41638 --- /dev/null +++ b/rofunc/config/learning/rl/task/viewer/first_person.yaml @@ -0,0 +1,2 @@ +pos: [-0.45, -0.55, 1.35] +lookat: [0, 0, 0.9] \ No newline at end of file diff --git a/rofunc/config/learning/rl/task/viewer/front_view.yaml b/rofunc/config/learning/rl/task/viewer/front_view.yaml new file mode 100644 index 00000000..38f9ff68 --- /dev/null +++ b/rofunc/config/learning/rl/task/viewer/front_view.yaml @@ -0,0 +1,2 @@ +pos: [0, 0.8, 1.35] +lookat: [0, 0, 0.9] \ No newline at end of file diff --git a/rofunc/config/learning/rl/train/BaseTaskPPORofuncRL.yaml b/rofunc/config/learning/rl/train/BaseTaskPPORofuncRL.yaml index 43370b99..317cbd45 100644 --- a/rofunc/config/learning/rl/train/BaseTaskPPORofuncRL.yaml +++ b/rofunc/config/learning/rl/train/BaseTaskPPORofuncRL.yaml @@ -60,7 +60,7 @@ Agent: # ========= Model parameters ========== Model: use_init: True - use_action_clip: False # If true, clip actions to the action space range. + use_action_clip: True # If true, clip actions to the action space range. use_same_model: False # If true, use the same model for the actor and critic. use_action_out_tanh: True # If true, use tanh to scale the action outputs to the action space range. action_clip: 1.0 # clipping coefficient for the norm of the actions diff --git a/rofunc/learning/RofuncRL/tasks/__init__.py b/rofunc/learning/RofuncRL/tasks/__init__.py index 51fa43ae..b5c7543b 100644 --- a/rofunc/learning/RofuncRL/tasks/__init__.py +++ b/rofunc/learning/RofuncRL/tasks/__init__.py @@ -45,6 +45,7 @@ def __init__(self, env_type="isaacgym"): from .isaacgymenv.hands.bi_qbhand_synergy_grasp import BiQbSoftHandSynergyGraspTask from .isaacgymenv.hands.qbhand_synergy_grasp import QbSoftHandSynergyGraspTask from .isaacgymenv.hands.shadow_hand_grasp import ShadowHandGraspTask + from .isaacgymenv.grasp.lift_object import LiftObjectTask self.task_map = { "Ant": AntTask, @@ -90,6 +91,7 @@ def __init__(self, env_type="isaacgym"): "BiQbSoftHandSynergyGrasp": BiQbSoftHandSynergyGraspTask, "QbSoftHandSynergyGrasp": QbSoftHandSynergyGraspTask, "ShadowHandGrasp": ShadowHandGraspTask, + "LiftObject": LiftObjectTask, } elif env_type == "omniisaacgym": # OmniIsaacGym tasks diff --git a/rofunc/learning/RofuncRL/tasks/isaacgymenv/grasp/base/base_task.py b/rofunc/learning/RofuncRL/tasks/isaacgymenv/grasp/base/base_task.py new file mode 100644 index 00000000..c8791115 --- /dev/null +++ b/rofunc/learning/RofuncRL/tasks/isaacgymenv/grasp/base/base_task.py @@ -0,0 +1,234 @@ +# Copyright (c) 2018-2022, NVIDIA Corporation +# All rights reserved. +# +# Redistribution and use in source and binary forms, with or without +# modification, are permitted provided that the following conditions are met: +# +# 1. Redistributions of source code must retain the above copyright notice, this +# list of conditions and the following disclaimer. +# +# 2. Redistributions in binary form must reproduce the above copyright notice, +# this list of conditions and the following disclaimer in the documentation +# and/or other materials provided with the distribution. +# +# 3. Neither the name of the copyright holder nor the names of its +# contributors may be used to endorse or promote products derived from +# this software without specific prior written permission. +# +# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE +# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, +# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + + +from abc import ABC, abstractmethod +from gym import spaces, Space +from isaacgym import gymapi +import numpy as np +import torch +from typing import * +import warnings + + +class Task(ABC): + def __init__( + self, + cfg: Dict[str, Any], + sim_device: str, + graphics_device_id: int, + headless: bool + ) -> None: + """Initializes the basic reinforcement learning task. + + Args: + cfg: Configuration dictionary that contains parameters for + how the simulation should run and defines properties of the + task. + sim_device: Device on which the physics simulation is run + (e.g. "cuda:0" or "cpu"). + graphics_device_id: ID of the device to run the rendering. + headless: Whether to run the simulation without a viewer. + """ + self.cfg = cfg + self.headless = headless + self._init_device(sim_device, cfg) + self._init_graphics_device(graphics_device_id, cfg) + self._init_environment_specs(cfg) + self.sim_params = self._parse_sim_params(cfg["physics_engine"], + cfg["sim"]) + self._set_optimization_flags() + + self.clip_obs = cfg["env"].get("clipObservations", np.Inf) + self.clip_actions = cfg["env"].get("clipActions", np.Inf) + self.step_simulation = step_simulation + + def __len__(self) -> int: + return self.num_envs + + def __repr__(self) -> str: + return (f"{self.cfg['name']} " + f"(num_envs={len(self)}, " + f"observation_space={self.observation_space}," + f"action_space={self.action_space})") + + def _init_device(self, sim_device: str, cfg: Dict[str, Any]) -> None: + split_device = sim_device.split(":") + self.device_type = split_device[0] + self.device_id = int(split_device[1]) if len(split_device) > 1 else 0 + + self.device = "cpu" + if cfg["sim"]["use_gpu_pipeline"]: + if self.device_type.lower() in ["gpu", "cuda"]: + self.device = "cuda" + ":" + str(self.device_id) + else: + warnings.warn("Tried to use GPU pipeline with simulation " + "running on CPU.") + cfg["sim"]["use_gpu_pipeline"] = False + self.rl_device = cfg.get("rl_device", "cuda:0") + + def _init_graphics_device(self, graphics_device_id: int, + cfg: Dict[str, Any]) -> None: + enable_camera_sensors = cfg["sim"].get("enableCameraSensors", False) + if "cameras" in cfg.keys(): + enable_camera_sensors = True + + self.graphics_device_id = graphics_device_id + if enable_camera_sensors is False and self.headless is True: + self.graphics_device_id = -1 + + def _init_environment_specs(self, cfg: Dict[str, Any]) -> None: + self.num_environments = cfg["env"]["numEnvs"] + self.num_agents = cfg["env"].get("numAgents", 1) + self.num_observations = cfg["env"]["numObservations"] + self.num_states = cfg["env"].get("numStates", 0) + self.num_actions = cfg["env"]["numActions"] + + self.control_freq_inv = cfg["control"].get("controlFreqInv", 1) + + self.obs_space = spaces.Box(np.ones(self.num_obs) * -np.Inf, + np.ones(self.num_obs) * np.Inf) + self.state_space = spaces.Box(np.ones(self.num_states) * -np.Inf, + np.ones(self.num_states) * np.Inf) + + self.act_space = spaces.Box(np.ones(self.num_actions) * -1., + np.ones(self.num_actions) * 1.) + + def _parse_sim_params(self, physics_engine: str, + sim_cfg: Dict[str, Any]) -> gymapi.SimParams: + sim_params = gymapi.SimParams() + + sim_params.dt = sim_cfg["dt"] + sim_params.num_client_threads = sim_cfg.get("num_client_threads", 0) + sim_params.use_gpu_pipeline = sim_cfg["use_gpu_pipeline"] + sim_params.substeps = sim_cfg.get("substeps", 2) + sim_params.gravity = gymapi.Vec3(*sim_cfg["gravity"]) + + if sim_cfg["up_axis"] == "z": + self.up_axis_idx = 2 + sim_params.up_axis = gymapi.UP_AXIS_Z + elif sim_cfg["up_axis"] == "y": + self.up_axis_idx = 1 + sim_params.up_axis = gymapi.UP_AXIS_Y + else: + raise ValueError(f"Invalid value for up-axis given.") + + if physics_engine == "physx": + self.physics_engine = gymapi.SIM_PHYSX + if "physx" in sim_cfg: + for opt in sim_cfg["physx"].keys(): + if opt == "contact_collection": + setattr(sim_params.physx, opt, + gymapi.ContactCollection(sim_cfg["physx"][opt])) + else: + setattr(sim_params.physx, opt, sim_cfg["physx"][opt]) + else: + raise ValueError("Physics engine should always be PhysX.") + return sim_params + + @staticmethod + def _set_optimization_flags() -> None: + torch._C._jit_set_profiling_mode(False) + torch._C._jit_set_profiling_executor(False) + + @abstractmethod + def allocate_buffers(self): + """Allocate PyTorch buffers for obs, rew, etc.""" + raise NotImplementedError + + @abstractmethod + def step( + self, + actions: torch.Tensor + ) -> Tuple[ + Dict[str, torch.Tensor], + torch.Tensor, + torch.Tensor, + Dict[str, Any]]: + """Steps the environment. + + Args: + actions: Actions to apply to the environments + Returns: + obs, rew, done, info + """ + raise NotImplementedError + + @abstractmethod + def pre_physics_step(self, actions: torch.Tensor): + """Applies actions to the environment. + + Args: + actions: Actions to apply. + """ + raise NotImplementedError + + @abstractmethod + def post_physics_step(self): + """Steps the environment. Computes observation, rewards and resets any + envs that require it.""" + raise NotImplementedError + + @abstractmethod + def reset(self) -> Dict[str, torch.Tensor]: + """Reset all environments. + + Returns: + obs + """ + raise NotImplementedError + + @abstractmethod + def reset_idx(self, env_ids: torch.Tensor) -> None: + """Reset environments specified by env_ids. + """ + raise NotImplementedError + + @property + def observation_space(self) -> Space: + return self.obs_space + + @property + def action_space(self) -> Space: + return self.act_space + + @property + def num_envs(self) -> int: + return self.num_environments + + @property + def num_acts(self) -> int: + return self.num_actions + + @property + def num_obs(self) -> int: + return self.num_observations + + +def step_simulation(gym, sim) -> None: + gym.simulate(sim) diff --git a/rofunc/learning/RofuncRL/tasks/isaacgymenv/grasp/base/camera_task.py b/rofunc/learning/RofuncRL/tasks/isaacgymenv/grasp/base/camera_task.py new file mode 100644 index 00000000..4105c1b4 --- /dev/null +++ b/rofunc/learning/RofuncRL/tasks/isaacgymenv/grasp/base/camera_task.py @@ -0,0 +1,393 @@ +from abc import ABC +import cv2 +from isaacgym import gymapi, gymtorch +from rofunc.learning.RofuncRL.tasks.isaacgymenv.grasp.utils.transform import fuse_rgbd_to_pointcloud, \ + subsample_valid, pointcloud_to_voxelgrid +import numpy as np +import os +import torch +from typing import * + + +class CameraTaskMixin(ABC): + """Provides methods to configure camera setups from the environment's config + file. + + Examples: + The following block specifies a camera setup in the config file. The + first three keywords define whether to save MP4 recordings of the + rollouts from all cameras and environment instances, as well as whether + RGBD images should be converted to further to point-cloud or voxel-grid + representations. + Every following block adds a new camera to the environment. Supported + camera types are 'rgb' and 'rgbd'. The current images of all cameras are + stored in a buffer of shape (num_envs, num_cameras, height, width, + num_channels), hence requiring cameras to have the same type and image + dimensions if multiple cameras are being used. This data will be added + to the obs_dict under the 'image' key. + + cameras: + save_recordings: ${...save_recordings} + convert_to_pointcloud: True + convert_to_voxelgrid: False + camera0: + type: rgbd + pos: [ 0.0, -0.5, 1.3 ] + lookat: [ 0, 0, 0.8 ] + horizontal_fov: 70 + width: 128 + height: 128 + camera1: + type: rgbd + pos: [ 0.0, 0.5, 1.3 ] + lookat: [ 0, 0, 0.8 ] + horizontal_fov: 70 + width: 128 + height: 128 + """ + + def _create_cameras(self, cfg: Dict[str, Dict]) -> None: + """Creates camera sensors according to the provided config file. + + Args: + cfg: Camera config. + """ + self.camera_handles, self.camera_names = [], [] + for i in range(self.num_envs): + self.camera_handles.append([]) + for camera_name, camera_cfg in cfg.items(): + if camera_name in ["save_recordings", "convert_to_pointcloud", + "convert_to_voxelgrid"]: + continue + self.camera_names.append(camera_name) + camera_properties = gymapi.CameraProperties() + camera_properties.width = camera_cfg["width"] + camera_properties.height = camera_cfg["height"] + camera_properties.horizontal_fov = camera_cfg["horizontal_fov"] + if self.device.startswith("cuda"): + camera_properties.enable_tensors = True + camera = self.gym.create_camera_sensor(self.envs[i], + camera_properties) + pos = gymapi.Vec3(*camera_cfg["pos"]) + lookat = gymapi.Vec3(*camera_cfg["lookat"]) + self.gym.set_camera_location(camera, self.envs[i], pos, lookat) + self.camera_handles[i].append(camera) + + self.num_cameras = len(self.camera_handles[0]) + self.camera_types = [t for t in [cfg[n]["type"] + for n in self.camera_names]] + self.camera_width = cfg[self.camera_names[0]]["width"] + self.camera_height = cfg[self.camera_names[0]]["height"] + self.convert_to_pointcloud = cfg["convert_to_pointcloud"] + self.convert_to_voxelgrid = cfg["convert_to_voxelgrid"] + + self._infer_camera_matrices() + + if cfg["save_recordings"]: + self._create_recordings() + + def _infer_camera_matrices(self) -> None: + """Infers camera matrices, which are required to convert the image data + to point-cloud or voxel-grid representations. + """ + self.proj_mat = torch.from_numpy( + self.gym.get_camera_proj_matrix( + self.sim, self.envs[0], self.camera_handles[0][0]) + ).to(self.device) + self.view_mat = [] + for cam_idx in range(self.num_cameras): + view_mat = torch.from_numpy( + self.gym.get_camera_view_matrix(self.sim, self.envs[0], + self.camera_handles[0][ + cam_idx])).to(self.device) + self.view_mat.append(view_mat) + self.view_mat = torch.stack(self.view_mat) + + def _create_recordings(self) -> None: + """Initializes video writers for all cameras, image types, and + environment instances. + """ + train_dir = self.cfg.get("train_dir", "runs") + experiment_name = self.cfg["experiment_name"] + experiment_dir = os.path.join(train_dir, experiment_name) + self.recording_dir = os.path.join(experiment_dir, "recordings") + if not os.path.exists(self.recording_dir): + os.mkdir(self.recording_dir) + self.fourcc = cv2.VideoWriter_fourcc(*'mp4v') + self.recording_episodes = [0 for _ in range(self.num_envs)] + self.image_types = ["color", "depth"] if \ + self.camera_types[0] == "rgbd" else ["color"] + + self.recordings = [[[None + for cam_id in range(self.num_cameras)] + for image_type in self.image_types] + for env_id in range(self.num_envs)] + + if self.convert_to_pointcloud: + self.image_types.append("point_cloud") + + for env_id in range(self.num_envs): + self.recordings[env_id].append([None for _ in range(self.num_cameras)]) + + from pytorch3d.renderer import ( + look_at_view_transform, + FoVOrthographicCameras, + PointsRasterizationSettings, + PointsRasterizer, + PulsarPointsRenderer + ) + R, T = look_at_view_transform(eye=((0.75, -0.4, 1.15),), up=((0, 0, 1),), + at=((0., 0., 0.8),),) + cameras = FoVOrthographicCameras(device=self.device, R=R, T=T, + znear=0.01) + raster_settings = PointsRasterizationSettings( + image_size=(1080, 1920), + radius=0.003, + points_per_pixel=1 + ) + self.pc_renderer = PulsarPointsRenderer( + rasterizer=PointsRasterizer(cameras=cameras, + raster_settings=raster_settings), + n_channels=3 + ).to(self.device) + + if self.convert_to_voxelgrid: + self.image_types.append("voxel_grid") + for env_id in range(self.num_envs): + self.recordings[env_id].append([None for _ in range(self.num_cameras)]) + + def _set_camera(self, pos: List, lookat: List) -> None: + if self.viewer is not None: + pos = gymapi.Vec3(*pos) + lookat = gymapi.Vec3(*lookat) + self.gym.viewer_camera_look_at(self.viewer, None, pos, lookat) + + def _render_camera_sensors(self) -> None: + """Renders all camera images and writes their content to the camera + buffer of shape (num_envs, num_cameras, height, width, channel). This + means that if multiple cameras are used they must have the same + image dimensions.""" + if self.device != 'cpu': + self.gym.fetch_results(self.sim, True) + if self.headless: + self.gym.step_graphics(self.sim) + self.gym.render_all_camera_sensors(self.sim) + + if self.device.startswith("cuda"): + self.gym.start_access_image_tensors(self.sim) + images = [] + for i in range(self.num_envs): + images.append([]) + for j in range(self.num_cameras): + images[i].append(self._get_image( + self.envs[i], self.camera_handles[i][j], + self.camera_types[j], self.device.startswith("cuda"))) + images[i] = torch.stack(images[i]) + images = torch.stack(images) + + if self.device.startswith("cuda"): + self.gym.end_access_image_tensors(self.sim) + + self.image_buf[:] = images + + def _convert_rgbd_to_pointcloud(self, + convert_rgb_to_camera_id: bool = False, + visualize: bool = False + ) -> None: + """Fuses the RGBD views of all cameras into a colored point-cloud. + + Args: + convert_rgb_to_camera_id: Whether to use color channels to identify + which camera a point belongs to. + visualize: Whether to visualize point-clouds in plotly. + """ + xyz, features = fuse_rgbd_to_pointcloud( + self.image_buf, self.proj_mat, self.view_mat, + convert_rgb_to_camera_id) + self.pointcloud_buf[..., 0:3] = xyz + self.pointcloud_buf[..., 3:6] = features + + if visualize: + from pytorch3d.structures import Pointclouds + from pytorch3d.vis.plotly_vis import plot_scene + plotly_figs = [] + for env_idx in range(self.num_envs): + views = {} + for cam_idx in range(self.num_cameras): + xyz_tmp, features_tmp = subsample_valid( + xyz[env_idx, cam_idx], features[env_idx, cam_idx]) + views[self.camera_names[cam_idx]] = Pointclouds( + points=[xyz_tmp], + features=[features_tmp]) + plotly_figs.append(plot_scene({ + f"Pointcloud in environment {env_idx}": views, + })) + for i in range(min(4, len(plotly_figs))): + plotly_figs[i].show() + import time + time.sleep(10) + + def _convert_pointcloud_to_voxelgrid(self, + visualize: bool = False + ) -> None: + """Converts the colored point-cloud into a voxel-grid. + + Args: + visualize: Whether to visualize the resulting voxelgrid in + matplotlib. + """ + + xyz = self.pointcloud_buf[..., 0:3] + features = self.pointcloud_buf[..., 3:6] + voxel_grid, voxel_features = pointcloud_to_voxelgrid(xyz, features) + + self.voxelgrid_buf[..., 0] = voxel_grid + self.voxelgrid_buf[..., 1:4] = voxel_features + + if visualize: + import matplotlib.pyplot as plt + voxel_array = voxel_grid[0].cpu().numpy().astype(bool) + colors = voxel_features[0].cpu().numpy() + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + ax.voxels(voxel_array, facecolors=colors) + ax.set_axis_off() + plt.show() + + def _get_image(self, env_ptr, camera, image_type: str, + gpu_tensors: bool) -> torch.Tensor: + """Called when rendering the camera sensors. Queries the camera images + from Isaac Gym and puts them into the desired format. + """ + if image_type == "rgb": + image_types = [gymapi.IMAGE_COLOR] + elif image_type == "rgbd": + image_types = [gymapi.IMAGE_COLOR, gymapi.IMAGE_DEPTH] + else: + assert False + + image = [] + for t in image_types: + if gpu_tensors: + img_tensor = self.gym.get_camera_image_gpu_tensor( + self.sim, env_ptr, camera, t) + img = gymtorch.wrap_tensor(img_tensor) + else: + img = self.gym.get_camera_image(self.sim, env_ptr, camera, t) + img = torch.Tensor(img).view(img.shape[0], -1, 4) + + if t == gymapi.IMAGE_COLOR: + img = img[..., 0:3] / 255. + elif t == gymapi.IMAGE_DEPTH: + img = img.unsqueeze(-1) + + image.append(img) + image = torch.cat(image, dim=2) + return image + + def _write_recordings(self) -> None: + """Appends the latest camera images to the MP4 recordings. + """ + for env_id in range(self.num_envs): + for cam_id in range(self.num_cameras): + for i, image_type in enumerate(self.image_types): + if image_type == "color": + image = self.image_buf[ + env_id, cam_id][..., 0:3].cpu().numpy() + image *= 255 + image = image.astype(np.uint8)[..., ::-1] + elif image_type == "depth": + image = self.image_buf[ + env_id, cam_id][..., -1:].cpu().numpy() + max_dist = 2 + image *= -255 / max_dist + image = np.clip(image, 0, 255) + image = 255 - image + image = image.astype(np.uint8) + image = np.repeat(image, 3, axis=2) + elif image_type == "point_cloud": + # Render merged point cloud from all camera views + if cam_id != 0: + continue + xyz = self.pointcloud_buf[ + env_id, ..., 0:3].view(-1, 3) + features = self.pointcloud_buf[ + env_id, ..., 3:6].view(-1, 3) + xyz_valid, features_valid = subsample_valid(xyz, + features) + image = self._render_pointcloud_to_image( + xyz_valid, features_valid).cpu().numpy()[0] + image *= 255 + image = image.astype(np.uint8)[..., ::-1] + + elif image_type == "voxel_grid": + # Render voxel grid + if cam_id != 0: + continue + voxel_array = self.voxelgrid_buf[env_id, ..., 0].cpu().numpy().astype(bool) + colors = self.voxelgrid_buf[env_id, ..., 1:4].cpu().numpy() + + image = self._render_voxelgrid_to_image(voxel_array, + colors) + else: + assert False + self.recordings[env_id][i][cam_id].write(image) + + def reset_recordings_idx(self, env_ids): + for env_id in env_ids: + for cam_id in range(self.num_cameras): + for i, image_type in enumerate(self.image_types): + if self.recordings[env_id][i][cam_id] is not None: + self.recordings[env_id][i][cam_id].release() + if i < 2: + self.recordings[env_id][i][cam_id] = \ + cv2.VideoWriter( + os.path.join(self.recording_dir, + f"recording_env_{env_id}_camera_{cam_id}" + f"_episode_" + f"{self.recording_episodes[env_id]}_" + f"{image_type}.mp4"), + self.fourcc, 1 / (self.dt * self.control_freq_inv), + (self.camera_width, self.camera_height)) + elif i == 2: + if cam_id == 0: + self.recordings[env_id][i][ + cam_id] = cv2.VideoWriter( + os.path.join(self.recording_dir, + f"recording_env_{env_id}_episode_" + f"{self.recording_episodes[env_id]}_" + f"{image_type}.mp4"), + self.fourcc, 1 / (self.dt * self.control_freq_inv), + (1920, 1080)) + elif i == 3: + self.recordings[env_id][i][ + cam_id] = cv2.VideoWriter( + os.path.join(self.recording_dir, + f"recording_env_{env_id}_episode_" + f"{self.recording_episodes[env_id]}_" + f"{image_type}.mp4"), + self.fourcc, 1 / (self.dt * self.control_freq_inv), + (640, 480)) + self.recording_episodes[env_id] += 1 + + def _render_pointcloud_to_image(self, xyz: torch.Tensor, + features: torch.Tensor) -> torch.Tensor: + """Used to create recordings of the point-clouds. + """ + from pytorch3d.structures import Pointclouds + point_cloud = Pointclouds(points=xyz.unsqueeze(0), features=features.unsqueeze(0)) + image = self.pc_renderer(point_cloud, gamma=(1e-4,)) + return image + + def _render_voxelgrid_to_image(self, voxel_grid, colors) -> np.ndarray: + import pyvista as pv + + grid = pv.UniformGrid() + + grid.dimensions = np.array(voxel_grid.shape) + 1 + grid.plot(show_edges=True) + + import time + time.sleep(1000) + data = 0 + return data diff --git a/rofunc/learning/RofuncRL/tasks/isaacgymenv/grasp/base/grasping_task.py b/rofunc/learning/RofuncRL/tasks/isaacgymenv/grasp/base/grasping_task.py new file mode 100644 index 00000000..a2a12b1f --- /dev/null +++ b/rofunc/learning/RofuncRL/tasks/isaacgymenv/grasp/base/grasping_task.py @@ -0,0 +1,1382 @@ +import collections +from rofunc.learning.RofuncRL.tasks.isaacgymenv.grasp.base.base_task import Task +from rofunc.learning.RofuncRL.tasks.isaacgymenv.grasp.base.camera_task import CameraTaskMixin +from isaacgym import gymapi, gymtorch +from isaacgym.torch_utils import * +import os +import sys +from torch.utils.tensorboard import SummaryWriter +from typing import * +from rofunc.utils.oslab import get_rofunc_path + + +class GraspingTask(Task, CameraTaskMixin): + def __init__( + self, + cfg: Dict[str, Any], + sim_device: str, + graphics_device_id: int, + headless: bool + ) -> None: + """Initializes the control class for all grasping/ dexterous hand + manipulation tasks. + + Args: + cfg: Configuration dictionary that contains parameters for + how the simulation should run and defines properties of the + task. + sim_device: Device on which the physics simulation is run + (e.g. "cuda:0" or "cpu"). + graphics_device_id: ID of the device to run the rendering. + headless: Whether to run the simulation without a viewer. + """ + self.cfg = cfg + self._parse_cfg(cfg) + super().__init__(cfg, sim_device, graphics_device_id, headless) + + self.gym = gymapi.acquire_gym() + + self.first_randomization = True + self.original_props = {} + self.dr_randomizations = {} + self.actor_params_generator = None + self.extern_actor_params = {} + self.last_step = -1 + self.last_rand_step = -1 + for env_id in range(self.num_envs): + self.extern_actor_params[env_id] = None + + self.sim_initialized = False + self.sim = self.create_sim() + self.create_envs(self.num_envs, self.cfg["env"]["envSpacing"], + int(np.sqrt(self.num_envs))) + + self.gym.prepare_sim(self.sim) + self.sim_initialized = True + + self.viewer = self.create_viewer() + self.allocate_buffers() + self._init_buffers() + self.obs_dict = {} + self.obs_separate = {} + + self.initial_reset = True + + if not headless: + self._set_camera(cfg["viewer"]["pos"], + cfg["viewer"]["lookat"]) + + if "cameras" in cfg.keys(): + self._create_cameras(cfg["cameras"]) + + def _parse_cfg(self, cfg: Dict[str, Any]) -> None: + # TEMPORARY + self.force_torque_obs_scale = 1.0 + + # General simulation parameters + self.dt = cfg["sim"]["dt"] + self.max_episode_length = cfg["reset"]["maxEpisodeLength"] + self.aggregate_mode = cfg["env"]["aggregateMode"] + self.up_axis = "z" + + # number of actions for UR5-SIH robot + self.cfg["env"]["numActions"] = 11 + + # Reset object pose and robot joint angles + self.reset_time = self.cfg["env"].get("resetTime", -1.0) + + # Specify observation-space of environment + self.cfg["env"]["numObservations"] = 0 + self.cfg["env"]["numStates"] = 0 + + # Overwrite episode length if reset_time is specified + if self.reset_time > 0.0: + self.max_episode_length = int( + round(self.reset_time / + (self.cfg["control"]["controlFreqInv"] * self.dt))) + print("Reset time: ", self.reset_time) + print("New episode length: ", self.max_episode_length) + + # quick and dirty fix for old configs + if "returnObsDict" not in self.cfg["task"].keys(): + self.cfg["task"]["returnObsDict"] = False + self.cfg["reward"]["returnRewardsDict"] = False + + self._init_summary_writer(cfg) + + def _init_summary_writer(self, cfg: Dict[str, Any]) -> None: + train_dir = cfg.get("train_dir", "runs") + experiment_name = cfg["experiment_name"] + experiment_dir = os.path.join(train_dir, experiment_name) + summaries_dir = os.path.join(experiment_dir, "summaries") + log_dir = os.path.join(summaries_dir, "env_stats") + self.writer = SummaryWriter(log_dir) + + def create_viewer(self): + self.enable_viewer_sync = True + viewer = None + + if not self.headless: + viewer = self.gym.create_viewer( + self.sim, gymapi.CameraProperties()) + self.gym.subscribe_viewer_keyboard_event( + viewer, gymapi.KEY_ESCAPE, "QUIT") + self.gym.subscribe_viewer_keyboard_event( + viewer, gymapi.KEY_V, "toggle_viewer_sync") + return viewer + + def allocate_buffers(self): + """Allocate the observation, states, etc. buffers. + + These are what is used to set observations and states in the environment classes which + inherit from this one, and are read in `step` and other related functions. + + """ + + # allocate buffers + self.obs_buf = torch.zeros( + (self.num_envs, self.num_obs), device=self.device, dtype=torch.float) + self.states_buf = torch.zeros( + (self.num_envs, self.num_states), device=self.device, dtype=torch.float) + self.rew_buf = torch.zeros( + self.num_envs, device=self.device, dtype=torch.float) + self.reset_buf = torch.ones( + self.num_envs, device=self.device, dtype=torch.long) + self.timeout_buf = torch.zeros( + self.num_envs, device=self.device, dtype=torch.long) + self.progress_buf = torch.zeros( + self.num_envs, device=self.device, dtype=torch.long) + self.randomize_buf = torch.zeros( + self.num_envs, device=self.device, dtype=torch.long) + self.extras = {} + + if "cameras" in self.cfg.keys(): + assert "save_recordings" in self.cfg["cameras"].keys() + for cam in self.cfg["cameras"].keys(): + if cam != "save_recordings": + camera_name = cam + num_cameras = len(self.cfg["cameras"].keys()) - 3 + height = self.cfg["cameras"][camera_name]["height"] + width = self.cfg["cameras"][camera_name]["width"] + camera_type = self.cfg["cameras"][camera_name]["type"] + + if camera_type == "rgb": + num_channels = 3 + elif camera_type == "rgbd": + num_channels = 4 + else: + assert False + + self.image_buf = torch.zeros( + (self.num_envs, num_cameras, height, width, num_channels), + device=self.device, dtype=torch.float) + + if self.cfg["cameras"]["convert_to_pointcloud"]: + self.pointcloud_buf = torch.zeros( + (self.num_envs, num_cameras, height * width, 6), + device=self.device, dtype=torch.float) + + if self.cfg["cameras"]["convert_to_voxelgrid"]: + self.voxelgrid_buf = torch.zeros( + (self.num_envs, 101, 101, 101, 4), device=self.device, + dtype=torch.float) + + def create_sim(self): + """Create the Isaac Gym simulation + + Returns: + Isaac Gym simulation object. + """ + + sim = self.gym.create_sim(self.device_id, self.graphics_device_id, + self.physics_engine, self.sim_params) + self._create_ground_plane(sim) + return sim + + def _create_ground_plane(self, sim) -> None: + plane_params = gymapi.PlaneParams() + plane_params.normal = gymapi.Vec3(0.0, 0.0, 1.0) + self.gym.add_ground(sim, plane_params) + + def create_envs(self, num_envs: int, spacing: float, num_per_row: int + ) -> None: + """Creates the environments: + 1. Loads assets from URDFs (robot, table, task-assets), + 2. For each environment + - Creates the environment, + - Adds the loaded assets as actors + 3. Stores handles and indices to be used later + """ + + lower = gymapi.Vec3(-spacing, -spacing, 0.0) + upper = gymapi.Vec3(spacing, spacing, 2 * spacing) + + # Load assets to be added + assets_dict = self._load_assets() + self.envs, self.robots = [], [] + self.robot_indices, self.fingertip_indices = [], [] + + for i in range(num_envs): + # Create env instance + env_ptr = self.gym.create_env(self.sim, lower, upper, num_per_row) + agg_info = self._begin_aggregate(env_ptr, i) + + # Add actors + self._add_robot(env_ptr, i, assets_dict["robot"]["asset"], + assets_dict["robot"]["start_pose"]) + self._add_table(env_ptr, i, assets_dict["table"]["asset"], + assets_dict["table"]["start_pose"]) + self._add_task(env_ptr, i, assets_dict["task"], agg_info) + + self._end_aggregate(env_ptr) + self.envs.append(env_ptr) + + self._process_handles() + self._process_task_handles() + + def _load_assets(self) -> Dict[str, Any]: + assets_dict = collections.defaultdict(dict) + + # Load robot asset + robot_asset, robot_start_pose = self._load_robot_asset() + self._process_robot_props(robot_asset) + assets_dict["robot"]["asset"] = robot_asset + assets_dict["robot"]["start_pose"] = robot_start_pose + + # Load table asset + table_asset, table_start_pose = self._load_table_asset() + + table_start_pose.r = gymapi.Quat(0, 0, 0.707, 0.707) + table_start_pose.p = gymapi.Vec3(0, -0.05, 0) + assets_dict["table"]["asset"] = table_asset + assets_dict["table"]["start_pose"] = table_start_pose + + # Load task-specific assets + assets_dict["task"].update(self._load_task_assets()) + return assets_dict + + @property + def asset_root(self): + rofunc_path = get_rofunc_path() + asset_root = os.path.join(rofunc_path, "simulator/assets") + if "asset" in self.cfg: + return os.path.normpath(os.path.join(os.path.dirname( + os.path.abspath(__file__)), self.cfg["asset"].get( + "assetRoot", asset_root))) + return asset_root + + @property + def robot_asset_file(self) -> os.path: + robot_asset_file = os.path.normpath( + "urdf/sih/ur5e_schunk_sih_right.urdf") + if "asset" in self.cfg: + return os.path.normpath(self.cfg["asset"].get( + "robotAssetFile", robot_asset_file)) + return robot_asset_file + + @property + def table_asset_file(self) -> os.path: + table_asset_file = "urdf/table.urdf" + if "asset" in self.cfg: + return os.path.normpath(self.cfg["asset"].get( + "tableAssetFile", table_asset_file)) + return table_asset_file + + def _load_robot_asset(self): + asset_options = gymapi.AssetOptions() + asset_options.fix_base_link = True + asset_options.collapse_fixed_joints = True + asset_options.disable_gravity = True + asset_options.use_mesh_materials = True + asset_options.thickness = 0.001 + asset_options.angular_damping = 0.01 + asset_options.override_com = True + asset_options.override_inertia = True + + if self.physics_engine == gymapi.SIM_PHYSX: + asset_options.use_physx_armature = True + asset_options.default_dof_drive_mode = gymapi.DOF_MODE_POS + robot_asset = self.gym.load_asset(self.sim, self.asset_root, + self.robot_asset_file, asset_options) + robot_start_pose = gymapi.Transform() + robot_start_pose.p = gymapi.Vec3(0, -0.7, 1.1) + return robot_asset, robot_start_pose + + def _process_robot_props(self, robot_asset): + """Processes some robot specific properties, such as DOF props and + limits and the coupling of the joints in the SIH hand.""" + self.fingertips = ["th_distal", "if_distal", "mf_distal", "rf_distal", + "lf_distal"] + self.num_fingertips = len(self.fingertips) + self.fingertip_handles = [self.gym.find_asset_rigid_body_index( + robot_asset, name) for name in self.fingertips] + + # Get counts + self.num_robot_bodies = self.gym.get_asset_rigid_body_count(robot_asset) + self.num_robot_shapes = self.gym.get_asset_rigid_shape_count(robot_asset) + self.num_robot_dofs = self.gym.get_asset_dof_count(robot_asset) + + self._set_robot_dof_props(robot_asset) + self._set_robot_dof_limits(robot_asset) + self._couple_robot_joints(robot_asset) + + def _set_robot_dof_props(self, robot_asset) -> None: + # Set the DOF properties of robot arm and hand + self.robot_dof_props = self.gym.get_asset_dof_properties(robot_asset) + + for idx in range(self.num_robot_dofs): + if idx < 6: + self.robot_dof_props["stiffness"][idx] = \ + self.cfg["asset"]["robotProps"]["armStiffness"] + self.robot_dof_props["damping"][idx] = \ + self.cfg["asset"]["robotProps"]["armDamping"] + if "armMaxEffort" in self.cfg["asset"]["robotProps"]: + self.robot_dof_props["effort"][idx] = \ + self.cfg["asset"]["robotProps"]["armMaxEffort"] + else: + self.robot_dof_props["stiffness"][idx] = \ + self.cfg["asset"]["robotProps"]["handStiffness"] + self.robot_dof_props["damping"][idx] = \ + self.cfg["asset"]["robotProps"]["handDamping"] + self.robot_dof_props["armature"][idx] = 0.005 + + # Specify actuated DOFs of arm and hand + arm_actuated_dof_names = ['shoulder_pan_joint', 'shoulder_lift_joint', + 'elbow_joint', 'wrist_1_joint', + 'wrist_2_joint', 'wrist_3_joint'] + hand_actuated_dof_names = ['palm_to_if_proximal', 'palm_to_mf_proximal', + 'palm_to_rf_proximal', 'palm_to_th_proximal', + 'th_proximal_to_th_inter'] + self.arm_actuated_dof_indices = [ + self.gym.find_asset_dof_index(robot_asset, name) + for name in arm_actuated_dof_names] + self.hand_actuated_dof_indices = [ + self.gym.find_asset_dof_index(robot_asset, name) + for name in hand_actuated_dof_names] + self.actuated_dof_indices = self.arm_actuated_dof_indices + \ + self.hand_actuated_dof_indices + self.arm_actuated_dof_indices = to_torch( + self.arm_actuated_dof_indices, dtype=torch.long, device=self.device) + self.hand_actuated_dof_indices = to_torch( + self.hand_actuated_dof_indices, dtype=torch.long, + device=self.device) + self.actuated_dof_indices = to_torch( + self.actuated_dof_indices, dtype=torch.long, device=self.device) + + def _set_robot_dof_limits(self, robot_asset) -> None: + # Set DOF limits and default positions and velocities + self.robot_dof_lower_limits = [] + self.robot_dof_upper_limits = [] + self.robot_dof_default_vel = [] + for i in range(self.num_robot_dofs): + self.robot_dof_lower_limits.append(self.robot_dof_props["lower"][i]) + self.robot_dof_upper_limits.append(self.robot_dof_props["upper"][i]) + self.robot_dof_default_vel.append(0.0) + arm_dof_default_pos = [1.3143, -0.61410, 1.7623, -1.1482, 1.3143, + -3.1416] + arm_dof_away_pos = [1.3143 - 0.5 * np.pi, -0.61410, 1.7623, -1.1482, 1.3143, + -3.1416] + hand_dof_default_pos = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] + self.robot_dof_default_pos = arm_dof_default_pos + hand_dof_default_pos + + self.robot_dof_away_pos = arm_dof_away_pos + hand_dof_default_pos + self.robot_dof_lower_limits = to_torch(self.robot_dof_lower_limits, + device=self.device) + self.robot_dof_upper_limits = to_torch(self.robot_dof_upper_limits, + device=self.device) + + # enforce direction the arm in bend + self.robot_dof_lower_limits[2] = 0.025 # elbow must stay up + self.robot_dof_lower_limits[3] = -np.pi + 0.1 * np.pi + self.robot_dof_upper_limits[3] = 0.5 * np.pi - 0.1 * np.pi + self.robot_dof_lower_limits[4] = 0 + 0.01 * np.pi + self.robot_dof_upper_limits[4] = np.pi - 0.01 * np.pi + + self.robot_dof_default_pos = to_torch(self.robot_dof_default_pos, + device=self.device) + self.robot_dof_default_vel = to_torch(self.robot_dof_default_vel, + device=self.device) + self.robot_dof_away_pos = to_torch(self.robot_dof_away_pos, + device=self.device) + + # Set EEF-Pose limits and default + self.hand_default_pose = to_torch( + [0, 0, 1.1, 0, 0, 0], device=self.device) + self.hand_pose_limits = to_torch( + [0.3, 0.3, 0.3, (1 / 2) * np.pi, (1 / 2) * np.pi, (1 / 2) * np.pi], + device=self.device) + + self.hand_pose_lower_limits = self.hand_default_pose - self.hand_pose_limits + self.hand_pose_upper_limits = self.hand_default_pose + self.hand_pose_limits + self.hand_pose_max_deviation = to_torch( + [0.1, 0.1, 0.1, 0.5, 0.5, 0.5], device=self.device) + self.hand_angle_max_deviation = to_torch( + [0.33, 0.33, 0.33, 0.33, 0.33], device=self.device) + + def _couple_robot_joints(self, robot_asset) -> None: + # Couple DOFs of SIH hand + hand_coupled_joint_names = [ + ["palm_to_if_proximal", "if_proximal_to_if_distal"], + ["palm_to_mf_proximal", "mf_proximal_to_mf_distal"], + ["palm_to_rf_proximal", "rf_proximal_to_rf_distal"], + ["palm_to_rf_proximal", "palm_to_lf_proximal"], + ["palm_to_lf_proximal", "lf_proximal_to_lf_distal"], + ["th_proximal_to_th_inter", "th_inter_to_th_distal"]] + hand_coupled_joint_vals = [[0, 1, 0, 0], + [0, 1, 0, 0], + [0, 1, 0, 0], + [0, 1, 0, 0], + [0, 1, 0, 0], + [0, 1, 0, 0]] + self.hand_coupled_joint_indices = [] + for row in hand_coupled_joint_names: + self.hand_coupled_joint_indices.append( + [self.gym.find_asset_dof_index(robot_asset, name) + for name in row]) + self.hand_coupled_joint_indices = to_torch( + self.hand_coupled_joint_indices, dtype=torch.long, + device=self.device) + self.hand_coupled_joint_vals = to_torch(hand_coupled_joint_vals, + device=self.device) + + def _load_table_asset(self): + """Loads the table and robot stand and keeps track of the number of + rigid bodies and shapes they add to the environment.""" + asset_options = gymapi.AssetOptions() + asset_options.fix_base_link = True + asset_options.use_mesh_materials = True + table_asset = self.gym.load_asset(self.sim, self.asset_root, + self.table_asset_file, asset_options) + table_start_pose = gymapi.Transform() + self.num_table_bodies = self.gym.get_asset_rigid_body_count(table_asset) + self.num_table_shapes = self.gym.get_asset_rigid_shape_count( + table_asset) + return table_asset, table_start_pose + + def _load_task_assets(self) -> Dict[str, Any]: + """Loads task specific assets""" + raise NotImplementedError + + def _begin_aggregate(self, env_ptr, env_idx: int) -> None: + max_agg_bodies = self.num_robot_bodies + self.num_table_bodies + max_agg_shapes = self.num_robot_shapes + self.num_table_shapes + if self.aggregate_mode >= 1: + self.gym.begin_aggregate(env_ptr, max_agg_bodies, + max_agg_shapes, True) + + def _end_aggregate(self, env_ptr) -> None: + if self.aggregate_mode > 0: + self.gym.end_aggregate(env_ptr) + + def _add_table(self, env_ptr, i, table_asset, table_start_pose) -> None: + table_actor = self.gym.create_actor(env_ptr, table_asset, + table_start_pose, "table", i, -1, 1) + + def _add_robot(self, env_ptr, i, robot_asset, robot_start_pose) -> None: + robot_actor = self.gym.create_actor(env_ptr, robot_asset, + robot_start_pose, "robot", i, -1, 0) + self.gym.set_actor_dof_properties(env_ptr, robot_actor, + self.robot_dof_props) + robot_idx = self.gym.get_actor_index(env_ptr, robot_actor, + gymapi.DOMAIN_SIM) + + self.robot_indices.append(robot_idx) + self.robots.append(robot_actor) + + def _add_task(self, env_ptr, i, task_asset_dict: Dict[str, Any], agg_info) -> None: + raise NotImplementedError + + def _process_handles(self) -> None: + self.fingertip_handles = to_torch( + self.fingertip_handles, dtype=torch.long, device=self.device) + self.robot_indices = to_torch( + self.robot_indices, dtype=torch.long, device=self.device) + self.fingertip_actor_rb_handle = [ + self.gym.find_actor_rigid_body_handle( + self.envs[0], self.robots[0], name) for name in self.fingertips] + self.fingertip_actor_rb_handle = to_torch( + self.fingertip_actor_rb_handle, dtype=torch.long, + device=self.device) + + def _process_task_handles(self) -> None: + raise NotImplementedError + + def _init_buffers(self) -> None: + """Initialize tensors that contain the state of the simulation.""" + self.env_steps = 0 + + actor_root_state_tensor = self.gym.acquire_actor_root_state_tensor( + self.sim) + dof_state_tensor = self.gym.acquire_dof_state_tensor(self.sim) + rigid_body_tensor = self.gym.acquire_rigid_body_state_tensor(self.sim) + + self.gym.refresh_actor_root_state_tensor(self.sim) + self.gym.refresh_dof_state_tensor(self.sim) + self.gym.refresh_rigid_body_state_tensor(self.sim) + + # create some wrapper tensors for different slices + self.dof_state = gymtorch.wrap_tensor(dof_state_tensor) + self.robot_dof_state = self.dof_state.view( + self.num_envs, -1, 2)[:, :self.num_robot_dofs] + self.robot_dof_pos = self.robot_dof_state[..., 0] + self.robot_dof_vel = self.robot_dof_state[..., 1] + + self.rigid_body_states = gymtorch.wrap_tensor(rigid_body_tensor).view( + self.num_envs, -1, 13) + + self.num_bodies = self.rigid_body_states.shape[1] + + self.root_state_tensor = gymtorch.wrap_tensor( + actor_root_state_tensor).view(-1, 13) + + self.num_dofs = self.gym.get_sim_dof_count(self.sim) // self.num_envs + self.prev_targets = torch.zeros((self.num_envs, self.num_dofs), + dtype=torch.float, device=self.device) + self.cur_targets = torch.zeros((self.num_envs, self.num_dofs), + dtype=torch.float, device=self.device) + + self.global_indices = torch.arange( + self.num_envs * 3, dtype=torch.int32, device=self.device).view( + self.num_envs, -1) + + # Acquire unit tensors + self.x_unit_tensor = to_torch( + [1, 0, 0], dtype=torch.float, device=self.device).repeat( + (self.num_envs, 1)) + self.y_unit_tensor = to_torch( + [0, 1, 0], dtype=torch.float, device=self.device).repeat( + (self.num_envs, 1)) + self.z_unit_tensor = to_torch( + [0, 0, 1], dtype=torch.float, device=self.device).repeat( + (self.num_envs, 1)) + + if self.cfg["sim"]["useContactForces"]: + self._acquire_contact_force_tensor() + + if self.cfg["sim"]["useForceSensors"]: + self._acquire_force_sensor_tensor() + + actor_count = self.gym.get_actor_count(self.envs[0]) + actor_names = [self.gym.get_actor_name(self.envs[0], i) + for i in range(actor_count)] + actor_rigid_body_counts = [self.gym.get_actor_rigid_body_count( + self.envs[0], i) for i in range(actor_count)] + actor_dof_counts = [self.gym.get_actor_dof_count( + self.envs[0], i) for i in range(actor_count)] + + self.gym.refresh_jacobian_tensors(self.sim) + + dof_state = self.dof_state.view( + self.num_envs, sum(actor_dof_counts), 2) + + self.rigid_body_state_dict, self.jacobian_dict, self.dof_dict = \ + {}, {}, {} + dof_count, rb_count = 0, 0 + for i, name in enumerate(actor_names): + # Rigid body states + rigid_body_names = self.gym.get_actor_rigid_body_names( + self.envs[0], i) + self.rigid_body_state_dict[name] = {} + self.dof_dict[name] = {} + + self.dof_dict[name]["position"] = \ + dof_state[:, dof_count:dof_count + actor_dof_counts[i], 0] + self.dof_dict[name]["velocity"] = \ + dof_state[:, dof_count:dof_count + actor_dof_counts[i], 1] + dof_count += actor_dof_counts[i] + + for j, body in enumerate(rigid_body_names): + self.rigid_body_state_dict[name][body] = {} + t = self.rigid_body_states + self.rigid_body_state_dict[name][body]["position"] = \ + self.rigid_body_states[:, j + rb_count, 0:3] + self.rigid_body_state_dict[name][body]["rotation"] = \ + self.rigid_body_states[:, j + rb_count, 3:7] + self.rigid_body_state_dict[name][body]["linear_velocity"] = \ + self.rigid_body_states[:, j + rb_count, 7:10] + self.rigid_body_state_dict[name][body][ + "rotational_velocity"] = self.rigid_body_states[ + :, j + rb_count, 10:13] + rb_count += actor_rigid_body_counts[i] + + # Jacobian matrices + if actor_dof_counts[i] > 0: + jacobian_tensor = self.gym.acquire_jacobian_tensor( + self.sim, name) + jacobian = gymtorch.wrap_tensor(jacobian_tensor) + self.jacobian_dict[name] = jacobian + + self._objects_dropped = False + + self.successes = torch.zeros(self.num_envs, dtype=torch.float, + device=self.device) + self.success_rate = torch.zeros(1, dtype=torch.float, + device=self.device) + + self.actions = torch.zeros(self.num_envs, self.cfg["env"]["numActions"], + device=self.device) + self.prev_actions = self.actions.clone() + + # haptics + self.prev_force_magnitude = torch.zeros(self.num_envs, 5).to(self.device) + self.low_pass_horizon_len = int(self.cfg["haptics"]["buzz"]["low_pass_horizon"] / (self.dt)) + self.past_force_magnitudes = torch.zeros(5, self.low_pass_horizon_len - 1).to(self.device) + self.past_force_directions = torch.zeros(5, 3, self.low_pass_horizon_len - 1).to(self.device) + self.prev_effective_force = torch.zeros(self.num_envs, 5).to(self.device) + self.prev_smoothed_force_increase = torch.zeros(self.num_envs, 5).to(self.device) + self.finger_collision = torch.zeros(self.num_envs, 5).to(self.device) # reports finger collision forces + self.finger_in_contact_for = torch.zeros((self.num_envs, 5), dtype=torch.long).to(self.device) # counts the time-steps for how long a finger has been in contact + self.collision_vibration_time_steps = int( + self.cfg["haptics"]["buzz"]["collision_vibration_time"] / ( + self.dt * self.control_freq_inv)) + + # buffers for prev targets needed for relative control + if self.cfg["control"]["useRelativeControl"]: + self._init_relative_targets() + + def _acquire_contact_force_tensor(self) -> None: + contact_force_tensor = self.gym.acquire_net_contact_force_tensor( + self.sim) + self.gym.refresh_net_contact_force_tensor(self.sim) + self.contact_forces = gymtorch.wrap_tensor(contact_force_tensor).view( + self.num_envs, -1, 3) # (num_envs, num_bodies, xyz) + + def _acquire_force_sensor_tensor(self) -> None: + num_force_sensors = self.num_fingertips + sensor_tensor = self.gym.acquire_force_sensor_tensor(self.sim) + self.gym.refresh_force_sensor_tensor(self.sim) + self.force_sensor_readings = gymtorch.wrap_tensor(sensor_tensor).view( + self.num_envs, num_force_sensors, 6) + self.sensor_forces = self.force_sensor_readings[..., :3] + self.sensor_torques = self.force_sensor_readings[..., 3:] + + def _init_relative_targets(self) -> None: + self.prev_hand_pose_targets = self.tracker_pose + self.prev_hand_finger_targets = self.prev_targets[ + :, self.hand_actuated_dof_indices] + + def _reset_relative_targets(self, env_ids) -> None: + if len(env_ids) > 0: + self.prev_hand_pose_targets[env_ids] = \ + self.initial_tracker_pose[env_ids] + + self.prev_hand_finger_targets[env_ids] = \ + self.initial_finger_angles[env_ids] + + @property + def eef_pos(self) -> torch.Tensor: + return self.rigid_body_state_dict["robot"]["wrist_3_link"]["position"] + + @property + def tracker_pos(self) -> torch.Tensor: + tracker_pos = self.eef_pos + quat_apply( + self.tracker_rot, torch.Tensor([[0, 0.075, 0.05]]).repeat( + self.num_envs, 1).to(self.device)) + return tracker_pos + + @property + def tracker_rot(self) -> torch.Tensor: + tracker_rot = quat_mul(self.eef_rot, torch.Tensor([[0.707, 0., 0., 0.707]]).repeat(self.num_envs, 1).to(self.device)) + return tracker_rot + + @property + def tracker_rot_euler(self) -> torch.Tensor: + return torch.stack(get_euler_xyz(self.tracker_rot)).transpose(0, 1) + + @property + def tracker_pose(self) -> torch.Tensor: + return torch.cat([self.tracker_pos, self.tracker_rot_euler], dim=-1) + + @property + def eef_rot(self) -> torch.Tensor: + return self.rigid_body_state_dict["robot"]["wrist_3_link"]["rotation"] + + @property + def eef_rot_euler(self) -> torch.Tensor: + return torch.stack(get_euler_xyz(self.eef_rot)).transpose(0, 1) + + @property + def eef_pose(self) -> torch.Tensor: + return torch.cat([self.eef_pos, self.eef_rot_euler], dim=-1) + + def _reset_robot(self, env_ids, default_dof_pos: torch.Tensor = None, + apply_dof_resets: bool = True, initial_sampling: bool = False) -> None: + """Resets the robot DOFs to the initial position plus randomized + delta position and delta velocity.""" + if default_dof_pos is None: + default_dof_pos = self.robot_dof_default_pos.unsqueeze(0).repeat(len(env_ids), 1) + + if initial_sampling: + # Generate random values + rand_floats = torch_rand_float( + -1.0, 1.0, (self.num_envs, self.num_robot_dofs), + device=self.device) + + # Reset position and velocity of robot DOFs + delta_max = self.robot_dof_upper_limits - default_dof_pos + delta_min = self.robot_dof_lower_limits - default_dof_pos + rand_delta = delta_min + (delta_max - delta_min) * \ + rand_floats[:, :self.num_robot_dofs] + pos = default_dof_pos + \ + self.cfg["initState"]["noise"]["robotDofPos"] * rand_delta + + self.initial_robot_dof_pos = pos.clone() + self.initial_robot_dof_vel = self.robot_dof_default_vel.unsqueeze(0).repeat(self.num_envs, 1).clone() + + self.robot_dof_pos[env_ids] = self.initial_robot_dof_pos[env_ids] + self.robot_dof_vel[env_ids] = self.initial_robot_dof_vel[env_ids] + + + self.prev_targets[env_ids, :self.num_robot_dofs] = self.initial_robot_dof_pos[env_ids] + self.cur_targets[env_ids, :self.num_robot_dofs] = self.initial_robot_dof_pos[env_ids] + + # Set DOF state and target tensors + robot_indices = self.robot_indices[env_ids].to(torch.int32) + + self.gym.set_dof_position_target_tensor_indexed( + self.sim, gymtorch.unwrap_tensor(self.prev_targets), + gymtorch.unwrap_tensor(robot_indices), len(env_ids)) + + if apply_dof_resets: + self.gym.set_dof_state_tensor_indexed( + self.sim, gymtorch.unwrap_tensor(self.dof_state), + gymtorch.unwrap_tensor(robot_indices), len(env_ids)) + + self.prev_actions[env_ids, :] = 0. + + def pre_physics_step(self, actions): + self.env_steps += self.num_envs + overwrite_actions = False + if overwrite_actions: + if self.progress_buf[0] < 150: + actions = torch.Tensor( + [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]).repeat( + self.num_envs, 1).to(self.device) + else: + actions = torch.Tensor( + [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]).repeat( + self.num_envs, 1).to(self.device) + + actions = actions.to(self.device) + + self._reset_terminated_envs() + + hand_pose_targets, hand_joint_targets = self._process_actions(actions) + + # Compute robot arm joint targets via inverse kinematics + arm_joint_targets = self._ik_hand_pose(hand_pose_targets) + + if self.cfg["control"]["useRelativeControl"]: + self._apply_relative_controls(arm_joint_targets, hand_joint_targets) + else: + self._apply_absolute_controls(arm_joint_targets, hand_joint_targets) + + # Update previous targets + self.prev_targets[:, self.actuated_dof_indices] = \ + self.cur_targets[:, self.actuated_dof_indices] + + # Enforce joint couplings of SIH hand + self.cur_targets = self._couple_joints(self.cur_targets) + + # Set joint targets in the simulation + self.gym.set_dof_position_target_tensor( + self.sim, gymtorch.unwrap_tensor(self.cur_targets)) + + def _reset_terminated_envs(self) -> None: + # Reset any environments that terminated + env_ids = self.reset_buf.nonzero(as_tuple=False).squeeze(-1) + if len(env_ids) > 0: + self.reset_idx(env_ids) + if "cameras" in self.cfg.keys(): + if self.cfg["cameras"]["save_recordings"]: + self.reset_recordings_idx(env_ids) + + # Reset relative control targets if needed + if self.cfg["control"]["useRelativeControl"]: + self._reset_relative_targets(env_ids) + + def _process_actions(self, actions: torch.Tensor + ) -> Tuple[torch.Tensor, torch.Tensor]: + self.actions[:] = actions.clone().to(self.device) + + assert torch.all(actions <= 1.) and torch.all(actions >= -1.), \ + "Expected all actions to be in [-1, 1]." + + assert actions.shape[1] == 11, \ + "Expected actions to be 11 dimensional with 6 dimensions " \ + "controlling the EEF-pose and 5 dimensions controlling the " \ + "SIH-hand joint targets." + + hand_pose_targets = actions[:, 0:6].clone() + + hand_joint_targets = actions[:, 6:11] + if self.cfg["control"]["useRelativeControl"]: + # hand_pose_targets = prev_targets + Δt * β * hand_pose_targets + hand_pose_targets *= self.dt + hand_pose_targets[:, 0:3] *= \ + self.cfg["control"]["scale"]["relativePosSpeed"] + hand_pose_targets[:, 3:6] *= \ + self.cfg["control"]["scale"]["relativeRotSpeed"] + hand_pose_targets += self.prev_hand_pose_targets + + if self.cfg["control"]["clampActionDeviation"]: + # Clamp hand_pose_targets not to deviate to far from the + # current pose + hand_pos_targets = hand_pose_targets[:, 0:3] + hand_rot_targets = hand_pose_targets[:, 3:6] + + # Clamp target_pose around current_pose + hand_pos_targets = tensor_clamp( + hand_pos_targets, + self.tracker_pos - self.hand_pose_max_deviation[0:3], + self.tracker_pos + self.hand_pose_max_deviation[0:3]) + + euler_rot_err = hand_rot_targets - self.tracker_rot_euler + + euler_rot_err = torch.where(euler_rot_err < -np.pi, + euler_rot_err + 2*np.pi, + euler_rot_err) + euler_rot_err = torch.where(euler_rot_err > np.pi, + euler_rot_err - 2*np.pi, + euler_rot_err) + # clamp euler rotation error + euler_rot_err = tensor_clamp(euler_rot_err, + -self.hand_pose_max_deviation[3:6], + self.hand_pose_max_deviation[3:6]) + hand_rot_targets = self.tracker_rot_euler + euler_rot_err + + # put hand_rot_targets in [-pi, pi] + hand_rot_targets = torch.where(hand_rot_targets < -np.pi, + hand_rot_targets + 2 * np.pi, + hand_rot_targets) + hand_rot_targets = torch.where(hand_rot_targets > np.pi, + hand_rot_targets - 2 * np.pi, + hand_rot_targets) + + hand_pose_targets = torch.cat( + [hand_pos_targets, hand_rot_targets], dim=1) + + # Clamp hand pose to remain in allowed workspace + hand_pose_targets = tensor_clamp(hand_pose_targets, + self.hand_pose_lower_limits, + self.hand_pose_upper_limits) + + self.prev_hand_pose_targets = hand_pose_targets + + # hand_joint_targets = prev_targets + Δt * β * hand_joint_targets + hand_joint_targets *= \ + self.cfg["control"]["scale"]["handJointSpeed"] * self.dt + hand_joint_targets += self.prev_targets[ + :, self.hand_actuated_dof_indices] + self.prev_hand_finger_targets = hand_joint_targets + + if self.cfg["control"]["clampActionDeviation"]: + # Clamp hand_joint_targets not to deviate to far from current pos + hand_joint_pos = self.robot_dof_pos[:, + self.hand_actuated_dof_indices] + hand_joint_targets = tensor_clamp( + hand_joint_targets, + hand_joint_pos - self.hand_angle_max_deviation, + hand_joint_pos + self.hand_angle_max_deviation) + else: + assert False + hand_pose_targets = scale(hand_pose_targets, + self.hand_pose_lower_limits, + self.hand_pose_upper_limits) + + if self.cfg["control"]["clampActionDeviation"]: + hand_pos_targets = hand_pose_targets[:, 0:3] + hand_rot_targets = hand_pose_targets[:, 3:6] + + # Clamp position deviation + hand_pos_targets = tensor_clamp( + hand_pos_targets, + self.hand_pos - self.hand_pose_max_deviation[0:3], + self.hand_pos + self.hand_pose_max_deviation[0:3]) + + hand_pose_targets = torch.cat([hand_pos_targets, hand_rot_targets], dim=1) + + import time + #time.sleep(1000) + # Clamp hand_pose_targets not to deviate to far from current pose + #hand_pose_targets = tensor_clamp( + # hand_pose_targets, + # self.eef_pose - self.hand_pose_max_deviation, + # self.eef_pose + self.hand_pose_max_deviation) + + hand_joint_targets = scale( + hand_joint_targets, + self.robot_dof_lower_limits[self.hand_actuated_dof_indices], + self.robot_dof_upper_limits[self.hand_actuated_dof_indices]) + + if self.cfg["control"]["clampActionDeviation"]: + # Clamp hand_joint_targets not to deviate to far from current pos + hand_joint_pos = self.robot_dof_pos[:, self.hand_actuated_dof_indices] + hand_joint_targets = tensor_clamp( + hand_joint_targets, + hand_joint_pos - self.hand_angle_max_deviation, + hand_joint_pos + self.hand_angle_max_deviation) + return hand_pose_targets, hand_joint_targets + + def _ik_hand_pose(self, hand_pose_targets: torch.Tensor): + hand_target_pos = hand_pose_targets[:, 0:3] + hand_target_rot_euler = hand_pose_targets[:, 3:6] + hand_target_rot = quat_from_euler_xyz( + hand_target_rot_euler[:, 0], hand_target_rot_euler[:, 1], + hand_target_rot_euler[:, 2]) + + pos_err = 0.5 * (hand_target_pos - self.tracker_pos) + rot_err = self.quat_rot_error(hand_target_rot, self.tracker_rot) + + delta_pose = torch.cat([pos_err, rot_err], dim=1).unsqueeze(2) + self.gym.refresh_jacobian_tensors(self.sim) + j_eef = self.jacobian_dict["robot"][:, 5] + + j_eef_T = torch.transpose(j_eef, 1, 2) + ls_dmp = 0.05 # least squares damping + lmbda = torch.eye(6, device=self.device) * (ls_dmp ** 2) + eef_joint_targets = (j_eef_T @ torch.inverse( + j_eef @ j_eef_T + lmbda) @ delta_pose).view( + self.num_envs, self.num_robot_dofs) + + eef_joint_pos = self.dof_dict["robot"]["position"] + eef_joint_targets += eef_joint_pos + return eef_joint_targets[:, 0:6] + + @staticmethod + def quat_rot_error(target, current): + cc = quat_conjugate(current) + q_r = quat_mul(target, cc) + return q_r[:, 0:3] * torch.sign(q_r[:, 3]).unsqueeze(-1) + + def _apply_relative_controls(self, arm_joint_targets: torch.Tensor, + hand_joint_targets: torch.Tensor) -> None: + # Make sure joint targets obey the joint limits + joint_targets = torch.cat([arm_joint_targets, hand_joint_targets], + dim=1) + self.cur_targets[:, self.actuated_dof_indices] = \ + tensor_clamp(joint_targets, + self.robot_dof_lower_limits[self.actuated_dof_indices], + self.robot_dof_upper_limits[self.actuated_dof_indices]) + + def _apply_absolute_controls(self, arm_joint_targets: torch.Tensor, + hand_joint_targets: torch.Tensor) -> None: + # Set current targets to joint targets inferred from actions + joint_targets = torch.cat([arm_joint_targets, hand_joint_targets], + dim=1) + self.cur_targets[:, self.actuated_dof_indices] = joint_targets + + # Apply moving average on joint targets + self.cur_targets[:, self.actuated_dof_indices] = \ + self.cfg["control"]["actionMovingAverage"] * \ + self.cur_targets[:, self.actuated_dof_indices] + \ + (1.0 - self.cfg["control"]["actionMovingAverage"]) * \ + self.prev_targets[:, self.actuated_dof_indices] + + # Make sure joint targets obey the joint limits + self.cur_targets[:, self.actuated_dof_indices] = \ + tensor_clamp(self.cur_targets[:, self.actuated_dof_indices], + self.robot_dof_lower_limits[self.actuated_dof_indices], + self.robot_dof_upper_limits[self.actuated_dof_indices]) + + def _couple_joints(self, targets: torch.Tensor) -> torch.Tensor: + parent_idxs = self.hand_coupled_joint_indices[:, 0] + child_idxs = self.hand_coupled_joint_indices[:, 1] + x = targets[:, parent_idxs] + c0, c1, c2, c3 = self.hand_coupled_joint_vals[:, 0], \ + self.hand_coupled_joint_vals[:, 1], \ + self.hand_coupled_joint_vals[:, 2], \ + self.hand_coupled_joint_vals[:, 3] + targets[:, child_idxs] = c0 + c1 * x + c2 * x.pow(2) + c3 * x.pow(3) + return targets + + def post_physics_step(self): + self.progress_buf += 1 + self.randomize_buf += 1 + + self.compute_observations() + self.compute_reward() + + if "cameras" in self.cfg.keys(): + self._render_camera_sensors() + if self.convert_to_pointcloud: + self._convert_rgbd_to_pointcloud() + if self.convert_to_voxelgrid: + self._convert_pointcloud_to_voxelgrid() + if self.cfg["cameras"]["save_recordings"]: + self._write_recordings() + + if self.cfg["debug"]["visualization"]: + self.gym.clear_lines(self.viewer) and (self.viewer or self.cfg["debug"].get("showInCamera", False)) + self.gym.refresh_rigid_body_state_tensor(self.sim) + + for i in range(self.num_envs): + self._draw_debug_visualization(i) + + def compute_observations(self): + raise NotImplementedError + + def compute_reward(self): + raise NotImplementedError + + def _refresh_state_tensors(self) -> None: + self.gym.refresh_dof_state_tensor(self.sim) + self.gym.refresh_actor_root_state_tensor(self.sim) + self.gym.refresh_rigid_body_state_tensor(self.sim) + + if self.cfg["sim"]["useContactForces"]: + self.gym.refresh_net_contact_force_tensor(self.sim) + + if self.cfg["sim"]["useForceSensors"]: + self.gym.refresh_force_sensor_tensor(self.sim) + + def _process_state_tensors(self) -> None: + self.fingertip_state = self.rigid_body_states[ + :, self.fingertip_handles][:, :, 0:13] + self.fingertip_rigid_body_pos = self.rigid_body_states[ + :, self.fingertip_handles][:, :, 0:3] + self.fingertip_rot = self.rigid_body_states[ + :, self.fingertip_handles][:, :, 3:7] + self.fingertip_pos = self.calculate_fingertip_pos() + + def calculate_fingertip_pos(self) -> torch.Tensor: + """Converts the default positions of the rigid bodies of the fingertips + to the actual positions to be used in the observation.""" + fingertip_pos = self.fingertip_rigid_body_pos.clone() + fingertip_pos += quat_apply( + self.fingertip_rot, torch.Tensor([[[-0.0200, 0.0000, 0.0150], + [-0.0870, 0.0175, 0.0400], + [-0.0375, 0.0175, 0.0490], + [ 0.0140, 0.0175, 0.0410], + [ 0.0640, 0.0170, 0.0340]]] + ).repeat(self.num_envs, 1, 1 + ).to(self.device)) + return fingertip_pos + + def add_separate_obs(self, key, data): + self.obs_separate[key] = data + + def add_fingertip_contact_forces(self, start_idx: int) -> None: + self.obs_buf[:, start_idx:start_idx + 15] = \ + self.contact_forces[:, self.fingertip_actor_rb_handle].reshape( + self.num_envs, 3 * self.num_fingertips) + if self.cfg["task"]["returnObsDict"]: + self.add_separate_obs( + "fingertipContactForces", + self.contact_forces[:, self.fingertip_actor_rb_handle].reshape(self.num_envs, 3 * self.num_fingertips)) + + def add_fingertip_pos(self, start_idx: int) -> None: + self.obs_buf[:, start_idx:start_idx+15] = self.fingertip_pos.reshape( + self.num_envs, 3 * self.num_fingertips) + if self.cfg["task"]["returnObsDict"]: + self.add_separate_obs( + "fingertipPos", + self.fingertip_pos.reshape(self.num_envs, 3 * self.num_fingertips)) + + def add_hand_pose(self, start_idx: int) -> None: + self.obs_buf[:, start_idx:start_idx+3] = self.eef_pos + self.obs_buf[:, start_idx+3:start_idx+7] = self.eef_rot + if self.cfg["task"]["returnObsDict"]: + self.add_separate_obs("handPose", torch.cat([self.eef_pos, self.eef_rot], dim=1)) + + def add_joint_pos(self, start_idx: int, + use_unscale: bool = True) -> None: + if use_unscale: + data = \ + unscale(self.robot_dof_pos, self.robot_dof_lower_limits, + self.robot_dof_upper_limits) + else: + data = \ + self.robot_dof_pos + self.obs_buf[:, start_idx:start_idx + self.num_robot_dofs] = data + if self.cfg["task"]["returnObsDict"]: + self.add_separate_obs("jointPos", data) + + def add_joint_vel(self, start_idx: int) -> None: + self.obs_buf[:, start_idx:start_idx + self.num_robot_dofs] = \ + self.robot_dof_vel + if self.cfg["task"]["returnObsDict"]: + self.add_separate_obs("jointVel", self.robot_dof_vel) + + def add_hand_dof_pos(self, start_idx: int, + use_unscale: bool = True) -> None: + # Only adds the 5 degrees of freedom of the SIH hand to the observation + if use_unscale: + data = \ + unscale(self.robot_dof_pos[:, self.hand_actuated_dof_indices], + self.robot_dof_lower_limits[self.hand_actuated_dof_indices], + self.robot_dof_upper_limits[self.hand_actuated_dof_indices]) + else: + data = \ + self.robot_dof_pos[:, self.hand_actuated_dof_indices] + self.obs_buf[:, start_idx:start_idx + 5] = data + if self.cfg["task"]["returnObsDict"]: + self.add_separate_obs("handDofPos", data) + + def add_previous_action(self, start_idx: int) -> None: + self.obs_buf[:, start_idx:start_idx+self.cfg["env"]["numActions"]] = \ + self.actions + if self.cfg["task"]["returnObsDict"]: + self.add_separate_obs("previousAction", self.actions) + + def get_state(self): + return torch.clamp(self.states_buf, -self.clip_obs, self.clip_obs).to( + self.rl_device) + + def step( + self, + actions: torch.Tensor + ) -> Tuple[ + Dict[str, torch.Tensor], + torch.Tensor, + torch.Tensor, + Dict[str, Any]]: + """Steps the environment. + + Args: + actions: Actions to apply to the environments + Returns: + obs, rew, done, info + """ + + # randomize actions + if self.dr_randomizations.get('actions', None): + actions = self.dr_randomizations['actions']['noise_lambda'](actions) + + action_tensor = torch.clamp(actions, -self.clip_actions, self.clip_actions) + # apply actions + self.pre_physics_step(action_tensor) + + # step physics and render each frame + for i in range(self.control_freq_inv): + self.render() + self.step_simulation(self.gym, self.sim) + + # to fix! + if self.device == 'cpu': + self.gym.fetch_results(self.sim, True) + + # fill time out buffer + self.timeout_buf = torch.where \ + (self.progress_buf >= self.max_episode_length - 1, torch.ones_like(self.timeout_buf), torch.zeros_like(self.timeout_buf)) + + # compute observations, rewards, resets, ... + self.post_physics_step() + + # randomize observations + if self.dr_randomizations.get('observations', None): + self.obs_buf = self.dr_randomizations['observations']['noise_lambda'](self.obs_buf) + + self.extras["time_outs"] = self.timeout_buf.to(self.rl_device) + + self.obs_dict["obs"] = torch.clamp(self.obs_buf, -self.clip_obs, self.clip_obs).to(self.rl_device) + if "cameras" in self.cfg.keys(): + self.obs_dict["image"] = self.image_buf.to(self.rl_device) + + if self.cfg["task"]["returnObsDict"]: + self.obs_dict["obs_separate"] = self.obs_separate + + if self.cfg["reward"]["returnRewardsDict"]: + self.obs_dict["rewards"] = self.rewards_dict + + # asymmetric actor-critic + if self.num_states > 0: + self.obs_dict["states"] = self.get_state() + + return self.obs_dict, self.rew_buf.to \ + (self.rl_device), self.reset_buf.to(self.rl_device), self.extras + + def zero_actions(self) -> torch.Tensor: + actions = torch.zeros([self.num_envs, self.num_actions], + dtype=torch.float32, device=self.rl_device) + return actions + + def reset(self) -> Dict[str, torch.Tensor]: + """Reset all environments. + + Returns: + obs + """ + zero_actions = self.zero_actions() + + self._refresh_state_tensors() + self._process_state_tensors() + + # step the simulator + self.step(zero_actions) + + self.obs_dict["obs"] = torch.clamp(self.obs_buf, -self.clip_obs, + self.clip_obs).to(self.rl_device) + if "cameras" in self.cfg.keys(): + self.obs_dict["image"] = self.image_buf.to(self.rl_device) + + # asymmetric actor-critic + if self.num_states > 0: + self.obs_dict["states"] = self.get_state() + + self.initial_reset = False + return self.obs_dict + + def render(self): + """Draw the frame to the viewer, and check for keyboard events.""" + if self.viewer: + # check for window closed + if self.gym.query_viewer_has_closed(self.viewer): + sys.exit() + + # check for keyboard events + for evt in self.gym.query_viewer_action_events(self.viewer): + if evt.action == "QUIT" and evt.value > 0: + sys.exit() + elif evt.action == "toggle_viewer_sync" and evt.value > 0: + self.enable_viewer_sync = not self.enable_viewer_sync + + # fetch results + if self.device != 'cpu': + self.gym.fetch_results(self.sim, True) + + # step graphics + if self.enable_viewer_sync: + self.gym.step_graphics(self.sim) + self.gym.draw_viewer(self.viewer, self.sim, True) + + # Wait for dt to elapse in real time. + # This synchronizes the physics simulation with the rendering rate. + self.gym.sync_frame_time(self.sim) + + else: + self.gym.poll_viewer_events(self.viewer) + + def _draw_debug_visualization(self, i: int) -> None: + if self.cfg["debug"]["drawEefPose"]: + self._draw_eef_pose(i) + if self.cfg["debug"]["drawTrackerPose"]: + self._draw_tracker_pose(i) + if self.cfg["debug"]["drawFingertipPose"]: + self._draw_fingertip_pose(i) + if self.cfg["debug"]["drawFingertipContactForces"]: + self._draw_fingertip_contact_forces(i) + if self.cfg["debug"]["colorFingertipContactForce"]: + self._color_fingertip_contact_force(i) + + def _draw_coordinate_system(self, env, pos, rot, axis_length: float = 0.15 + ) -> None: + targetx = (pos + quat_apply(rot, to_torch( + [1, 0, 0], device=self.device) * axis_length)).cpu().numpy() + targety = (pos + quat_apply(rot, to_torch( + [0, 1, 0], device=self.device) * axis_length)).cpu().numpy() + targetz = (pos + quat_apply(rot, to_torch( + [0, 0, 1], device=self.device) * axis_length)).cpu().numpy() + p0 = pos.cpu().numpy() + self.gym.add_lines(self.viewer, env, 1, + [p0[0], p0[1], p0[2], targetx[0], targetx[1], + targetx[2]], [0.85, 0.1, 0.1]) + self.gym.add_lines(self.viewer, env, 1, + [p0[0], p0[1], p0[2], targety[0], targety[1], + targety[2]], [0.1, 0.85, 0.1]) + self.gym.add_lines(self.viewer, env, 1, + [p0[0], p0[1], p0[2], targetz[0], targetz[1], + targetz[2]], [0.1, 0.1, 0.85]) + + def _draw_arrow(self, env, base, head, + color: List[float] = [0.9, 0., 0.], + num_arrowhead_lines: int = 16) -> None: + base = base.cpu().numpy() + head = head.cpu().numpy() + self.gym.add_lines(self.viewer, env, 1, + [base[0], base[1], base[2], + head[0], head[1], head[2]], color) + arrow = np.array(head - base) + + ah_base = base + 0.8 * arrow + + arrow_dir = arrow / np.linalg.norm(arrow) + + v = np.array([1., 0, 0]) + if np.linalg.norm(v - arrow_dir) < 0.1: + v = np.array([0., 1., 0.]) + + n1 = np.cross(v, arrow_dir) + n2 = np.cross(n1, arrow_dir) + n1 = n1 / np.linalg.norm(n1) + n2 = n2 / np.linalg.norm(n2) + + ah_width = 0.01 + prev_ah_target = None + for i in range(num_arrowhead_lines): + + rot_angle = (2 * np.pi) * (i / num_arrowhead_lines) + ah_target = ah_base + ah_width * (np.sin(rot_angle) * n1 + np.cos(rot_angle) * n2) + if i == 0: + first_ah_target = ah_target + self.gym.add_lines(self.viewer, env, 1, + [ah_target[0], ah_target[1], ah_target[2], + head[0], head[1], head[2]], color) + if prev_ah_target is not None: + self.gym.add_lines(self.viewer, env, 1, + [ah_target[0], ah_target[1], ah_target[2], + prev_ah_target[0], prev_ah_target[1], + prev_ah_target[2]], color) + prev_ah_target = ah_target + + if i == num_arrowhead_lines - 1: + self.gym.add_lines(self.viewer, env, 1, + [ah_target[0], ah_target[1], ah_target[2], + first_ah_target[0], first_ah_target[1], + first_ah_target[2]], color) + + def _draw_eef_pose(self, i: int) -> None: + self._draw_coordinate_system(self.envs[i], self.eef_pos[i], + self.eef_rot[i]) + + def _draw_hand_pose(self, i: int) -> None: + self._draw_coordinate_system(self.envs[i], self.hand_pos[i], + self.eef_rot[i]) + + def _draw_tracker_pose(self, i: int) -> None: + self._draw_coordinate_system(self.envs[i], self.tracker_pos[i], + self.tracker_rot[i]) + + def _draw_fingertip_pose(self, i: int) -> None: + for ft_idx in range(self.num_fingertips): + self._draw_coordinate_system(self.envs[i], + self.fingertip_pos[i, ft_idx], + self.fingertip_rot[i, ft_idx], + axis_length=0.05) + + def _draw_fingertip_contact_forces(self, i: int, + force_scaling: float = 0.1, + sqrt_scaling: bool = True) -> None: + ft_pos = self.fingertip_pos[i].cpu().numpy() + ft_contact_force = self.contact_forces[ + i, self.fingertip_actor_rb_handle].cpu().numpy() + + if sqrt_scaling: + for ft_idx in range(self.num_fingertips): + force_magnitude = np.linalg.norm(ft_contact_force[ft_idx]) + ft_contact_force[ft_idx] *= (np.sqrt(force_magnitude) / (force_magnitude + 1e-8)) + + target_pos = ft_pos + force_scaling * ft_contact_force + for ft_idx in range(self.num_fingertips): + self.gym.add_lines(self.viewer, self.envs[i], 1, + [ft_pos[ft_idx, 0], ft_pos[ft_idx, 1], ft_pos[ft_idx, 2], + target_pos[ft_idx, 0], target_pos[ft_idx, 1], target_pos[ft_idx, 2]], + [0.55, 0., 0.]) + + def _color_fingertip_contact_force(self, i: int, + start_color: np.ndarray = np.array([0., 0.8, 0.]), + end_color: np.ndarray = np.array([0.8, 0., 0.]), + max_force: float = 5., + sqrt_scaling: bool = True) -> None: + ft_contact_force = self.contact_forces[ + i, self.fingertip_actor_rb_handle].cpu().numpy() + for ft_idx in range(self.num_fingertips): + force_magnitude = np.linalg.norm(ft_contact_force[ft_idx]) + if sqrt_scaling: + force_magnitude = np.sqrt(force_magnitude) + force_ratio = min(force_magnitude / max_force, 1) + color = start_color + force_ratio * (end_color - start_color) + self.gym.set_rigid_body_color( + self.envs[i], self.robots[i], + self.fingertip_handles[ft_idx], gymapi.MeshType.MESH_VISUAL, + gymapi.Vec3(*tuple(color))) diff --git a/rofunc/learning/RofuncRL/tasks/isaacgymenv/grasp/base/vr_task.py b/rofunc/learning/RofuncRL/tasks/isaacgymenv/grasp/base/vr_task.py new file mode 100644 index 00000000..c362b563 --- /dev/null +++ b/rofunc/learning/RofuncRL/tasks/isaacgymenv/grasp/base/vr_task.py @@ -0,0 +1,170 @@ +import functools +from isaacgym.torch_utils import * +import numpy as np +import time +import torch +from typing import * +from senseglove_teleoperation import SenseGlove +from vrviewer import VRViewer + + +def step_simulation(gym, sim, vr_viewer, senseglove_operator, compute_haptics, headless, wait_time) -> None: + gym.simulate(sim) + if headless: + gym.step_graphics(sim) + vr_viewer.step() + buzz, force_feedback = compute_haptics() + senseglove_operator.haptic_feedback(buzz, force_feedback) + wait_time() + + +class FPSLimit: + def __init__(self, fps: int, control_freq_inv: int, + tau: float = 0.2) -> None: + self.target_dt = control_freq_inv / fps + self.control_freq_inv = control_freq_inv + self.exp_avg_runtime = control_freq_inv / fps + self.tau = tau + + def __enter__(self) -> None: + self.loop_start = time.time() + + def __exit__(self, exc_type: Any, exc_val: Any, exc_tb: Any) -> None: + self.loop_end = time.time() + runtime = max(self.loop_end - self.loop_start - + (self.prev_wait_time * self.control_freq_inv), 0) + + self.exp_avg_runtime = (1 - self.tau) * self.exp_avg_runtime + \ + self.tau * runtime + + def wait_time(self) -> None: + self.prev_wait_time = max( + (self.target_dt - self.exp_avg_runtime) / self.control_freq_inv, 0) + time.sleep(self.prev_wait_time) + + +class VRTask: + def __init__(self, wrapped_task, vr_viewer_cfg: Dict[str, Any], + senseglove_cfg: Dict[str, Any], headless, + control_freq_inv: int, target_fps: int) -> None: + self._wrapped_task = wrapped_task + self._senseglove_operator = SenseGlove(senseglove_cfg) + self._vr_viewer = VRViewer(wrapped_task.gym, wrapped_task.envs[0], + wrapped_task.sim, vr_viewer_cfg) + + self.fps_limit = FPSLimit(target_fps, control_freq_inv) + + self._wrapped_task.step_simulation = functools.partial( + step_simulation, vr_viewer=self._vr_viewer, + senseglove_operator=self._senseglove_operator, + compute_haptics=self.compute_haptics, headless=headless, wait_time=self.fps_limit.wait_time) + + @property + def wrapped_task(self): + return self._wrapped_task + + @property + def vr_viewer(self): + return self._vr_viewer + + @property + def operator(self): + return self._senseglove_operator + + def __getattr__(self, item): + return getattr(self._wrapped_task, item) + + def compute_haptics(self): + # Compute vibration. Vibration depends on the absolute fingertip contact + # force independent of direction. As such it is also activated when + # impact is made with an object with the outside of the fingertip + # for example. + if not self.cfg["haptics"]["enable"]: + return np.zeros(5), np.zeros(5) + + + self.gym.refresh_net_contact_force_tensor(self.sim) + + ft_contact_force = self.contact_forces[:, self.fingertip_actor_rb_handle] + force_magnitude = torch.norm(ft_contact_force[0], dim=1) + force_direction = torch.zeros_like(ft_contact_force[0]) + for f in range(5): + force_direction[f] = ft_contact_force[0, f] / (force_magnitude[f] + 1e-5) + if self.cfg["haptics"]["buzz"]["proportional_to"] == "force": + buzz = 100 * torch.clamp( + force_magnitude / self.cfg["haptics"]["buzz"]["full_at"], + 0., 1.) + elif self.cfg["haptics"]["buzz"]["proportional_to"] == "force_increase": + force_increase = torch.abs( + force_magnitude - self.prev_force_magnitude) + smoothed_force_increase = torch.where( + force_increase > 0.5 * self.cfg["haptics"]["buzz"]["full_at"], + force_increase, + 0.2 * force_increase + 0.8 * self.prev_smoothed_force_increase) + + self.prev_smoothed_force_increase = smoothed_force_increase + buzz = 100 * torch.clamp( + smoothed_force_increase[0] / self.cfg["haptics"]["buzz"]["full_at"], 0., 1.) + self.prev_force_magnitude = force_magnitude + elif self.cfg["haptics"]["buzz"]["proportional_to"] == "collision": + self.finger_collision = torch.where( + self.finger_in_contact_for < + self.collision_vibration_time_steps, + force_magnitude, torch.zeros_like(force_magnitude)) + self.finger_in_contact_for = torch.where( + force_magnitude < 0.1, 0, self.finger_in_contact_for + 1) + buzz = 100 * torch.clamp(self.finger_collision[0] / self.cfg["haptics"]["buzz"]["full_at"], 0., 1.) + elif self.cfg["haptics"]["buzz"]["proportional_to"] == "high_freq": + low_pass_force_magnitude = (1 / self.low_pass_horizon_len) * force_magnitude + for prev in range(self.low_pass_horizon_len - 1): + low_pass_force_magnitude += (1 / self.low_pass_horizon_len) * self.past_force_magnitudes[:, prev] + self.past_force_magnitudes = torch.roll(self.past_force_magnitudes, 1, 1) + self.past_force_magnitudes[:, 0] = force_magnitude + + self.past_force_magnitudes = torch.where(force_magnitude.unsqueeze(-1).repeat(1, self.low_pass_horizon_len - 1) < 0.1, + torch.zeros_like(self.past_force_magnitudes), + self.past_force_magnitudes) + + low_pass_force_direction = (1 / self.low_pass_horizon_len) * force_direction + for prev in range(self.low_pass_horizon_len - 1): + low_pass_force_direction += (1 / self.low_pass_horizon_len) * self.past_force_directions[:, :, prev] + self.past_force_directions = torch.roll(self.past_force_directions, 1, 2) + self.past_force_directions[:, :, 0] = force_direction + + high_freq_force_magnitude = (force_magnitude / (low_pass_force_magnitude + 1)) - 1 + + high_freq_angle_change = torch.arccos((force_direction * low_pass_force_direction).sum(1)) + high_freq_angle_change = torch.where(force_magnitude > 0.1, high_freq_angle_change, torch.zeros_like(high_freq_angle_change)) + + buzz = high_freq_force_magnitude + buzz = 100 * torch.clamp(buzz / self.cfg["haptics"]["buzz"]["full_at"], 0., 1.) + + else: + assert False + self.extras["buzz"] = buzz.cpu().numpy() + + # Compute force-feedback. The force-feedback depends on the direction of + # the forces, as it stops the fingers of the SenseGlove from closing. + # Hence, the contact forces are projected into the rigid body coordinate + # system of the fingertips and only the components acting 'through' the + # fingertips are relevant. + ft_contact_force_in_rb_coordinates = quat_apply( + quat_conjugate(self.fingertip_rot), ft_contact_force) + eff_dir = to_torch([2, 1, 1, 1, 1], device=self.device, + dtype=torch.long).unsqueeze(-1) + effective_force = ft_contact_force_in_rb_coordinates[0].gather( + 1, eff_dir).squeeze() + effective_force = -effective_force + if self.cfg["haptics"]['force_feedback']['proportional_to'] == 'force': + force_feedback = 100 * torch.clamp( + effective_force / self.cfg["haptics"]["force_feedback"][ + "full_at"], 0., 1.) + else: + assert False + + if self.cfg["haptics"]["force_feedback"]["binary"]: + force_feedback = torch.where(force_feedback > 0.99, force_feedback, + torch.zeros_like(force_feedback)) + self.extras["force_feedback"] = force_feedback.cpu().numpy() + + return buzz.cpu().numpy(), force_feedback.cpu().numpy() diff --git a/rofunc/learning/RofuncRL/tasks/isaacgymenv/grasp/empty_bin.py b/rofunc/learning/RofuncRL/tasks/isaacgymenv/grasp/empty_bin.py new file mode 100644 index 00000000..abd7ce25 --- /dev/null +++ b/rofunc/learning/RofuncRL/tasks/isaacgymenv/grasp/empty_bin.py @@ -0,0 +1,961 @@ +import os +from torch import Tensor +from isaacgym import gymtorch, gymapi, gymutil +from isaacgym.torch_utils import * +from typing import * +import random +from gym_grasp.tasks.base.grasping_task import GraspingTask +from .lift_object import LiftObject +import math + + +class EmptyBin(LiftObject): + def __init__(self, + cfg: Dict[str, Any], + sim_device: str, + graphics_device_id: int, + headless: bool) -> None: + """Creates UR5e-SIH robot on the task of emptying a bin filled with + various objects. The agent is rewarded for every object removed from the + bin and objects removed successfully are stored in a done bin, until all + objects are cleared. This is similar to the way the lifting policy is + trained in the tasks in QT-Opt on the scenario where objects are not put + back. + + Arguments: + cfg: (Dict) Configuration dictionary that contains parameters for + how the simulation should run and defines properties of the + task. + sim_device: (str) Device on which the physics simulation is run + (e.g. "cuda:0" or "cpu"). + graphics_device_id: (int) ID of the device to run the rendering. + headless: (bool) Whether to run the simulation without a viewer. + """ + super().__init__(cfg, sim_device, graphics_device_id, headless) + + def _parse_observation_type(self, observation_type, + num_observations) -> Tuple[List, List]: + # Adjust dimensionality to the number of objects per bin + for obs in num_observations.keys(): + if obs.startswith("object"): + num_observations[obs] *= self.cfg["task"]["numObjectsPerBin"] + return super()._parse_observation_type(observation_type, + num_observations) + + def _init_buffers(self) -> None: + super()._init_buffers() + self.remaining_objects = torch.ones( + self.num_envs, self.cfg["task"]["numObjectsPerBin"], + device=self.device, dtype=torch.long) + self.num_remaining_objects = self.cfg["task"]["numObjectsPerBin"] * \ + torch.ones(self.num_envs, + device=self.device, + dtype=torch.long) + + self.prev_remaining_objects = self.remaining_objects.clone() + + def _load_task_assets(self) -> Dict[str, Any]: + assets_dict = {} + assets_dict["object"] = super()._load_task_assets() + self.object_indices = torch.zeros( + [self.num_envs, self.cfg["task"]["numObjectsPerBin"]], + dtype=torch.long, device=self.device) + self.object_cleared_state = [] + self.objects = [[] for _ in range(self.num_envs)] + self.object_ids = [] + + active_bin_asset, done_bin_asset, active_bin_start_pose, \ + done_bin_start_pose = self._load_bin_assets() + assets_dict["active_bin"] = {} + assets_dict["done_bin"] = {} + assets_dict["active_bin"]["asset"] = active_bin_asset + assets_dict["active_bin"]["start_pose"] = active_bin_start_pose + assets_dict["done_bin"]["asset"] = done_bin_asset + assets_dict["done_bin"]["start_pose"] = done_bin_start_pose + return assets_dict + + def _add_task(self, env_ptr, i, task_asset_dict: Dict[str, Any], agg_info) -> None: + self._add_objects(env_ptr, i, task_asset_dict["object"]["asset"], + task_asset_dict["object"]["start_pose"], agg_info) + self._add_bins(env_ptr, i, task_asset_dict["active_bin"]["asset"], + task_asset_dict["done_bin"]["asset"], + task_asset_dict["active_bin"]["start_pose"], + task_asset_dict["done_bin"]["start_pose"]) + + def _process_task_handles(self) -> None: + self.object_init_state = to_torch( + self.object_init_state, device=self.device, dtype=torch.float + ).view(self.num_envs, self.cfg["task"]["numObjectsPerBin"], 13) + self.object_cleared_state = to_torch( + self.object_cleared_state, device=self.device, dtype=torch.float + ).view(self.num_envs, self.cfg["task"]["numObjectsPerBin"], 13) + + def _load_bin_assets(self): + asset_options = gymapi.AssetOptions() + asset_options.fix_base_link = True + asset_options.use_mesh_materials = True + # Enable convex decomposition + asset_options.vhacd_enabled = True + asset_options.vhacd_params = gymapi.VhacdParams() + asset_options.vhacd_params.resolution = 1000000 + + active_bin_asset = self.gym.load_asset( + self.sim, self.asset_root, self.active_bin_asset_file, + asset_options) + + done_bin_asset = self.gym.load_asset( + self.sim, self.asset_root, self.done_bin_asset_file, + asset_options) + + active_x, active_y, active_z = self.cfg["asset"]["activeBinPosition"] + done_x, done_y, done_z = self.cfg["asset"]["doneBinPosition"] + active_bin_start_pose = gymapi.Transform(p=gymapi.Vec3( + active_x, active_y, active_z)) + done_bin_start_pose = gymapi.Transform(p=gymapi.Vec3( + done_x, done_y, done_z), r=gymapi.Quat(0, 0, 0, 1)) + + self.num_bin_bodies = self.gym.get_asset_rigid_body_count( + active_bin_asset) + self.gym.get_asset_rigid_body_count( + done_bin_asset) + self.num_bin_shapes = self.gym.get_asset_rigid_shape_count( + active_bin_asset) + self.gym.get_asset_rigid_shape_count( + done_bin_asset) + return active_bin_asset, done_bin_asset, active_bin_start_pose, \ + done_bin_start_pose + + @property + def active_bin_asset_file(self) -> os.path: + bin_asset_file = "urdf/bin.urdf" + if "asset" in self.cfg: + return os.path.normpath(self.cfg["asset"].get( + "activeBinAssetFile", bin_asset_file)) + return bin_asset_file + + @property + def done_bin_asset_file(self) -> os.path: + bin_asset_file = "urdf/bin.urdf" + if "asset" in self.cfg: + return os.path.normpath(self.cfg["asset"].get( + "doneBinAssetFile", bin_asset_file)) + return bin_asset_file + + def _begin_aggregate(self, env_ptr, env_idx: int) -> List: + possible_objects = list(range(self.num_different_objects)) + object_indices = random.sample(possible_objects, + self.cfg["task"]["numObjectsPerBin"]) + + max_agg_bodies = self.num_robot_bodies + self.num_table_bodies + \ + + self.num_bin_bodies + + max_agg_shapes = self.num_robot_shapes + self.num_table_shapes + \ + self.num_bin_shapes + + for object_idx in object_indices: + max_agg_bodies += self.num_object_bodies[object_idx] + max_agg_shapes += self.num_object_shapes[object_idx] + + if self.aggregate_mode >= 1: + self.gym.begin_aggregate(env_ptr, max_agg_bodies, + max_agg_shapes, True) + return object_indices + + def _add_objects(self, env_ptr, i, object_assets, + object_start_poses, object_indices, + min_height: float = 0.9, + height_diff: float = 0.1) -> None: + self.object_ids.append(object_indices) + for j, object_idx in enumerate(object_indices): + x, y, _ = self.cfg["asset"]["activeBinPosition"] + z = min_height + height_diff * j + object_start_pose = gymapi.Transform(p=gymapi.Vec3(x, y, z)) + object_actor = self.gym.create_actor(env_ptr, + object_assets[object_idx], + object_start_pose, + self.objects_list[object_idx], + i, 0, 0) + + # Set color of EGAD objects + if self.objects_list[object_idx] in self.egad_objects: + self.gym.set_rigid_body_color( + env_ptr, object_actor, 0, gymapi.MeshType.MESH_VISUAL, + gymapi.Vec3(*self.egad_colors[ + self.objects_list[object_idx]])) + + self.object_init_state.append( + [object_start_pose.p.x, + object_start_pose.p.y, + object_start_pose.p.z, + object_start_pose.r.x, + object_start_pose.r.y, + object_start_pose.r.z, + object_start_pose.r.w, + 0, 0, 0, 0, 0, 0]) + + self.object_cleared_state.append( + [self.cfg["asset"]["doneBinPosition"][0], + self.cfg["asset"]["doneBinPosition"][1], + object_start_pose.p.z, + object_start_pose.r.x, + object_start_pose.r.y, + object_start_pose.r.z, + object_start_pose.r.w, + 0, 0, 0, 0, 0, 0]) + + object_idx = self.gym.get_actor_index(env_ptr, object_actor, + gymapi.DOMAIN_SIM) + self.object_indices[i, j] = object_idx + self.objects[i].append(object_actor) + + def _add_bins(self, env_ptr, i, active_bin_asset, done_bin_asset, + active_bin_start_pose, done_bin_start_pose) -> None: + if self.cfg["asset"]["useActiveBin"]: + active_bin_actor = self.gym.create_actor(env_ptr, active_bin_asset, + active_bin_start_pose, + "active_bin", i, -1, 1) + + done_bin_actor = self.gym.create_actor(env_ptr, done_bin_asset, + done_bin_start_pose, "done_bin", + i, -1, 1) + + # change color of done bin to green + self.gym.set_rigid_body_color(env_ptr, done_bin_actor, 0, + gymapi.MeshType.MESH_VISUAL, + gymapi.Vec3(0.05, 0.65, 0.05)) + + def compute_reward(self) -> None: + if self.cfg["control"]["useRelativeControl"]: + actions = self.actions + else: + actions = self.actions - self.prev_actions + self.prev_actions = self.actions + + self.rew_buf[:], self.reset_buf[:], self.progress_buf[:], \ + self.successes[:], self.success_rate[:], \ + self.remaining_objects[:], self.num_remaining_objects[:], \ + action_reward, \ + fingertips_to_object_distance_reward, \ + object_height_reward, \ + target_height_reward, \ + self.rewards_dict, \ + = compute_bin_picking_reward( + self.rew_buf, self.reset_buf, self.progress_buf, + self.successes, self.success_rate, self.remaining_objects, + self.object_pos, self.object_linvel, self.object_init_pos, + self.fingertip_pos, actions, + self.cfg["reward"]["scale"]["actionPenalty"], + self.cfg["reward"]["scale"]["fingertipsToObjectDistanceReward"], + self.max_episode_length, + self.cfg["reward"]["scale"]["objectHeightReward"], + self.cfg["reward"]["scale"]["targetHeightReward"], + self.cfg["reward"]["scale"]["objectFallsOffTablePenalty"], + self.cfg["reward"]["liftOffHeight"], + self.cfg["reward"]["maxXyDrift"], + self.cfg["reward"]["targetHeight"], + self.cfg["reward"]["epsFingertips"], + self.cfg["reward"]["epsHeight"], + torch.Tensor(self.cfg["reward"]["xyCenter"]).to( + self.device).unsqueeze(0).repeat(self.num_envs, 1).unsqueeze( + 1).repeat(1, self.cfg["task"]["numObjectsPerBin"], 1), + self.cfg["reward"]["sparse"], + self.cfg["reset"]["objectFallsOffTable"], + self.cfg["reset"]["objectLifted"], + self.cfg["reward"]["returnRewardsDict"] + ) + + if self.cfg["debug"]["verbose"]: + self.writer.add_scalar( + "action_reward", + torch.mean(action_reward), self.env_steps) + self.writer.add_scalar( + "fingertips_to_object_distance_reward", + torch.mean(fingertips_to_object_distance_reward), + self.env_steps) + self.writer.add_scalar( + "object_height_reward", + torch.mean(object_height_reward), self.env_steps) + self.writer.add_scalar( + "target_height_reward", + torch.mean(target_height_reward), self.env_steps) + + self.writer.add_scalar("successes", torch.sum(self.successes), + self.env_steps) + self.writer.add_scalar("success_rate", self.success_rate, + self.env_steps) + + def _process_state_tensors(self) -> None: + GraspingTask._process_state_tensors(self) + self.object_pos = self.root_state_tensor[self.object_indices, 0:3].view( + self.num_envs, self.cfg["task"]["numObjectsPerBin"], 3) + self.object_rot = self.root_state_tensor[self.object_indices, 3:7].view( + self.num_envs, self.cfg["task"]["numObjectsPerBin"], 4) + self.object_linvel = self.root_state_tensor[self.object_indices, + 7:10].view(self.num_envs, + self.cfg["task"]["numObjectsPerBin"], 3) + self.object_angvel = self.root_state_tensor[self.object_indices, + 10:13].view(self.num_envs, + self.cfg["task"]["numObjectsPerBin"], + 3) + if any([o.startswith("objectBbox") for o in self.object_obs]): + self.calculate_object_bbox() + if "objectSurfaceSamples" in self.object_obs: + self.calculate_object_surface_samples() + + def calculate_object_bbox(self) -> None: + if not hasattr(self, "object_bbox_extent"): + self.init_object_bbox() + + bbox_pos = self.object_pos + quat_apply( + self.object_rot, self.object_bbox_pos_offset) + bbox_rot = quat_mul(self.object_rot, self.object_bbox_rot_offset) + self.object_bbox_pose = torch.cat([bbox_pos, bbox_rot], dim=2) + self.object_bbox_corners = bbox_pos.unsqueeze(2).repeat(1, 1, 8, 1) + \ + quat_apply( + bbox_rot.unsqueeze(2).repeat(1, 1, 8, 1), + self.bbox_corner_coords * self.object_bbox_extent.unsqueeze( + 2).repeat(1, 1, 8, 1)) + self.object_bbox_bounds = torch.stack( + [self.object_bbox_corners[:, :, 0], + self.object_bbox_corners[:, :, -1]], + dim=2) + + def init_object_bbox(self) -> None: + from scipy.spatial.transform import Rotation as R + self.object_bbox_extent = [] + self.object_bbox_pos_offset = [] + self.object_bbox_rot_offset = [] + for env_id in range(self.num_envs): + self.object_bbox_extent.append([]) + self.object_bbox_pos_offset.append([]) + self.object_bbox_rot_offset.append([]) + for obj_id in range(self.cfg["task"]["numObjectsPerBin"]): + self.object_bbox_extent[env_id].append( + self.object_assets_dict["bbox"][self.object_ids[env_id][obj_id]][1]) + to_origin = self.object_assets_dict["bbox"][ + self.object_ids[env_id][obj_id]][0] + from_origin = np.linalg.inv(to_origin) + r = R.from_matrix(from_origin[0:3, 0:3]) + r_quat = r.as_quat() + translation = np.array( + [from_origin[0, 3], from_origin[1, 3], from_origin[2, 3]]) + self.object_bbox_pos_offset[env_id].append(translation) + self.object_bbox_rot_offset[env_id].append(r_quat) + + self.object_bbox_extent = torch.from_numpy( + np.array(self.object_bbox_extent)).to(self.device).float() + self.object_bbox_pos_offset = torch.from_numpy( + np.array(self.object_bbox_pos_offset)).to(self.device).float() + self.object_bbox_rot_offset = torch.from_numpy( + np.array(self.object_bbox_rot_offset)).to(self.device).float() + + self.bbox_corner_coords = torch.tensor( + [[[[-0.5, -0.5, -0.5], + [-0.5, -0.5, 0.5], + [-0.5, 0.5, -0.5], + [-0.5, 0.5, 0.5], + [0.5, -0.5, -0.5], + [0.5, -0.5, 0.5], + [0.5, 0.5, -0.5], + [0.5, 0.5, 0.5]]]], + device=self.device).repeat( + self.num_envs, self.cfg["task"]["numObjectsPerBin"], 1, 1) + + def calculate_object_surface_samples(self) -> None: + if not hasattr(self, "object_surface_sample_offset_pos"): + self.init_object_surface_samples() + object_pos = self.object_pos.unsqueeze(2).repeat( + 1, 1, self.object_surface_sample_offset_pos.shape[2], 1) + object_rot = self.object_rot.unsqueeze(2).repeat( + 1, 1, self.object_surface_sample_offset_pos.shape[2], 1) + self.object_surface_samples = object_pos + quat_apply( + object_rot, self.object_surface_sample_offset_pos) + + def init_object_surface_samples(self) -> None: + self.object_surface_sample_offset_pos = [] + for env_id in range(self.num_envs): + self.object_surface_sample_offset_pos.append([]) + for obj_id in range(self.cfg["task"]["numObjectsPerBin"]): + self.object_surface_sample_offset_pos[env_id].append( + self.object_assets_dict["samples"][ + self.object_ids[env_id][obj_id]]) + self.object_surface_sample_offset_pos = torch.from_numpy( + np.array(self.object_surface_sample_offset_pos)).to( + self.device).float() + + def add_object_obs(self, object_obs: List) -> int: + obs_idx = 0 + obj_obs = [] + + def add_to_obs(obs_list, key, obs) -> List: + obs_list.append(obs) + if self.cfg["task"]["returnObsDict"]: + self.add_separate_obs(key, obs) + return obs_list + + for obs_key in object_obs: + if obs_key == "objectPos": + obj_obs = add_to_obs(obj_obs, obs_key, self.object_pos) + elif obs_key == "objectLinVel": + obj_obs = add_to_obs(obj_obs, obs_key, self.object_linvel) + elif obs_key == "objectRot": + obj_obs = add_to_obs(obj_obs, obs_key, self.object_rot) + elif obs_key == "objectAngVel": + obj_obs = add_to_obs(obj_obs, obs_key, self.object_angvel) + elif obs_key == "objectBboxBounds": # min, max corners + obj_obs = add_to_obs(obj_obs, obs_key, + self.object_bbox_bounds.flatten(2, 3)) + elif obs_key == "objectBboxCorners": # 8 3D corner points + obj_obs = add_to_obs(obj_obs, obs_key, + self.object_bbox_corners.flatten(2, 3)) + elif obs_key == "objectBboxPose": # Center pose of bounding box + obj_obs = add_to_obs(obj_obs, obs_key, self.object_bbox_pose) + elif obs_key == "objectSurfaceSamples": # Samples on mesh surface + obj_obs = add_to_obs(obj_obs, obs_key, + self.object_surface_samples.flatten(2, 3)) + + obs_idx += self.num_obs_dict[obs_key] + obj_obs = torch.cat(obj_obs, dim=2) + self.obs_buf[:, 0:obs_idx] = obj_obs.view(self.num_envs, -1) + return obs_idx + + def _drop_objects(self, env_ids, skip_steps: int = 250) -> None: + assert torch.equal(env_ids, + to_torch(list(range(self.num_envs)), + device=self.device, dtype=torch.long)), \ + "Objects must be dropped in all environments simultaneously, as " \ + "this is a distinct phase where the physics simulation is " \ + "stepped before the RL agent can actually interact with the task." + + self._drop_objects_into_done_bin(env_ids, skip_steps) + self._drop_objects_into_active_bin(env_ids, skip_steps) + self._end_of_drop_objects(env_ids) + self._objects_dropped = True + + def _drop_objects_into_active_bin(self, env_ids, skip_steps) -> None: + self._drop_objects_into_bin( + env_ids, self.cfg["asset"]["activeObjectDropPos"], skip_steps) + self._set_object_init_state(env_ids, active=True) + + def _drop_objects_into_done_bin(self, env_ids, skip_steps) -> None: + self._drop_objects_into_bin( + env_ids, self.cfg["asset"]["doneObjectDropPos"], skip_steps) + self._set_object_init_state(env_ids, active=False) + + def _drop_objects_into_bin(self, env_ids, drop_pos, skip_steps) -> None: + for obj_idx in range(self.cfg["task"]["numObjectsPerBin"]): + # Select which objects to drop + active_object_indices = self.object_indices[:, obj_idx].flatten() + passive_object_indices = self.object_indices[:, obj_idx+1:].flatten() + object_indices = torch.cat([active_object_indices, + passive_object_indices]) + + # Sample states for object being dropped + active_object_state = self._sample_active_object_state( + active_object_indices, drop_pos) + + num_passive_objects = len(passive_object_indices) / self.num_envs + linspace = torch.linspace(-self.cfg["env"]["envSpacing"], + self.cfg["env"]["envSpacing"], + int(math.ceil(math.sqrt( + num_passive_objects))) + 2)[1:-1] + + passive_object_state = to_torch( + [[1, 0, 0.1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0]], device=self.device + ).repeat(int(len(passive_object_indices) / self.num_envs), 1) + + for i, x in enumerate(linspace): + for j, y in enumerate(linspace): + obj_idx = int(math.ceil(math.sqrt(num_passive_objects))) * i + j + if obj_idx < passive_object_state.shape[0]: + passive_object_state[obj_idx][0] = x + passive_object_state[obj_idx][1] = y + + passive_object_state = passive_object_state.repeat(self.num_envs, 1) + + self._position_objects_for_drop( + active_object_indices, passive_object_indices, + active_object_state, passive_object_state) + + # Set root state tensor + object_indices = torch.unique( + torch.cat([object_indices]).to(torch.int32)) + self.gym.set_actor_root_state_tensor_indexed( + self.sim, gymtorch.unwrap_tensor(self.root_state_tensor), + gymtorch.unwrap_tensor(object_indices), len(object_indices)) + + # Run simulation + for _ in range(skip_steps): + self.render() + self.gym.simulate(self.sim) + + def _sample_active_object_state(self, active_object_indices, drop_pos) -> Tensor: + active_rand_floats = torch_rand_float( + -1.0, 1.0, (len(active_object_indices), 5), device=self.device) + active_object_state = torch.zeros(len(active_object_indices), 13, + device=self.device) + active_object_state[:, 0] = drop_pos[0] + \ + self.cfg["initState"]["noise"][ + "objectPos"] * active_rand_floats[:, 0] + active_object_state[:, 1] = drop_pos[1] + \ + self.cfg["initState"]["noise"][ + "objectPos"] * active_rand_floats[:, 1] + active_object_state[:, 2] = drop_pos[2] + \ + self.cfg["initState"]["noise"][ + "objectPos"] * active_rand_floats[:, 2] + active_object_rot = randomize_rotation( + active_rand_floats[:, 3], active_rand_floats[:, 4], + self.x_unit_tensor[0].unsqueeze(0).repeat( + len(active_object_indices), 1), + self.y_unit_tensor[0].unsqueeze(0).repeat( + len(active_object_indices), 1)) + active_object_state[:, 3:7] = active_object_rot + return active_object_state + + def _end_of_drop_objects(self, env_ids) -> None: + pass + + def _not_in_active_bin(self, object_init_state) -> Tensor: + fallen_objects = object_init_state[:, :, 0] < self.cfg["asset"]["activeBinPosition"][0] - 0.3 + fallen_objects = torch.logical_or(fallen_objects, + object_init_state[:, :, 0] > self.cfg["asset"]["activeBinPosition"][0] + 0.3) + fallen_objects = torch.logical_or(fallen_objects, + object_init_state[:, :, 1] < self.cfg["asset"]["activeBinPosition"][1] - 0.175) + fallen_objects = torch.logical_or(fallen_objects, + object_init_state[:, :, 1] > self.cfg["asset"]["activeBinPosition"][1] + 0.175) + fallen_objects = torch.logical_or(fallen_objects, + object_init_state[:, :, 2] < 0.8) + return fallen_objects + + def _not_in_done_bin(self, object_init_state) -> Tensor: + fallen_objects = object_init_state[:, :, 0] < self.cfg["asset"]["doneBinPosition"][0] - 0.3 + fallen_objects = torch.logical_or(fallen_objects, + object_init_state[:, :, 0] > self.cfg["asset"]["doneBinPosition"][0] + 0.3) + fallen_objects = torch.logical_or(fallen_objects, + object_init_state[:, :, 1] < self.cfg["asset"]["doneBinPosition"][1] - 0.175) + fallen_objects = torch.logical_or(fallen_objects, + object_init_state[:, :, 1] > self.cfg["asset"]["doneBinPosition"][1] + 0.175) + fallen_objects = torch.logical_or(fallen_objects, + object_init_state[:, :, 2] < 0.8) + return fallen_objects + + def _set_object_init_state(self, env_ids, active: bool) -> None: + self.gym.refresh_actor_root_state_tensor(self.sim) + object_init_state = self.root_state_tensor[self.object_indices[env_ids]] + if active: + fallen_objects = self._not_in_active_bin(object_init_state) + else: + fallen_objects = self._not_in_done_bin(object_init_state) + + num_redropping_attempts = 5 + for attempt in range(num_redropping_attempts): + if torch.any(fallen_objects): + print(f"Dropping fallen objects again (Attempt {attempt}).") + if active: + fallen_objects, object_init_state = self._redrop_objects( + fallen_objects, + self.cfg["asset"]["activeObjectDropPos"], active=True) + else: + fallen_objects, object_init_state = self._redrop_objects( + fallen_objects, self.cfg["asset"]["doneObjectDropPos"], + active=False) + + if torch.any(fallen_objects): + assert False, "Not all objects were positioned correctly." + + if active: + self.init_root_state_tensor = self.root_state_tensor.clone() + self.object_init_state = object_init_state + self.object_init_pos = self.object_init_state[:, :, 0:3] + else: + self.object_cleared_state = object_init_state + + def _redrop_objects(self, fallen_objects, drop_pos, skip_steps=250, + active=True): + num_fallen_objects = torch.sum(fallen_objects.int(), dim=1) + env_nums = torch.arange(0, self.num_envs, dtype=torch.long, device=self.device) + fallen_object_indices = -torch.ones(self.num_envs, torch.max(num_fallen_objects) + 1, dtype=torch.long, device=self.device) + curr_fallen_idx = torch.zeros(self.num_envs, dtype=torch.long, device=self.device) + indices = torch.stack([env_nums, curr_fallen_idx]) + + for obj_idx in range(self.cfg["task"]["numObjectsPerBin"]): + if torch.any(fallen_objects[:, obj_idx]): + fallen_object_indices[tuple(indices)] = torch.where( + fallen_objects[:, obj_idx], + torch.ones_like(fallen_object_indices[tuple(indices)]) * obj_idx, + torch.ones_like(fallen_object_indices[tuple(indices)]) * -1) + indices[1] += fallen_objects[:, obj_idx].int() + + for fallen_idx in range(fallen_object_indices.shape[1] - 1): + active_object_indices = [] + for env_id, idx in enumerate(fallen_object_indices[:, fallen_idx]): + if idx >= 0: + active_object_indices.append(self.object_indices[env_id, idx]) + + active_object_indices = torch.Tensor(active_object_indices).long().to(self.device) + + # Sample states for object being dropped + active_object_state = self._sample_active_object_state( + active_object_indices, drop_pos) + + self._position_objects_for_drop( + active_object_indices, torch.Tensor([]).long(), + active_object_state, torch.Tensor([])) + + # Set root state tensor + object_indices = torch.unique( + torch.cat([active_object_indices]).to(torch.int32)) + self.gym.set_actor_root_state_tensor_indexed( + self.sim, gymtorch.unwrap_tensor(self.root_state_tensor), + gymtorch.unwrap_tensor(object_indices), len(object_indices)) + + # Run simulation + for _ in range(skip_steps): + self.render() + self.gym.simulate(self.sim) + + self.gym.refresh_actor_root_state_tensor(self.sim) + object_init_state = self.root_state_tensor[self.object_indices] + if active: + fallen_objects = self._not_in_active_bin(object_init_state) + else: + fallen_objects = self._not_in_done_bin(object_init_state) + object_init_state[..., 7:] = 0. + return fallen_objects, object_init_state + + def _position_objects_for_drop(self, active_object_indices: Tensor, + passive_object_indices: Tensor, + active_object_state: Tensor, + passive_object_state: Tensor) -> None: + self.root_state_tensor[active_object_indices] = \ + active_object_state + + if len(passive_object_indices) > 0: + self.root_state_tensor[passive_object_indices] = \ + passive_object_state + + def _reset_objects(self, env_ids) -> None: + # Find cleared and remaining objects and their init states + remaining_object_indices = torch.masked_select( + self.object_indices, self.remaining_objects == 1) + remaining_object_init_state = torch.masked_select( + self.object_init_state, + (self.remaining_objects == 1).unsqueeze(2).repeat(1, 1, 13)).view(-1, 13) + cleared_object_indices = torch.masked_select( + self.object_indices, self.remaining_objects == 0) + cleared_object_init_state = torch.masked_select( + self.object_cleared_state, + (self.remaining_objects == 0).unsqueeze(2).repeat(1, 1, 13)).view(-1, 13) + + # Reset remaining and cleared objects + if len(remaining_object_indices) > 0: + self.root_state_tensor[remaining_object_indices] = \ + remaining_object_init_state + if len(cleared_object_indices) > 0: + self.root_state_tensor[cleared_object_indices] = \ + cleared_object_init_state + + self.prev_remaining_objects = self.remaining_objects + + # Set root state tensor of the simulation + object_indices = torch.unique( + torch.cat([self.object_indices[env_ids]]).to(torch.int32)) + self.gym.set_actor_root_state_tensor_indexed( + self.sim, gymtorch.unwrap_tensor(self.root_state_tensor), + gymtorch.unwrap_tensor(object_indices), len(object_indices)) + + # Refill empty bins + empty_bin = (self.num_remaining_objects == 0).bool() + if torch.any(empty_bin): + selection_mask = empty_bin.unsqueeze(1).repeat(1, self.cfg["task"]["numObjectsPerBin"]) + refill_indices = torch.masked_select(self.object_indices, selection_mask) + self.root_state_tensor[refill_indices] = self.init_root_state_tensor[refill_indices] + object_indices = torch.unique( + torch.cat([refill_indices]).to(torch.int32)) + self.gym.set_actor_root_state_tensor_indexed( + self.sim, gymtorch.unwrap_tensor(self.root_state_tensor), + gymtorch.unwrap_tensor(object_indices), len(object_indices)) + + def get_object_set(self): + obj_set = self.obs_buf[:, 0:self.object_state_size * self.cfg["task"]["numObjectsPerBin"]] + obj_set = obj_set.view(self.num_envs, self.cfg["task"]["numObjectsPerBin"], + self.object_state_size) + return obj_set + + def _draw_object_pose(self, i: int) -> None: + for j in range(self.cfg["task"]["numObjectsPerBin"]): + self._draw_coordinate_system(self.envs[i], self.object_pos[i, j], + self.object_rot[i, j]) + + def _draw_hand_object_distance(self, i: int) -> None: + hand_pos = self.hand_pos[i].cpu().numpy() + for j in range(self.cfg["task"]["numObjectsPerBin"]): + object_pos = self.object_pos[i, j].cpu().numpy() + self.gym.add_lines(self.viewer, self.envs[i], 1, + [object_pos[0], object_pos[1], object_pos[2], + hand_pos[0], hand_pos[1], hand_pos[2]], + [0.85, 0.1, 0.85]) + + def _draw_object_target_distance(self, i: int) -> None: + for j in range(self.cfg["task"]["numObjectsPerBin"]): + object_pos = self.object_pos[i, j].cpu().numpy() + target_pos = object_pos.copy() + target_pos[2] = self.cfg["reward"]["targetHeight"] + self.gym.add_lines(self.viewer, self.envs[i], 1, + [object_pos[0], object_pos[1], object_pos[2], + target_pos[0], target_pos[1], target_pos[2]], + [0.85, 0.85, 0.1]) + + def _draw_adaptive_relation_graph(self, i: int, num_relations: int = 2) -> None: + object_pos = self.object_pos[i].cpu() + object_pos_i = object_pos.unsqueeze(0).repeat( + self.cfg["task"]["numObjectsPerBin"], 1, 1) + object_pos_j = object_pos.unsqueeze(1).repeat( + 1, self.cfg["task"]["numObjectsPerBin"], 1) + o_pair = torch.stack([object_pos_i, object_pos_j], dim=-1) + + object_distances = torch.norm((o_pair[..., 0] - o_pair[..., 1]), dim=2) + + smallest_distances, indices = torch.topk(object_distances, num_relations + 1, dim=1, largest=False) + smallest_distances = smallest_distances[:, 1:] + indices = indices[:, 1:] + + for o in range(self.cfg["task"]["numObjectsPerBin"]): + start_pos = object_pos[o].numpy() + for idx in indices[o]: + target_pos = object_pos[idx].numpy() + self.gym.add_lines(self.viewer, self.envs[i], 1, + [start_pos[0], start_pos[1], start_pos[2], + target_pos[0], target_pos[1], + target_pos[2]], + [0.85, 0.1, 0.85]) + + def _color_objects_on_success(self, i: int) -> None: + for j in range(self.cfg["task"]["numObjectsPerBin"]): + if torch.norm(self.object_pos[i] - self.goal_pos) <= \ + self.cfg["reward"]["goalProximityThreshold"]: + self.gym.set_rigid_body_color(self.envs[i], self.objects[i][j], + 0, gymapi.MeshType.MESH_VISUAL, + gymapi.Vec3(0.1, 0.95, 0.1)) + + def _draw_debug_visualization(self, i: int) -> None: + super()._draw_debug_visualization(i) + if self.cfg["debug"]["drawAdaptiveRelationGraph"]: + self._draw_adaptive_relation_graph(i) + + def _draw_object_bbox( + self, + i: int, + draw_bounds: bool = False, + draw_corners: bool = True, + draw_pose: bool = False + ) -> None: + assert any([o.startswith("objectBbox") for o in self.object_obs]), \ + "Cannot draw bounding boxes of objects if it is not included " \ + "in the observations." + for j in range(self.cfg["task"]["numObjectsPerBin"]): + # draw bounding box + relative_bbox = torch.stack([-0.5 * self.object_bbox_extent[i, j], + 0.5 * self.object_bbox_extent[i, j]], + dim=0) + bbox_pos = self.object_bbox_pose[i, j, 0:3].cpu().numpy() + bbox_rot = self.object_bbox_pose[i, j, 3:7].cpu().numpy() + bbox_pose = gymapi.Transform( + p=gymapi.Vec3( + x=bbox_pos[0], y=bbox_pos[1], z=bbox_pos[2]), + r=gymapi.Quat( + x=bbox_rot[0], y=bbox_rot[1], z=bbox_rot[2], w=bbox_rot[3])) + bbox = gymutil.WireframeBBoxGeometry(relative_bbox, pose=bbox_pose) + gymutil.draw_lines(bbox, self.gym, self.viewer, self.envs[i], + pose=gymapi.Transform()) + + if draw_bounds: + bbox_bounds = self.object_bbox_bounds[i, j].cpu().numpy() + front_lower_left_pose = gymapi.Transform( + p=gymapi.Vec3(x=bbox_bounds[0, 0], + y=bbox_bounds[0, 1], + z=bbox_bounds[0, 2])) + back_upper_right_pose = gymapi.Transform( + p=gymapi.Vec3(x=bbox_bounds[1, 0], + y=bbox_bounds[1, 1], + z=bbox_bounds[1, 2])) + front_lower_left = gymutil.WireframeSphereGeometry( + 0.005, 12, 12, front_lower_left_pose, color=(1, 1, 0)) + back_upper_right = gymutil.WireframeSphereGeometry( + 0.005, 12, 12, back_upper_right_pose, color=(1, 1, 0)) + gymutil.draw_lines(front_lower_left, self.gym, self.viewer, + self.envs[i], + pose=gymapi.Transform()) + gymutil.draw_lines(back_upper_right, self.gym, self.viewer, + self.envs[i], + pose=gymapi.Transform()) + if draw_corners: + bbox_corners = self.object_bbox_corners[i, j].cpu().numpy() + for k in range(bbox_corners.shape[0]): + corner_pose = gymapi.Transform( + p=gymapi.Vec3(x=bbox_corners[k, 0], + y=bbox_corners[k, 1], + z=bbox_corners[k, 2])) + corner = gymutil.WireframeSphereGeometry( + 0.005, 12, 12, corner_pose, color=(1, 0, 1)) + gymutil.draw_lines(corner, self.gym, self.viewer, + self.envs[i], + pose=gymapi.Transform()) + + if draw_pose: + self._draw_coordinate_system(self.envs[i], + self.object_bbox_pose[i, j, 0:3], + self.object_bbox_pose[i, j, 3:7]) + + def _draw_object_surface_samples(self, i: int) -> None: + assert "objectSurfaceSamples" in self.object_obs, \ + "Cannot draw surface samples of objects if they are not included " \ + "in the observations." + for j in range(self.cfg["task"]["numObjectsPerBin"]): + surface_samples = self.object_surface_samples[i, j].cpu().numpy() + for k in range(surface_samples.shape[0]): + sample_pose = gymapi.Transform( + p=gymapi.Vec3(x=surface_samples[k, 0], + y=surface_samples[k, 1], + z=surface_samples[k, 2])) + sample_sphere = gymutil.WireframeSphereGeometry( + 0.0025, 12, 12, sample_pose, color=(1, 0, 1)) + gymutil.draw_lines(sample_sphere, self.gym, self.viewer, + self.envs[i], pose=gymapi.Transform()) + + +@torch.jit.script +def compute_bin_picking_reward( + rew_buf, reset_buf, progress_buf, successes, success_rate, remaining_objects, + object_pos, object_linvel, object_init_pos, + fingertip_pos, actions, + action_penalty_scale: float, + fingertips_to_object_distance_reward_scale: float, + max_episode_length: int, + object_height_reward_scale: float, + target_height_reward_scale: float, + object_falls_off_table_penalty_scale: float, + lift_off_height: float, + max_xy_drift: float, + target_height: float, + eps_fingertips: float, + eps_height: float, + xy_center, + sparse: bool, + reset_when_object_falls_off_table: bool, + reset_when_object_is_lifted: bool, + return_rewards_dict: bool, +): + # Rewards are calculated by first calculating usual object lifting rewards + # for all objects, that multiplying the rewards by a mask of objects which + # are still in the bin that the agent is picking from and then summing up + # the result. + + # ============ computing reward ... ============ + # penalize large actions: r ~ -|| a_t ||^2 + squared_action_norm = torch.sum(actions.pow(2), dim=-1) + action_reward = squared_action_norm * action_penalty_scale + + # penalize fingertips-object distance: r ~ (1 / (d(ft, obj)^2 + ϵ_ft))^2 + under_object_center = object_pos.clone() + under_object_center[:, :, 2] -= 0.0 + under_object_center = under_object_center.unsqueeze(1).repeat( + 1, fingertip_pos.shape[1], 1, 1) + fingertips_to_object_distance = torch.norm( + fingertip_pos.unsqueeze(2).repeat(1, 1, object_pos.shape[1], 1) + - under_object_center, dim=-1) + thumb_scaling = 2 + fingertips_to_object_distance[:, 0] *= thumb_scaling + fingertips_to_object_distance = torch.sum(fingertips_to_object_distance, + dim=1) + + fingertips_to_object_distance_reward = \ + 1.0 / (eps_fingertips + 5 * fingertips_to_object_distance.pow(2)) + fingertips_to_object_distance_reward *= fingertips_to_object_distance_reward + fingertips_to_object_distance_reward *= fingertips_to_object_distance_reward_scale + + # continuous height reward: r ~ 1 / (Δh + eps_h) + object_raised_by = object_pos[:, :, 2] - object_init_pos[:, :, 2] + delta_height = torch.clamp( + object_raised_by - lift_off_height, max=0) + object_height_reward = (1.0 / (torch.abs(delta_height) + eps_height)) - ( + 1.0 / (lift_off_height + eps_height)) + object_height_reward *= object_height_reward_scale + + # object height is not rewarded far away from center + xy_drift = torch.norm(object_pos[:, :, 0:2] - xy_center, dim=2) + close_to_center = xy_drift <= max_xy_drift + object_height_reward *= close_to_center.float() + + # reward reaching the target height: r ~ 1(object_lifted == True) + object_lifted = object_raised_by >= target_height + target_height_reward = target_height_reward_scale * object_lifted.float() + + # compute final reward + rewards_dict = {} + if sparse: + reward = object_lifted.float() - 1 + if return_rewards_dict: + dense_reward = fingertips_to_object_distance_reward + \ + object_height_reward + \ + target_height_reward + rewards_dict["sparse"] = reward + rewards_dict["dense"] = dense_reward + else: + reward = fingertips_to_object_distance_reward + \ + object_height_reward + \ + target_height_reward + if return_rewards_dict: + sparse_reward = object_lifted.float() - 1 + rewards_dict["sparse"] = sparse_reward + rewards_dict["dense"] = reward + + # Mask reward to only include objects that are still in the bin + reward *= remaining_objects + # Sum up reward over all objects + reward = torch.sum(reward, dim=1) + reward += action_reward + + # ============ determining resets ... ============ + # reset environments that have reached the maximum number of steps + resets = torch.where(progress_buf >= max_episode_length, + torch.ones_like(reset_buf), reset_buf) + + # reset environments that lifted the object successfully + if reset_when_object_is_lifted: + num_objects_lifted = torch.sum(object_lifted.int(), dim=1) + resets = torch.where(num_objects_lifted > 0, + torch.ones_like(resets), resets) + + # reset environments if object falls of the table + if reset_when_object_falls_off_table: + table_height = 0.8 + num_objects_fallen = torch.sum((object_pos[:, :, 2] < table_height).int(), dim=1) + resets = torch.where(num_objects_fallen > 0, + torch.ones_like(resets), resets) + reward += num_objects_fallen * object_falls_off_table_penalty_scale + + # remove lifted objects from bin + remaining_objects -= object_lifted.long() + num_remaining_objects = torch.sum(remaining_objects, dim=1) + + empty_bin = (num_remaining_objects == 0).unsqueeze(1).repeat( + 1, object_pos.shape[1]) + + # Put all objects back into the starting bin if none are remaining + remaining_objects = torch.where(empty_bin, + torch.ones_like(remaining_objects), + remaining_objects) + + return reward, resets, progress_buf, \ + successes, success_rate, \ + remaining_objects, num_remaining_objects, \ + action_reward, \ + fingertips_to_object_distance_reward, \ + object_height_reward, \ + target_height_reward, \ + rewards_dict + + +@torch.jit.script +def randomize_rotation(rand0, rand1, x_unit_tensor, y_unit_tensor): + return quat_mul(quat_from_angle_axis(rand0 * np.pi, x_unit_tensor), + quat_from_angle_axis(rand1 * np.pi, y_unit_tensor)) diff --git a/rofunc/learning/RofuncRL/tasks/isaacgymenv/grasp/lift_object.py b/rofunc/learning/RofuncRL/tasks/isaacgymenv/grasp/lift_object.py new file mode 100644 index 00000000..7c32121f --- /dev/null +++ b/rofunc/learning/RofuncRL/tasks/isaacgymenv/grasp/lift_object.py @@ -0,0 +1,803 @@ +import os +import glob +from collections import defaultdict +from isaacgym import gymtorch, gymapi, gymutil +from isaacgym.torch_utils import * +from typing import * +from rofunc.learning.RofuncRL.tasks.isaacgymenv.grasp.base.grasping_task import GraspingTask + + +class LiftObjectTask(GraspingTask): + def __init__(self, + cfg: Dict[str, Any], + sim_device: str, + graphics_device_id: int, + headless: bool) -> None: + """Creates UR5e-SIH robot on the task of lifting an object off the + table. + + Args: + cfg: Configuration dictionary that contains parameters for + how the simulation should run and defines properties of the + task. + sim_device: Device on which the physics simulation is run + (e.g. "cuda:0" or "cpu"). + graphics_device_id: ID of the device to run the rendering. + headless: Whether to run the simulation without a viewer. + """ + super().__init__(cfg, sim_device, graphics_device_id, headless) + + def _parse_cfg(self, cfg: Dict[str, Any]) -> None: + super()._parse_cfg(cfg) + self.ycb_objects, self.egad_objects = self._parse_objects(cfg["task"]) + self.egad_colors = {name: tuple(np.random.rand(3)) + for name in self.egad_objects} + self.objects_list = self.ycb_objects + self.egad_objects + self.object_obs, self.robot_obs = self._parse_observation_type( + cfg["task"]["observationType"], cfg["task"]["num_observations"]) + self.cfg["env"]["numObservations"] = self.num_observations + + def _parse_objects(self, task_cfg: Dict[str, Any]) -> List[List[str]]: + object_datasets = ["ycbObjects", "egadObjects"] + used_objects = [] + for dataset in object_datasets: + if dataset in task_cfg.keys(): + if isinstance(task_cfg[dataset], str): + requested_objects = [task_cfg[dataset]] + else: + requested_objects = task_cfg[dataset] + object_list = [] + for obj in requested_objects: + if "*" in obj: + object_list += self._solve_object_regex(obj, dataset) + else: + object_list.append(obj) + used_objects.append(object_list) + else: + used_objects.append([]) + return used_objects + + def _solve_object_regex(self, regex: str, dataset: str) -> List[str]: + if dataset == "ycbObjects": + root = os.path.join(self.asset_root, self.ycb_object_asset_root) + elif dataset == "egadObjects": + root = os.path.join(self.asset_root, self.egad_object_asset_root) + else: + assert False + object_list = [] + regex = os.path.join(root, regex) + for path in glob.glob(regex): + file_name = path.split("/")[-1] + if "." in file_name: + obj, extension = file_name.split(".") + else: + obj = file_name + extension = "" + if extension == "urdf": + object_list.append(obj) + return object_list + + def _parse_observation_type(self, observation_type, + num_observations) -> Tuple[List, List]: + if isinstance(observation_type, str): + observation_type = [observation_type] + + self.num_observations = 0 + object_observations, robot_observations = [], [] + for obs in observation_type: + if not (obs in num_observations.keys()): + raise ValueError(f"Unknown observation type '{obs}' given. " + f"Should be in {num_observations.keys()}.") + if obs.startswith("object"): + object_observations.append(obs) + else: + robot_observations.append(obs) + self.num_observations += num_observations[obs] + self.num_obs_dict = num_observations + + if "fingertipContactForces" in observation_type: + self.cfg["sim"]["useContactForces"] = True + + return object_observations, robot_observations + + def _load_task_assets(self) -> Dict[str, Any]: + self.objects = [] + self.object_init_state = [] + self.object_indices, self.object_rigid_body_indices = [], [] + self.object_ids = [] + object_assets_dict = self._load_object_assets() + self.object_assets_dict = object_assets_dict + return object_assets_dict + + def _add_task(self, env_ptr, i, task_asset_dict: Dict[str, Any], + agg_info) -> None: + self._add_object(env_ptr, i, task_asset_dict["asset"], + task_asset_dict["start_pose"]) + + def _process_task_handles(self) -> None: + self.object_init_state = to_torch( + self.object_init_state, device=self.device, dtype=torch.float + ).view(self.num_envs, 13) + self.default_object_init_state = self.object_init_state.clone() + self.object_indices = to_torch( + self.object_indices, dtype=torch.long, device=self.device) + self.object_rigid_body_indices = to_torch( + self.object_rigid_body_indices, dtype=torch.long, + device=self.device) + + def _load_object_assets(self): + """Loads the object(s) to be picked up from the YCB or EGAD dataset.""" + object_assets_dict = defaultdict(list) + + self.num_object_bodies, self.num_object_shapes = [], [] + # load YCB objects + for ycb_object in self.ycb_objects: + object_asset_file = os.path.join( + self.ycb_object_asset_root, + os.path.normpath(ycb_object + "/" + ycb_object + ".urdf")) + object_assets_dict = self._load_object( + object_assets_dict, object_asset_file, + start_pose=gymapi.Transform(p=gymapi.Vec3(0, 0, 1.0))) + + # load EGAD objects + for egad_object in self.egad_objects: + object_asset_file = os.path.join( + self.egad_object_asset_root, + os.path.normpath(egad_object + ".urdf")) + object_assets_dict = self._load_object( + object_assets_dict, object_asset_file, + start_pose=gymapi.Transform(p=gymapi.Vec3(0, 0, 1.0))) + return object_assets_dict + + def _load_object(self, object_assets_dict, asset_file, start_pose) -> Dict[str, Any]: + object_asset_options = gymapi.AssetOptions() + object_asset_options.override_com = True + object_asset_options.override_inertia = True + # Enable convex decomposition + object_asset_options.vhacd_enabled = True + object_asset_options.vhacd_params = gymapi.VhacdParams() + object_asset_options.vhacd_params.resolution = 1000000 + + object_assets_dict["asset"].append( + self.gym.load_asset(self.sim, self.asset_root, asset_file, + object_asset_options)) + object_assets_dict["start_pose"].append(start_pose) + + # retrieve mesh-specific information in case of shape-dependent obs + if any([o.startswith("objectBbox") for o in self.object_obs]): + from urdfpy import URDF + import trimesh + object_urdf = URDF.load( + os.path.join(self.asset_root, asset_file)) + object_bounding_box = trimesh.bounds.oriented_bounds( + object_urdf.base_link.collision_mesh) + object_assets_dict["bbox"].append(object_bounding_box) + + if "objectSurfaceSamples" in self.object_obs: + from urdfpy import URDF + import trimesh + object_urdf = URDF.load( + os.path.join(self.asset_root, asset_file)) + object_mesh = object_urdf.base_link.collision_mesh + surface_samples = np.array(trimesh.sample.sample_surface( + object_mesh, count=20)[0]).astype(np.float) + object_assets_dict["samples"].append(surface_samples) + + # update number of object bodies and shapes + self.num_object_bodies.append( + self.gym.get_asset_rigid_body_count( + object_assets_dict["asset"][-1])) + self.num_object_shapes.append( + self.gym.get_asset_rigid_shape_count( + object_assets_dict["asset"][-1])) + return object_assets_dict + + @property + def egad_object_asset_root(self) -> os.path: + object_asset_root = os.path.normpath("urdf/egad/train") + if "asset" in self.cfg: + return os.path.normpath(self.cfg["asset"].get( + "egadObjectAssetRoot", object_asset_root)) + return object_asset_root + + @property + def ycb_object_asset_root(self) -> os.path: + object_asset_root = os.path.normpath("urdf/ycb_video_objects/centered") + if "asset" in self.cfg: + return os.path.normpath(self.cfg["asset"].get( + "ycbObjectAssetRoot", object_asset_root)) + return object_asset_root + + @property + def num_different_objects(self) -> int: + return len(self.ycb_objects) + len(self.egad_objects) + + def _begin_aggregate(self, env_ptr, env_idx: int) -> None: + object_idx = env_idx % self.num_different_objects + max_agg_bodies = self.num_robot_bodies + self.num_table_bodies + \ + self.num_object_bodies[object_idx] + max_agg_shapes = self.num_robot_shapes + self.num_table_shapes + \ + self.num_object_shapes[object_idx] + if self.aggregate_mode >= 1: + self.gym.begin_aggregate(env_ptr, max_agg_bodies, + max_agg_shapes, True) + + def _add_object(self, env_ptr, i, object_assets, + object_start_poses) -> None: + # choose object and create actor + object_id = i % self.num_different_objects + self.object_ids.append(object_id) + object_actor = self.gym.create_actor(env_ptr, object_assets[object_id], + object_start_poses[object_id], + self.objects_list[object_id], i, + 0, 0) + + # infer rigid body index + object_rigid_body_name = self.gym.get_actor_rigid_body_names( + env_ptr, object_actor)[0] + object_rigid_body_index = self.gym.find_actor_rigid_body_index( + env_ptr, object_actor, object_rigid_body_name, gymapi.DOMAIN_SIM) + + self.object_init_state.append( + [object_start_poses[object_id].p.x, + object_start_poses[object_id].p.y, + object_start_poses[object_id].p.z, + object_start_poses[object_id].r.x, + object_start_poses[object_id].r.y, + object_start_poses[object_id].r.z, + object_start_poses[object_id].r.w, + 0, 0, 0, 0, 0, 0]) + self.object_indices.append(self.gym.get_actor_index( + env_ptr, object_actor, gymapi.DOMAIN_SIM)) + self.object_rigid_body_indices.append(object_rigid_body_index) + self.objects.append(object_actor) + + def compute_reward(self) -> None: + if self.cfg["control"]["useRelativeControl"]: + actions = self.actions + else: + actions = self.actions - self.prev_actions + self.prev_actions = self.actions + + self.rew_buf[:], self.reset_buf[:], self.progress_buf[:], \ + self.successes[:], self.success_rate[:], \ + action_reward, \ + fingertips_to_object_distance_reward, \ + object_height_reward, \ + object_velocity_reward, \ + target_height_reward, \ + self.rewards_dict, \ + = compute_object_lifting_reward( + self.rew_buf, self.reset_buf, self.progress_buf, + self.successes, self.success_rate, + self.object_pos, self.object_linvel, self.object_init_pos, + self.fingertip_pos, actions, + self.cfg["reward"]["scale"]["actionPenalty"], + self.cfg["reward"]["scale"]["fingertipsToObjectDistanceReward"], + self.max_episode_length, + self.cfg["reward"]["scale"]["objectHeightReward"], + self.cfg["reward"]["scale"]["objectVelocityReward"], + self.cfg["reward"]["scale"]["targetHeightReward"], + self.cfg["reward"]["scale"]["objectFallsOffTablePenalty"], + self.cfg["reward"]["liftOffHeight"], + self.cfg["reward"]["maxXyDrift"], + self.cfg["reward"]["targetHeight"], + self.cfg["reward"]["epsFingertips"], + self.cfg["reward"]["epsHeight"], + torch.Tensor(self.cfg["reward"]["xyCenter"]).to( + self.device).unsqueeze(0).repeat(self.num_envs, 1), + self.cfg["reward"]["sparse"], + self.cfg["reset"]["objectFallsOffTable"], + self.cfg["reset"]["objectLifted"], + self.cfg["reward"]["returnRewardsDict"], + ) + + if self.cfg["debug"]["verbose"]: + self.writer.add_scalar( + "action_reward", + torch.mean(action_reward), self.env_steps) + self.writer.add_scalar( + "fingertips_to_object_distance_reward", + torch.mean(fingertips_to_object_distance_reward), self.env_steps) + self.writer.add_scalar( + "object_height_reward", + torch.mean(object_height_reward), self.env_steps) + self.writer.add_scalar( + "object_velocity_reward", + torch.mean(object_velocity_reward), self.env_steps) + self.writer.add_scalar( + "target_height_reward", + torch.mean(target_height_reward), self.env_steps) + + self.writer.add_scalar("successes", torch.sum(self.successes), + self.env_steps) + self.writer.add_scalar("success_rate", self.success_rate, + self.env_steps) + + def compute_observations(self): + """Updates the state tensors and adds the desired information to the + observation buffer.""" + self._refresh_state_tensors() + self._process_state_tensors() + + obs_idx = self.add_object_obs(self.object_obs) + + for obs in self.robot_obs: + if obs == "fingertipContactForces": + self.add_fingertip_contact_forces(obs_idx) + elif obs == "fingertipPos": + self.add_fingertip_pos(obs_idx) + elif obs == "handDofPos": + self.add_hand_dof_pos(obs_idx) + elif obs == "handPose": + self.add_hand_pose(obs_idx) + elif obs == "jointPos": + self.add_joint_pos(obs_idx) + elif obs == "jointVel": + self.add_joint_vel(obs_idx) + elif obs == "previousAction": + self.add_previous_action(obs_idx) + obs_idx += self.num_obs_dict[obs] + + def _process_state_tensors(self) -> None: + super()._process_state_tensors() + self.object_pos = self.root_state_tensor[self.object_indices, 0:3] + self.object_rot = self.root_state_tensor[self.object_indices, 3:7] + self.object_linvel = self.root_state_tensor[self.object_indices, 7:10] + self.object_angvel = self.root_state_tensor[self.object_indices, 10:13] + + if any([o.startswith("objectBbox") for o in self.object_obs]): + self.calculate_object_bbox() + if "objectSurfaceSamples" in self.object_obs: + self.calculate_object_surface_samples() + + def calculate_object_bbox(self) -> None: + if not hasattr(self, "object_bbox_extent"): + self.init_object_bbox() + + bbox_pos = self.object_pos + quat_apply( + self.object_rot, self.object_bbox_pos_offset) + bbox_rot = quat_mul(self.object_rot, self.object_bbox_rot_offset) + self.object_bbox_pose = torch.cat([bbox_pos, bbox_rot], dim=1) + self.object_bbox_corners = bbox_pos.unsqueeze(1).repeat(1, 8, 1) + \ + quat_apply( + bbox_rot.unsqueeze(1).repeat(1, 8, 1), + self.bbox_corner_coords * self.object_bbox_extent.unsqueeze( + 1).repeat(1, 8, 1)) + self.object_bbox_bounds = torch.stack( + [self.object_bbox_corners[:, 0], self.object_bbox_corners[:, -1]], + dim=1) + + def init_object_bbox(self) -> None: + from scipy.spatial.transform import Rotation as R + self.object_bbox_extent = [] + self.object_bbox_pos_offset = [] + self.object_bbox_rot_offset = [] + for env_id in range(self.num_envs): + self.object_bbox_extent.append( + self.object_assets_dict["bbox"][self.object_ids[env_id]][1]) + to_origin = self.object_assets_dict["bbox"][ + self.object_ids[env_id]][0] + from_origin = np.linalg.inv(to_origin) + r = R.from_matrix(from_origin[0:3, 0:3]) + r_quat = r.as_quat() + translation = np.array( + [from_origin[0, 3], from_origin[1, 3], from_origin[2, 3]]) + self.object_bbox_pos_offset.append(translation) + self.object_bbox_rot_offset.append(r_quat) + + self.object_bbox_extent = torch.from_numpy( + np.stack(self.object_bbox_extent)).to(self.device).float() + self.object_bbox_pos_offset = torch.from_numpy( + np.stack(self.object_bbox_pos_offset)).to(self.device).float() + self.object_bbox_rot_offset = torch.from_numpy( + np.stack(self.object_bbox_rot_offset)).to(self.device).float() + + self.bbox_corner_coords = torch.tensor( + [[[-0.5, -0.5, -0.5], + [-0.5, -0.5, 0.5], + [-0.5, 0.5, -0.5], + [-0.5, 0.5, 0.5], + [0.5, -0.5, -0.5], + [0.5, -0.5, 0.5], + [0.5, 0.5, -0.5], + [0.5, 0.5, 0.5]]], + device=self.device).repeat(self.num_envs, 1, 1) + + def calculate_object_surface_samples(self) -> None: + if not hasattr(self, "object_surface_sample_offset_pos"): + self.init_object_surface_samples() + object_pos = self.object_pos.unsqueeze(1).repeat( + 1, self.object_surface_sample_offset_pos.shape[1], 1) + object_rot = self.object_rot.unsqueeze(1).repeat( + 1, self.object_surface_sample_offset_pos.shape[1], 1) + self.object_surface_samples = object_pos + quat_apply( + object_rot, self.object_surface_sample_offset_pos) + + def init_object_surface_samples(self) -> None: + self.object_surface_sample_offset_pos = [] + for env_id in range(self.num_envs): + self.object_surface_sample_offset_pos.append( + self.object_assets_dict["samples"][self.object_ids[env_id]]) + self.object_surface_sample_offset_pos = torch.from_numpy( + np.stack(self.object_surface_sample_offset_pos)).to( + self.device).float() + + def add_object_obs(self, object_obs: List) -> int: + obs_idx = 0 + obj_obs = [] + + def add_to_obs(obs_list, key, obs) -> List: + obs_list.append(obs) + if self.cfg["task"]["returnObsDict"]: + self.add_separate_obs(key, obs) + return obs_list + + for obs_key in object_obs: + if obs_key == "objectPos": + obj_obs = add_to_obs(obj_obs, obs_key, self.object_pos) + elif obs_key == "objectLinVel": + obj_obs = add_to_obs(obj_obs, obs_key, self.object_linvel) + elif obs_key == "objectRot": + obj_obs = add_to_obs(obj_obs, obs_key, self.object_rot) + elif obs_key == "objectAngVel": + obj_obs = add_to_obs(obj_obs, obs_key, self.object_angvel) + elif obs_key == "objectBboxBounds": # min, max corners + obj_obs = add_to_obs(obj_obs, obs_key, + self.object_bbox_bounds.flatten(1, 2)) + elif obs_key == "objectBboxCorners": # 8 3D corner points + obj_obs = add_to_obs(obj_obs, obs_key, + self.object_bbox_corners.flatten(1, 2)) + elif obs_key == "objectBboxPose": # Center pose of bounding box + obj_obs = add_to_obs(obj_obs, obs_key, self.object_bbox_pose) + elif obs_key == "objectSurfaceSamples": # Samples on mesh surface + obj_obs = add_to_obs(obj_obs, obs_key, + self.object_surface_samples.flatten(1, 2)) + + obs_idx += self.num_obs_dict[obs_key] + obj_obs = torch.cat(obj_obs, dim=1) + self.obs_buf[:, 0:obs_idx] = obj_obs + return obs_idx + + def reset_idx(self, env_ids): + # Domain randomization would be added here ... + + if self._objects_dropped and not self.cfg["control"]["teleoperated"]: + self._reset_robot(env_ids) + self._reset_objects(env_ids) + + else: + self._reset_robot(env_ids, self.robot_dof_away_pos, initial_sampling=True) + self._drop_objects(env_ids) + self._reset_robot(env_ids, initial_sampling=True) + + # simulate for one time-step to update the tracker position + self.render() + self.gym.simulate(self.sim) + self._refresh_state_tensors() + + self.initial_tracker_pose = self.tracker_pose + self.initial_finger_angles = self.robot_dof_pos[:, self.hand_actuated_dof_indices] + + self.progress_buf[env_ids] = 0 + self.reset_buf[env_ids] = 0 + + def _drop_objects(self, env_ids, skip_steps: int = 250) -> None: + assert torch.equal(env_ids, + to_torch(list(range(self.num_envs)), + device=self.device, dtype=torch.long)), \ + "Objects must be dropped in all environments simultaneously, as " \ + "this is a distinct phase where the physics simulation is " \ + "stepped before the RL agent can actually interact with the task." + + # Position objects above table + self._randomize_object_positions(env_ids) + + # Run simulation + for _ in range(skip_steps): + self.render() + self.gym.simulate(self.sim) + + # Set object state after drop as object init state + self.gym.refresh_actor_root_state_tensor(self.sim) + self.object_init_state = self.root_state_tensor[self.object_indices[env_ids]] + self.object_init_pos = self.object_init_state[:, 0:3] + self.compute_observations() + self._objects_dropped = True + + def _randomize_object_positions(self, env_ids) -> None: + # Generate random values + rand_floats = torch_rand_float( + -1.0, 1.0, (len(env_ids), 5), device=self.device) + + # Reset object position and rotation + self.root_state_tensor[self.object_indices[env_ids]] = \ + self.default_object_init_state[env_ids].clone() + self.root_state_tensor[self.object_indices[env_ids], 0:2] = \ + self.default_object_init_state[env_ids, 0:2] + \ + self.cfg["initState"]["noise"]["objectPos"] * rand_floats[:, 0:2] + self.root_state_tensor[self.object_indices[env_ids], + self.up_axis_idx] = \ + self.default_object_init_state[env_ids, self.up_axis_idx] + \ + self.cfg["initState"]["noise"]["objectPos"] * \ + rand_floats[:, self.up_axis_idx] + new_object_rot = randomize_rotation(rand_floats[:, 3], + rand_floats[:, 4], + self.x_unit_tensor[env_ids], + self.y_unit_tensor[env_ids]) + self.root_state_tensor[self.object_indices[env_ids], 3:7] = \ + new_object_rot + + # Set object velocities to zero + self.root_state_tensor[self.object_indices[env_ids], 7:13] = \ + torch.zeros_like( + self.root_state_tensor[self.object_indices[env_ids], 7:13]) + + # Set root state tensor of the simulation + object_indices = torch.unique( + torch.cat([self.object_indices[env_ids]]).to(torch.int32)) + self.gym.set_actor_root_state_tensor_indexed( + self.sim, gymtorch.unwrap_tensor(self.root_state_tensor), + gymtorch.unwrap_tensor(object_indices), len(object_indices)) + + def _reset_objects(self, env_ids) -> None: + self.root_state_tensor[self.object_indices[env_ids]] = \ + self.object_init_state[env_ids].clone() + + # Set root state tensor of the simulation + object_indices = torch.unique( + torch.cat([self.object_indices[env_ids]]).to(torch.int32)) + self.gym.set_actor_root_state_tensor_indexed( + self.sim, gymtorch.unwrap_tensor(self.root_state_tensor), + gymtorch.unwrap_tensor(object_indices), len(object_indices)) + + def _draw_debug_visualization(self, i: int) -> None: + super()._draw_debug_visualization(i) + if self.cfg["debug"]["drawObjectPose"]: + self._draw_object_pose(i) + if self.cfg["debug"]["drawObjectBbox"]: + self._draw_object_bbox(i) + if self.cfg["debug"]["drawObjectSurfaceSamples"]: + self._draw_object_surface_samples(i) + if self.cfg["debug"]["drawHandObjectDistance"]: + self._draw_hand_object_distance(i) + if self.cfg["debug"]["drawObjectTargetDistance"]: + self._draw_object_target_distance(i) + if self.cfg["debug"]["colorObjectsOnSuccess"]: + self._color_objects_on_success(i) + + def _draw_object_pose(self, i: int) -> None: + self._draw_coordinate_system(self.envs[i], self.object_pos[i], + self.object_rot[i]) + + def _draw_hand_object_distance(self, i: int) -> None: + object_pos = self.object_pos[i].cpu().numpy() + hand_pos = self.hand_pos[i].cpu().numpy() + self.gym.add_lines(self.viewer, self.envs[i], 1, + [object_pos[0], object_pos[1], object_pos[2], + hand_pos[0], hand_pos[1], hand_pos[2]], + [0.85, 0.1, 0.85]) + + def _draw_object_target_distance(self, i: int) -> None: + object_pos = self.object_pos[i].cpu().numpy() + init_object_pos = self.object_init_pos[i].cpu().numpy() + target_pos = object_pos.copy() + target_pos[2] = init_object_pos[2] + self.cfg["reward"]["objectLiftingThreshold"] + self.gym.add_lines(self.viewer, self.envs[i], 1, + [object_pos[0], object_pos[1], object_pos[2], + target_pos[0], target_pos[1], target_pos[2]], + [0.85, 0.85, 0.1]) + + def _color_objects_on_success(self, i: int) -> None: + if self.object_pos[i][2] >= self.object_init_pos[i][2] + \ + self.cfg["reward"]["objectLiftingThreshold"]: + self.gym.set_rigid_body_color(self.envs[i], self.objects[i], 0, + gymapi.MeshType.MESH_VISUAL, + gymapi.Vec3(0.1, 0.95, 0.1)) + + def _draw_object_bbox( + self, + i: int, + draw_bounds: bool = False, + draw_corners: bool = True, + draw_pose: bool = True + ) -> None: + assert any([o.startswith("objectBbox") for o in self.object_obs]), \ + "Cannot draw bounding boxes of objects if it is not included in " \ + "the observations." + + # draw bounding box + relative_bbox = torch.stack([-0.5 * self.object_bbox_extent[i], + 0.5 * self.object_bbox_extent[i]], dim=0) + bbox_pos = self.object_bbox_pose[i, 0:3].cpu().numpy() + bbox_rot = self.object_bbox_pose[i, 3:7].cpu().numpy() + bbox_pose = gymapi.Transform( + p=gymapi.Vec3( + x=bbox_pos[0], y=bbox_pos[1], z=bbox_pos[2]), + r=gymapi.Quat( + x=bbox_rot[0], y=bbox_rot[1], z=bbox_rot[2], w=bbox_rot[3])) + bbox = gymutil.WireframeBBoxGeometry(relative_bbox, pose=bbox_pose) + gymutil.draw_lines(bbox, self.gym, self.viewer, self.envs[i], + pose=gymapi.Transform()) + + if draw_bounds: + bbox_bounds = self.object_bbox_bounds[i].cpu().numpy() + front_lower_left_pose = gymapi.Transform( + p=gymapi.Vec3(x=bbox_bounds[0, 0], + y=bbox_bounds[0, 1], + z=bbox_bounds[0, 2])) + back_upper_right_pose = gymapi.Transform( + p=gymapi.Vec3(x=bbox_bounds[1, 0], + y=bbox_bounds[1, 1], + z=bbox_bounds[1, 2])) + front_lower_left = gymutil.WireframeSphereGeometry( + 0.005, 12, 12, front_lower_left_pose, color=(1, 1, 0)) + back_upper_right = gymutil.WireframeSphereGeometry( + 0.005, 12, 12, back_upper_right_pose, color=(1, 1, 0)) + gymutil.draw_lines(front_lower_left, self.gym, self.viewer, + self.envs[i], + pose=gymapi.Transform()) + gymutil.draw_lines(back_upper_right, self.gym, self.viewer, + self.envs[i], + pose=gymapi.Transform()) + if draw_corners: + bbox_corners = self.object_bbox_corners[i].cpu().numpy() + for j in range(bbox_corners.shape[0]): + corner_pose = gymapi.Transform( + p=gymapi.Vec3(x=bbox_corners[j, 0], + y=bbox_corners[j, 1], + z=bbox_corners[j, 2])) + corner = gymutil.WireframeSphereGeometry( + 0.005, 12, 12, corner_pose, color=(1, 0, 1)) + gymutil.draw_lines(corner, self.gym, self.viewer, self.envs[i], + pose=gymapi.Transform()) + + if draw_pose: + self._draw_coordinate_system(self.envs[i], + self.object_bbox_pose[i, 0:3], + self.object_bbox_pose[i, 3:7]) + + def _draw_object_surface_samples(self, i: int) -> None: + assert "objectSurfaceSamples" in self.object_obs, \ + "Cannot draw surface samples of objects if they are not included " \ + "in the observations." + surface_samples = self.object_surface_samples[i].cpu().numpy() + for j in range(surface_samples.shape[0]): + sample_pose = gymapi.Transform( + p=gymapi.Vec3(x=surface_samples[j, 0], + y=surface_samples[j, 1], + z=surface_samples[j, 2])) + sample_sphere = gymutil.WireframeSphereGeometry( + 0.0025, 12, 12, sample_pose, color=(1, 0, 1)) + gymutil.draw_lines(sample_sphere, self.gym, self.viewer, + self.envs[i], pose=gymapi.Transform()) + + +@torch.jit.script +def compute_object_lifting_reward( + rew_buf, reset_buf, progress_buf, successes, success_rate, + object_pos, object_linvel, object_init_pos, + fingertip_pos, actions, + action_penalty_scale: float, + fingertips_to_object_distance_reward_scale: float, + max_episode_length: int, + object_height_reward_scale: float, + object_velocity_reward_scale: float, + target_height_reward_scale: float, + object_falls_off_table_penalty_scale: float, + lift_off_height: float, + max_xy_drift: float, + target_height: float, + eps_fingertips: float, + eps_height: float, + xy_center, + sparse: bool, + reset_when_object_falls_off_table: bool, + reset_when_object_is_lifted: bool, + return_rewards_dict: bool, +): + + # ============ computing reward ... ============ + # penalize large actions: r ~ -|| a_t ||^2 + squared_action_norm = torch.sum(actions.pow(2), dim=-1) + action_reward = squared_action_norm * action_penalty_scale + + # penalize fingertips-object distance: r ~ (1 / (d(ft, obj)^2 + ϵ_ft))^2 + under_object_center = object_pos.clone() + under_object_center[:, 2] -= 0.0 + under_object_center = under_object_center.unsqueeze(1).repeat(1, fingertip_pos.shape[1], 1) + fingertips_to_object_distance = torch.norm(fingertip_pos - under_object_center, dim=-1) + thumb_scaling = 2 + fingertips_to_object_distance[:, 0] *= thumb_scaling + fingertips_to_object_distance = torch.sum(fingertips_to_object_distance, dim=-1) + fingertips_to_object_distance_reward = \ + 1.0 / (eps_fingertips + 5 * fingertips_to_object_distance.pow(2)) + fingertips_to_object_distance_reward *= fingertips_to_object_distance_reward + fingertips_to_object_distance_reward *= fingertips_to_object_distance_reward_scale + + # continuous height reward: r ~ 1 / (Δh + eps_h) + object_raised_by = object_pos[:, 2] - object_init_pos[:, 2] + delta_height = torch.clamp( + object_raised_by - lift_off_height, max=0) + object_height_reward = (1.0 / (torch.abs(delta_height) + eps_height)) - (1.0 / (lift_off_height + eps_height)) + object_height_reward *= object_height_reward_scale + + # object height is not rewarded far away from center + xy_drift = torch.norm(object_pos[:, 0:2] - xy_center, dim=1) + close_to_center = xy_drift <= max_xy_drift + object_height_reward *= close_to_center.float() + + # reward reaching the target height: r ~ 1(object_lifted == True) + object_lifted = object_raised_by >= target_height + target_height_reward = target_height_reward_scale * object_lifted.float() + + # reward moving the object: r ~ tanh(10 * object_vel) + object_velocity = torch.norm(object_linvel, dim=1) + object_velocity_reward = torch.tanh(10. * object_velocity) + object_velocity_reward *= object_velocity_reward_scale + + # compute final reward + rewards_dict = {} + if sparse: + reward = object_lifted.float() - 1 + if return_rewards_dict: + dense_reward = action_reward + \ + fingertips_to_object_distance_reward + \ + object_height_reward + \ + object_velocity_reward + \ + target_height_reward + rewards_dict["sparse"] = reward + rewards_dict["dense"] = dense_reward + else: + reward = action_reward + \ + fingertips_to_object_distance_reward + \ + object_height_reward + \ + object_velocity_reward + \ + target_height_reward + if return_rewards_dict: + sparse_reward = object_lifted.float() - 1 + rewards_dict["sparse"] = sparse_reward + rewards_dict["dense"] = reward + + # ============ determining resets ... ============ + # reset environments that have reached the maximum number of steps + resets = torch.where(progress_buf >= max_episode_length, + torch.ones_like(reset_buf), reset_buf) + + # reset environments that lifted the object successfully + if reset_when_object_is_lifted: + resets = torch.where(object_lifted, + torch.ones_like(resets), resets) + + # reset environments if object falls of the table + if reset_when_object_falls_off_table: + table_height = 0.8 + resets = torch.where(object_pos[:, 2] < table_height, + torch.ones_like(resets), resets) + object_has_fallen_off_table = object_pos[:, 2] < table_height + reward += object_has_fallen_off_table.float() * object_falls_off_table_penalty_scale + + # determine total number of successful episodes and success rate + total_resets = torch.sum(resets) + avg_factor = 0.01 + successful_resets = torch.sum(object_lifted.float()) + successes += object_lifted.float() + success_rate = torch.where(total_resets > 0, + avg_factor * (successful_resets/total_resets) + + (1. - avg_factor) * success_rate, + success_rate) + + return reward, resets, progress_buf, \ + successes, success_rate, \ + action_reward, \ + fingertips_to_object_distance_reward, \ + object_height_reward, \ + object_velocity_reward, \ + target_height_reward, \ + rewards_dict + + +@torch.jit.script +def randomize_rotation(rand0, rand1, x_unit_tensor, y_unit_tensor): + return quat_mul(quat_from_angle_axis(rand0 * np.pi, x_unit_tensor), + quat_from_angle_axis(rand1 * np.pi, y_unit_tensor)) diff --git a/rofunc/learning/RofuncRL/tasks/isaacgymenv/grasp/open_door.py b/rofunc/learning/RofuncRL/tasks/isaacgymenv/grasp/open_door.py new file mode 100644 index 00000000..a6d174ea --- /dev/null +++ b/rofunc/learning/RofuncRL/tasks/isaacgymenv/grasp/open_door.py @@ -0,0 +1,383 @@ +import os +from torch import Tensor +from isaacgym import gymtorch +from isaacgym import gymapi +from isaacgym.torch_utils import * +import torch +from gym_grasp.tasks.open_drawer import OpenDrawer +from gym_grasp.tasks.base.grasping_task import GraspingTask +from typing import * + + +class OpenDoor(OpenDrawer): + def __init__(self, + cfg: Dict[str, Any], + sim_device: str, + graphics_device_id: int, + headless: bool) -> None: + """Creates UR5e-SIH robot on the task of opening a door. + + Arguments: + cfg: (Dict) Configuration dictionary that contains parameters for + how the simulation should run and defines properties of the + task. + sim_device: (str) Device on which the physics simulation is run + (e.g. "cuda:0" or "cpu"). + graphics_device_id: (int) ID of the device to run the rendering. + headless: (bool) Whether to run the simulation without a viewer. + """ + super().__init__(cfg, sim_device, graphics_device_id, headless) + + def _init_buffers(self) -> None: + super()._init_buffers() + self.door_dof_state = self.dof_state.view( + self.num_envs, -1, 2)[:, self.num_robot_dofs:] + self.door_dof_pos = self.door_dof_state[..., 0] + self.door_dof_vel = self.door_dof_state[..., 1] + + def _load_task_assets(self) -> Dict[str, Any]: + self.doors, self.door_indices = [], [] + door_asset, door_start_pose = self._load_door_asset() + self._set_door_dof_limits(door_asset) + assets_dict = {"door": {}} + assets_dict["door"]["asset"] = door_asset + assets_dict["door"]["start_pose"] = door_start_pose + return assets_dict + + def _set_door_dof_limits(self, door_asset): + self.num_door_dofs = self.gym.get_asset_dof_count(door_asset) + self.door_dof_props = self.gym.get_asset_dof_properties(door_asset) + # Set DOF limits and default positions and velocities + self.door_dof_lower_limits = [] + self.door_dof_upper_limits = [] + self.door_dof_default_vel = [] + for i in range(self.num_door_dofs): + self.door_dof_lower_limits.append(self.door_dof_props["lower"][i]) + self.door_dof_upper_limits.append(self.door_dof_props["upper"][i]) + self.door_dof_default_vel.append(0.0) + + self.door_dof_lower_limits = to_torch(self.door_dof_lower_limits, + device=self.device) + self.door_dof_upper_limits = to_torch(self.door_dof_upper_limits, + device=self.device) + self.door_dof_default_pos = to_torch([0.], + device=self.device) + self.door_dof_default_vel = to_torch(self.door_dof_default_vel, + device=self.device) + + def _load_door_asset(self): + """Loads the door to be opened.""" + asset_options = gymapi.AssetOptions() + asset_options.fix_base_link = True + asset_options.use_mesh_materials = True + + # Enable convex decomposition for handle geometry + asset_options.vhacd_enabled = True + asset_options.vhacd_params = gymapi.VhacdParams() + asset_options.vhacd_params.resolution = 1000000 + + door_asset = self.gym.load_asset( + self.sim, self.asset_root, self.door_asset_file, + asset_options) + + x, y, z = self.cfg["asset"]["doorPosition"] + q_w, q_x, q_y, q_z = self.cfg["asset"]["doorRotation"] + door_start_pose = gymapi.Transform( + p=gymapi.Vec3(x, y, z), r=gymapi.Quat(q_x, q_y, q_z, q_w)) + + self.num_door_bodies = self.gym.get_asset_rigid_body_count( + door_asset) + self.num_door_shapes = self.gym.get_asset_rigid_shape_count( + door_asset) + return door_asset, door_start_pose + + @property + def door_asset_file(self) -> os.path: + door_asset_file = "urdf/door.urdf" + if "asset" in self.cfg: + return os.path.normpath(self.cfg["asset"].get( + "doorAssetFile", door_asset_file)) + return door_asset_file + + def _add_task(self, env_ptr, i, task_asset_dict: Dict[str, Any], + agg_info) -> None: + self._add_door(env_ptr, i, task_asset_dict["door"]["asset"], + task_asset_dict["door"]["start_pose"]) + + def _process_task_handles(self) -> None: + self.door_indices = to_torch( + self.door_indices, dtype=torch.long, device=self.device) + + def _begin_aggregate(self, env_ptr, env_idx: int) -> None: + max_agg_bodies = self.num_robot_bodies + self.num_table_bodies + \ + self.num_door_bodies + max_agg_shapes = self.num_robot_shapes + self.num_table_shapes + \ + self.num_door_shapes + if self.aggregate_mode >= 1: + self.gym.begin_aggregate(env_ptr, max_agg_bodies, + max_agg_shapes, True) + + def _add_door(self, env_ptr, i, door_asset, door_start_pose) -> None: + # create door actor + door_actor = self.gym.create_actor( + env_ptr, door_asset, door_start_pose, 'door', i, 0, 0) + + if "doorColor" in self.cfg["asset"].keys(): + self.gym.set_rigid_body_color( + env_ptr, door_actor, 2, gymapi.MeshType.MESH_VISUAL, + gymapi.Vec3(*self.cfg["asset"]["doorColor"])) + + self.handle_handle = [self.gym.find_asset_rigid_body_index( + door_asset, 'handle') + self.num_robot_bodies + + self.num_table_bodies] + self.handle_handle = to_torch( + self.handle_handle, dtype=torch.long, device=self.device) + + self.door_indices.append(self.gym.get_actor_index( + env_ptr, door_actor, gymapi.DOMAIN_SIM)) + self.doors.append(door_actor) + + def compute_reward(self) -> None: + if self.cfg["control"]["useRelativeControl"]: + actions = self.actions + else: + actions = self.actions - self.prev_actions + self.prev_actions = self.actions + + self.rew_buf[:], self.reset_buf[:], self.progress_buf[:], \ + self.successes[:], self.success_rate[:], \ + action_reward, \ + fingertips_to_handle_distance_reward, \ + door_opening_reward, \ + target_reward, \ + self.rewards_dict, \ + = compute_door_opening_reward( + self.rew_buf, self.reset_buf, self.progress_buf, + self.successes, self.success_rate, + self.handle_pos, self.door_dof_pos, + self.fingertip_pos, actions, + self.cfg["reward"]["scale"]["actionPenalty"], + self.cfg["reward"]["scale"]["fingertipsToHandleDistanceReward"], + self.max_episode_length, + self.cfg["reward"]["scale"]["doorOpeningReward"], + self.cfg["reward"]["scale"]["targetReward"], + self.cfg["reward"]["targetOpening"], + self.cfg["reward"]["epsFingertips"], + self.cfg["reward"]["sparse"], + self.cfg["reset"]["doorOpened"], + self.cfg["reward"]["returnRewardsDict"] + ) + + if self.cfg["debug"]["verbose"]: + self.writer.add_scalar( + "action_reward", + torch.mean(action_reward), self.env_steps) + self.writer.add_scalar( + "fingertips_to_handle_distance_reward", + torch.mean(fingertips_to_handle_distance_reward), self.env_steps) + self.writer.add_scalar( + "door_opening_reward", + torch.mean(door_opening_reward), self.env_steps) + self.writer.add_scalar( + "target_reward", + torch.mean(target_reward), self.env_steps) + + self.writer.add_scalar("successes", torch.sum(self.successes), + self.env_steps) + self.writer.add_scalar("success_rate", self.success_rate, + self.env_steps) + + def compute_observations(self): + """Updates the state tensors and adds the desired information to the + observation buffer.""" + self._refresh_state_tensors() + self._process_state_tensors() + + obs_idx = 0 + for obs in self.obs_types: + if obs == "fingertipContactForces": + self.add_fingertip_contact_forces(obs_idx) + elif obs == "fingertipPos": + self.add_fingertip_pos(obs_idx) + elif obs == "handDofPos": + self.add_hand_dof_pos(obs_idx) + elif obs == "handPose": + self.add_hand_pose(obs_idx) + elif obs == "jointPos": + self.add_joint_pos(obs_idx) + elif obs == "jointVel": + self.add_joint_vel(obs_idx) + elif obs == "previousAction": + self.add_previous_action(obs_idx) + elif obs == "handlePos": + self.add_handle_pos(obs_idx) + elif obs == "handlePose": + self.add_handle_pose(obs_idx) + obs_idx += self.num_obs_dict[obs] + + def _process_state_tensors(self) -> None: + GraspingTask._process_state_tensors(self) + + self.handle_state = self.rigid_body_states[:, self.handle_handle][ + :, 0, 0:13] + self.handle_rigid_body_pos = self.handle_state[:, 0:3] + self.handle_rot = self.handle_state[:, 3:7] + self.handle_pos = self.calculate_handle_pos() + + def calculate_handle_pos(self) -> Tensor: + handle_pos = self.handle_rigid_body_pos.clone() + handle_pos += quat_apply( + self.handle_rot, torch.Tensor( + [[0.05, 0.055, 0]]).repeat(self.num_envs, 1).to(self.device)) + return handle_pos + + def add_handle_pose(self, start_idx: int) -> None: + self.obs_buf[:, start_idx:start_idx + 3] = self.handle_pos + self.obs_buf[:, start_idx + 3: start_idx + 7] = self.handle_rot + if self.cfg["task"]["returnObsDict"]: + self.add_separate_obs("handlePose", torch.cat([self.handle_pos, self.handle_rot], dim=1)) + + def reset_idx(self, env_ids): + # Domain randomization would be added here ... + + if not self.initial_reset and not self.cfg["control"]["teleoperated"]: + self._reset_robot(env_ids, apply_dof_resets=False) + self._reset_door(env_ids) + # Reset DOF state + robot_indices = self.robot_indices[env_ids].to(torch.int32) + door_indices = self.door_indices[env_ids].to(torch.int32) + dof_indices = torch.cat([robot_indices, door_indices]) + self.gym.set_dof_state_tensor_indexed( + self.sim, gymtorch.unwrap_tensor(self.dof_state), + gymtorch.unwrap_tensor(dof_indices), len(dof_indices)) + else: + self._reset_robot(env_ids, apply_dof_resets=False, + initial_sampling=True) + self._reset_door(env_ids) + # Reset DOF state + robot_indices = self.robot_indices[env_ids].to(torch.int32) + door_indices = self.door_indices[env_ids].to(torch.int32) + dof_indices = torch.cat([robot_indices, door_indices]) + self.gym.set_dof_state_tensor_indexed( + self.sim, gymtorch.unwrap_tensor(self.dof_state), + gymtorch.unwrap_tensor(dof_indices), len(dof_indices)) + # simulate for one time-step to update the tracker position + self.render() + self.gym.simulate(self.sim) + self._refresh_state_tensors() + self.initial_tracker_pose = self.tracker_pose + self.initial_finger_angles = self.robot_dof_pos[:, + self.hand_actuated_dof_indices] + + self.progress_buf[env_ids] = 0 + self.reset_buf[env_ids] = 0 + + def _reset_door(self, env_ids): + rand_floats = torch_rand_float( + 0.0, 1.0, (len(env_ids), self.num_door_dofs), + device=self.device) + + delta_max = self.door_dof_upper_limits - self.door_dof_default_pos + delta_min = self.door_dof_lower_limits - self.door_dof_default_pos + rand_delta = delta_min + (delta_max - delta_min) * rand_floats + pos = self.door_dof_default_pos + \ + self.cfg["initState"]["noise"]["doorDofPos"] * rand_delta + + self.door_dof_pos[env_ids, :] = pos + self.door_dof_vel[env_ids, :] = self.door_dof_default_vel + + def _draw_handle_pos(self, i: int) -> None: + self._draw_coordinate_system( + self.envs[i], self.handle_pos[i], self.handle_rot[i], + axis_length=0.5) + + +@torch.jit.script +def compute_door_opening_reward( + rew_buf, reset_buf, progress_buf, successes, success_rate, + handle_pos, door_dof_pos, + fingertip_pos, actions, + action_penalty_scale: float, + fingertips_to_handle_distance_reward_scale: float, + max_episode_length: int, + door_opening_reward_scale: float, + target_reward_scale: float, + target_opening: float, + eps_fingertips: float, + sparse: bool, + reset_when_door_is_opened: bool, + return_rewards_dict: bool, +): + + # ============ computing reward ... ============ + # penalize large actions: r ~ -|| a_t ||^2 + squared_action_norm = torch.sum(actions.pow(2), dim=-1) + action_reward = squared_action_norm * action_penalty_scale + + # penalize fingertips-handle distance: r ~ (1 / (d(ft, handle)^2 + ϵ_ft))^2 + handle_center = handle_pos.unsqueeze(1).repeat(1, fingertip_pos.shape[1], 1) + fingertips_to_handle_distance = torch.norm(fingertip_pos - handle_center, dim=-1) + thumb_scaling = 2 + fingertips_to_handle_distance[:, 0] *= thumb_scaling + fingertips_to_handle_distance = torch.sum(fingertips_to_handle_distance, dim=-1) + fingertips_to_handle_distance_reward = \ + 1.0 / (eps_fingertips + 5 * fingertips_to_handle_distance.pow(2)) + fingertips_to_handle_distance_reward *= fingertips_to_handle_distance_reward + fingertips_to_handle_distance_reward *= fingertips_to_handle_distance_reward_scale + + # continuous door-opening reward: r ~ Δd + door_opened_by = door_dof_pos[:, 0] + door_opening_reward = door_opening_reward_scale * door_opened_by + + # reward reaching the target door opening: r ~ 1(door_opened == True) + door_opened = door_opened_by >= target_opening + target_reward = target_reward_scale * door_opened.float() + + # compute final reward + rewards_dict = {} + if sparse: + reward = door_opened.float() - 1 + if return_rewards_dict: + dense_reward = action_reward + \ + fingertips_to_handle_distance_reward + \ + door_opening_reward + \ + target_reward + rewards_dict["sparse"] = reward + rewards_dict["dense"] = dense_reward + else: + reward = action_reward + \ + fingertips_to_handle_distance_reward + \ + door_opening_reward + \ + target_reward + if return_rewards_dict: + sparse_reward = door_opened.float() - 1 + rewards_dict["sparse"] = sparse_reward + rewards_dict["dense"] = reward + + # ============ determining resets ... ============ + # reset environments that have reached the maximum number of steps + resets = torch.where(progress_buf >= max_episode_length, + torch.ones_like(reset_buf), reset_buf) + + # reset environments that opened the door successfully + if reset_when_door_is_opened: + resets = torch.where(door_opened, + torch.ones_like(resets), resets) + + # determine total number of successful episodes and success rate + total_resets = torch.sum(resets) + avg_factor = 0.01 + successful_resets = torch.sum(door_opened.float()) + successes += door_opened.float() + success_rate = torch.where(total_resets > 0, + avg_factor * (successful_resets/total_resets) + + (1. - avg_factor) * success_rate, + success_rate) + + return reward, resets, progress_buf, \ + successes, success_rate, \ + action_reward, \ + fingertips_to_handle_distance_reward, \ + door_opening_reward, \ + target_reward, \ + rewards_dict diff --git a/rofunc/learning/RofuncRL/tasks/isaacgymenv/grasp/open_drawer.py b/rofunc/learning/RofuncRL/tasks/isaacgymenv/grasp/open_drawer.py new file mode 100644 index 00000000..cfec4e58 --- /dev/null +++ b/rofunc/learning/RofuncRL/tasks/isaacgymenv/grasp/open_drawer.py @@ -0,0 +1,424 @@ +import os +from torch import Tensor +from isaacgym import gymtorch +from isaacgym import gymapi +from isaacgym.torch_utils import * +import torch +from gym_grasp.tasks.base.grasping_task import GraspingTask +from typing import * + + +class OpenDrawer(GraspingTask): + def __init__(self, + cfg: Dict[str, Any], + sim_device: str, + graphics_device_id: int, + headless: bool) -> None: + """Creates UR5e-SIH robot on the task of opening a drawer. + + Arguments: + cfg: (Dict) Configuration dictionary that contains parameters for + how the simulation should run and defines properties of the + task. + sim_device: (str) Device on which the physics simulation is run + (e.g. "cuda:0" or "cpu"). + graphics_device_id: (int) ID of the device to run the rendering. + headless: (bool) Whether to run the simulation without a viewer. + """ + super().__init__(cfg, sim_device, graphics_device_id, headless) + + def _parse_cfg(self, cfg: Dict[str, Any]) -> None: + super()._parse_cfg(cfg) + self.obs_types = self._parse_observation_type( + cfg["task"]["observationType"], cfg["task"]["num_observations"]) + self.cfg["env"]["numObservations"] = self.num_observations + + def _parse_observation_type(self, observation_type, + num_observations) -> List: + if isinstance(observation_type, str): + observation_type = [observation_type] + + self.num_observations = 0 + observations = [] + for obs in observation_type: + if not (obs in num_observations.keys()): + raise ValueError(f"Unknown observation type '{obs}' given. " + f"Should be in {num_observations.keys()}.") + observations.append(obs) + self.num_observations += num_observations[obs] + self.num_obs_dict = num_observations + + if "fingertipContactForces" in observation_type: + self.cfg["sim"]["useContactForces"] = True + return observations + + def _init_buffers(self) -> None: + super()._init_buffers() + self.drawer_dof_state = self.dof_state.view( + self.num_envs, -1, 2)[:, self.num_robot_dofs:] + self.drawer_dof_pos = self.drawer_dof_state[..., 0] + self.drawer_dof_vel = self.drawer_dof_state[..., 1] + + def _load_task_assets(self) -> Dict[str, Any]: + self.drawers, self.drawer_indices = [], [] + drawer_asset, drawer_start_pose = self._load_drawer_asset() + self._set_drawer_dof_limits(drawer_asset) + assets_dict = {"drawer": {}} + assets_dict["drawer"]["asset"] = drawer_asset + assets_dict["drawer"]["start_pose"] = drawer_start_pose + return assets_dict + + def _set_drawer_dof_limits(self, drawer_asset): + self.num_drawer_dofs = self.gym.get_asset_dof_count(drawer_asset) + self.drawer_dof_props = self.gym.get_asset_dof_properties(drawer_asset) + # Set DOF limits and default positions and velocities + self.drawer_dof_lower_limits = [] + self.drawer_dof_upper_limits = [] + self.drawer_dof_default_vel = [] + for i in range(self.num_drawer_dofs): + self.drawer_dof_lower_limits.append(self.drawer_dof_props["lower"][i]) + self.drawer_dof_upper_limits.append(self.drawer_dof_props["upper"][i]) + self.drawer_dof_default_vel.append(0.0) + + self.drawer_dof_lower_limits = to_torch(self.drawer_dof_lower_limits, + device=self.device) + self.drawer_dof_upper_limits = to_torch(self.drawer_dof_upper_limits, + device=self.device) + self.drawer_dof_default_pos = to_torch([0.], + device=self.device) + self.drawer_dof_default_vel = to_torch(self.drawer_dof_default_vel, + device=self.device) + + def _load_drawer_asset(self): + """Loads the drawer to be opened.""" + asset_options = gymapi.AssetOptions() + asset_options.fix_base_link = True + asset_options.use_mesh_materials = True + + # Enable convex decomposition for handle geometry + asset_options.vhacd_enabled = True + asset_options.vhacd_params = gymapi.VhacdParams() + asset_options.vhacd_params.resolution = 1000000 + + drawer_asset = self.gym.load_asset( + self.sim, self.asset_root, self.drawer_asset_file, + asset_options) + + x, y, z = self.cfg["asset"]["drawerPosition"] + q_w, q_x, q_y, q_z = self.cfg["asset"]["drawerRotation"] + drawer_start_pose = gymapi.Transform( + p=gymapi.Vec3(x, y, z), r=gymapi.Quat(q_x, q_y, q_z, q_w)) + + self.num_drawer_bodies = self.gym.get_asset_rigid_body_count( + drawer_asset) + self.num_drawer_shapes = self.gym.get_asset_rigid_shape_count( + drawer_asset) + return drawer_asset, drawer_start_pose + + @property + def drawer_asset_file(self) -> os.path: + drawer_asset_file = "urdf/drawer.urdf" + if "asset" in self.cfg: + return os.path.normpath(self.cfg["asset"].get( + "drawerAssetFile", drawer_asset_file)) + return drawer_asset_file + + def _add_task(self, env_ptr, i, task_asset_dict: Dict[str, Any], + agg_info) -> None: + self._add_drawer(env_ptr, i, task_asset_dict["drawer"]["asset"], + task_asset_dict["drawer"]["start_pose"]) + + def _process_task_handles(self) -> None: + self.drawer_indices = to_torch( + self.drawer_indices, dtype=torch.long, device=self.device) + + def _begin_aggregate(self, env_ptr, env_idx: int) -> None: + max_agg_bodies = self.num_robot_bodies + self.num_table_bodies + \ + self.num_drawer_bodies + max_agg_shapes = self.num_robot_shapes + self.num_table_shapes + \ + self.num_drawer_shapes + if self.aggregate_mode >= 1: + self.gym.begin_aggregate(env_ptr, max_agg_bodies, + max_agg_shapes, True) + + def _add_drawer(self, env_ptr, i, drawer_asset, drawer_start_pose) -> None: + # create drawer actor + drawer_actor = self.gym.create_actor( + env_ptr, drawer_asset, drawer_start_pose, 'drawer', i, 0, 0) + + if "drawerColor" in self.cfg["asset"].keys(): + self.gym.set_rigid_body_color( + env_ptr, drawer_actor, 2, gymapi.MeshType.MESH_VISUAL, + gymapi.Vec3(*self.cfg["asset"]["drawerColor"])) + + self.handle_handle = [self.gym.find_asset_rigid_body_index( + drawer_asset, 'handle') + self.num_robot_bodies + + self.num_table_bodies] + self.handle_handle = to_torch( + self.handle_handle, dtype=torch.long, device=self.device) + + self.drawer_indices.append(self.gym.get_actor_index( + env_ptr, drawer_actor, gymapi.DOMAIN_SIM)) + self.drawers.append(drawer_actor) + + def compute_reward(self) -> None: + if self.cfg["control"]["useRelativeControl"]: + actions = self.actions + else: + actions = self.actions - self.prev_actions + self.prev_actions = self.actions + + self.rew_buf[:], self.reset_buf[:], self.progress_buf[:], \ + self.successes[:], self.success_rate[:], \ + action_reward, \ + fingertips_to_handle_distance_reward, \ + drawer_opening_reward, \ + target_reward, \ + self.rewards_dict, \ + = compute_drawer_opening_reward( + self.rew_buf, self.reset_buf, self.progress_buf, + self.successes, self.success_rate, + self.handle_pos, + to_torch(self.cfg["reward"]["handleDefaultPos"], + device=self.device), + self.fingertip_pos, actions, + self.cfg["reward"]["scale"]["actionPenalty"], + self.cfg["reward"]["scale"]["fingertipsToHandleDistanceReward"], + self.max_episode_length, + self.cfg["reward"]["scale"]["drawerOpeningReward"], + self.cfg["reward"]["scale"]["targetReward"], + self.cfg["reward"]["targetOpening"], + self.cfg["reward"]["epsFingertips"], + self.cfg["reward"]["sparse"], + self.cfg["reset"]["drawerOpened"], + self.cfg["reward"]["returnRewardsDict"] + ) + + if self.cfg["debug"]["verbose"]: + self.writer.add_scalar( + "action_reward", + torch.mean(action_reward), self.env_steps) + self.writer.add_scalar( + "fingertips_to_handle_distance_reward", + torch.mean(fingertips_to_handle_distance_reward), self.env_steps) + self.writer.add_scalar( + "drawer_opening_reward", + torch.mean(drawer_opening_reward), self.env_steps) + self.writer.add_scalar( + "target_reward", + torch.mean(target_reward), self.env_steps) + + self.writer.add_scalar("successes", torch.sum(self.successes), + self.env_steps) + self.writer.add_scalar("success_rate", self.success_rate, + self.env_steps) + + def compute_observations(self): + """Updates the state tensors and adds the desired information to the + observation buffer.""" + self._refresh_state_tensors() + self._process_state_tensors() + + obs_idx = 0 + for obs in self.obs_types: + if obs == "fingertipContactForces": + self.add_fingertip_contact_forces(obs_idx) + elif obs == "fingertipPos": + self.add_fingertip_pos(obs_idx) + elif obs == "handPose": + self.add_hand_pose(obs_idx) + elif obs == "handDofPos": + self.add_hand_dof_pos(obs_idx) + elif obs == "jointPos": + self.add_joint_pos(obs_idx) + elif obs == "jointVel": + self.add_joint_vel(obs_idx) + elif obs == "previousAction": + self.add_previous_action(obs_idx) + elif obs == "handlePos": + self.add_handle_pos(obs_idx) + elif obs == "drawerOpening": + self.add_drawer_opening(obs_idx) + + obs_idx += self.num_obs_dict[obs] + + def _process_state_tensors(self) -> None: + super()._process_state_tensors() + + self.handle_state = self.rigid_body_states[:, self.handle_handle][ + :, :, 0:13] + self.handle_rigid_body_pos = self.handle_state[:, 0, 0:3] + self.handle_pos = self.calculate_handle_pos() + + def calculate_handle_pos(self) -> Tensor: + handle_pos = self.handle_rigid_body_pos.clone() + handle_pos += to_torch([[0.175, 0, 0.9]], device=self.device + ).repeat(self.num_envs, 1) + return handle_pos + + def add_handle_pos(self, start_idx: int) -> None: + self.obs_buf[:, start_idx:start_idx + 3] = self.handle_pos + if self.cfg["task"]["returnObsDict"]: + self.add_separate_obs("handlePos", self.handle_pos) + + def add_drawer_opening(self, start_idx: int) -> None: + # 1-dimensional version of handle pos observation + + self.obs_buf[:, start_idx:start_idx + 1] = self.handle_pos[:, 1:2] + if self.cfg["task"]["returnObsDict"]: + self.add_separate_obs("drawerOpening", self.handle_pos[:, 1:2]) + + def reset_idx(self, env_ids): + # Domain randomization would be added here ... + + if not self.initial_reset and not self.cfg["control"]["teleoperated"]: + self._reset_robot(env_ids, apply_dof_resets=False) + self._reset_drawer(env_ids) + # Reset DOF state + robot_indices = self.robot_indices[env_ids].to(torch.int32) + drawer_indices = self.drawer_indices[env_ids].to(torch.int32) + dof_indices = torch.cat([robot_indices, drawer_indices]) + self.gym.set_dof_state_tensor_indexed( + self.sim, gymtorch.unwrap_tensor(self.dof_state), + gymtorch.unwrap_tensor(dof_indices), len(dof_indices)) + else: + self._reset_robot(env_ids, apply_dof_resets=False, + initial_sampling=True) + self._reset_drawer(env_ids) + # Reset DOF state + robot_indices = self.robot_indices[env_ids].to(torch.int32) + drawer_indices = self.drawer_indices[env_ids].to(torch.int32) + dof_indices = torch.cat([robot_indices, drawer_indices]) + self.gym.set_dof_state_tensor_indexed( + self.sim, gymtorch.unwrap_tensor(self.dof_state), + gymtorch.unwrap_tensor(dof_indices), len(dof_indices)) + + # simulate for one time-step to update the tracker position + self.render() + self.gym.simulate(self.sim) + self._refresh_state_tensors() + self.initial_tracker_pose = self.tracker_pose + self.initial_finger_angles = self.robot_dof_pos[:, + self.hand_actuated_dof_indices] + + self.progress_buf[env_ids] = 0 + self.reset_buf[env_ids] = 0 + + def _reset_drawer(self, env_ids): + rand_floats = torch_rand_float( + 0.0, 1.0, (len(env_ids), self.num_drawer_dofs), + device=self.device) + + delta_max = self.drawer_dof_upper_limits - self.drawer_dof_default_pos + delta_min = self.drawer_dof_lower_limits - self.drawer_dof_default_pos + rand_delta = delta_min + (delta_max - delta_min) * rand_floats + pos = self.drawer_dof_default_pos + \ + self.cfg["initState"]["noise"]["drawerDofPos"] * rand_delta + + self.drawer_dof_pos[env_ids, :] = pos + self.drawer_dof_vel[env_ids, :] = self.drawer_dof_default_vel + + drawer_indices = self.drawer_indices[env_ids].to(torch.int32) + self.gym.set_actor_root_state_tensor_indexed( + self.sim, gymtorch.unwrap_tensor(self.root_state_tensor), + gymtorch.unwrap_tensor(drawer_indices), len(drawer_indices)) + + def _draw_debug_visualization(self, i: int) -> None: + super()._draw_debug_visualization(i) + if self.cfg["debug"]["drawHandlePos"]: + self._draw_handle_pos(i) + + def _draw_handle_pos(self, i: int) -> None: + self._draw_coordinate_system(self.envs[i], self.handle_pos[i], + to_torch([0, 0, 0, 1], device=self.device), axis_length=0.5) + + +@torch.jit.script +def compute_drawer_opening_reward( + rew_buf, reset_buf, progress_buf, successes, success_rate, + drawer_pos, handle_default_pos, + fingertip_pos, actions, + action_penalty_scale: float, + fingertips_to_handle_distance_reward_scale: float, + max_episode_length: int, + drawer_opening_reward_scale: float, + target_reward_scale: float, + target_opening: float, + eps_fingertips: float, + sparse: bool, + reset_when_drawer_is_opened: bool, + return_rewards_dict: bool, +): + + # ============ computing reward ... ============ + # penalize large actions: r ~ -|| a_t ||^2 + squared_action_norm = torch.sum(actions.pow(2), dim=-1) + action_reward = squared_action_norm * action_penalty_scale + + # penalize fingertips-handle distance: r ~ (1 / (d(ft, handle)^2 + ϵ_ft))^2 + handle_center = drawer_pos.unsqueeze(1).repeat(1, fingertip_pos.shape[1], 1) + fingertips_to_handle_distance = torch.norm(fingertip_pos - handle_center, dim=-1) + thumb_scaling = 2 + fingertips_to_handle_distance[:, 0] *= thumb_scaling + fingertips_to_handle_distance = torch.sum(fingertips_to_handle_distance, dim=-1) + fingertips_to_handle_distance_reward = \ + 1.0 / (eps_fingertips + 5 * fingertips_to_handle_distance.pow(2)) + fingertips_to_handle_distance_reward *= fingertips_to_handle_distance_reward + fingertips_to_handle_distance_reward *= fingertips_to_handle_distance_reward_scale + + # continuous drawer-opening reward: r ~ Δd + drawer_opened_by = -(drawer_pos[:, 1] - handle_default_pos[1]) + drawer_opening_reward = drawer_opening_reward_scale * drawer_opened_by + + # reward reaching the target drawer opening: r ~ 1(drawer_opened == True) + drawer_opened = drawer_opened_by >= target_opening + target_reward = target_reward_scale * drawer_opened.float() + + # compute final reward + rewards_dict = {} + if sparse: + reward = drawer_opened.float() - 1 + if return_rewards_dict: + dense_reward = action_reward + \ + fingertips_to_handle_distance_reward + \ + drawer_opening_reward + \ + target_reward + rewards_dict["sparse"] = reward + rewards_dict["dense"] = dense_reward + else: + reward = action_reward + \ + fingertips_to_handle_distance_reward + \ + drawer_opening_reward + \ + target_reward + if return_rewards_dict: + sparse_reward = drawer_opened.float() - 1 + rewards_dict["sparse"] = sparse_reward + rewards_dict["dense"] = reward + + # ============ determining resets ... ============ + # reset environments that have reached the maximum number of steps + resets = torch.where(progress_buf >= max_episode_length, + torch.ones_like(reset_buf), reset_buf) + + # reset environments that opened the drawer successfully + if reset_when_drawer_is_opened: + resets = torch.where(drawer_opened, + torch.ones_like(resets), resets) + + # determine total number of successful episodes and success rate + total_resets = torch.sum(resets) + avg_factor = 0.01 + successful_resets = torch.sum(drawer_opened.float()) + successes += drawer_opened.float() + success_rate = torch.where(total_resets > 0, + avg_factor * (successful_resets/total_resets) + + (1. - avg_factor) * success_rate, + success_rate) + + return reward, resets, progress_buf, \ + successes, success_rate, \ + action_reward, \ + fingertips_to_handle_distance_reward, \ + drawer_opening_reward, \ + target_reward, \ + rewards_dict diff --git a/rofunc/learning/RofuncRL/tasks/isaacgymenv/grasp/pick_from_bin.py b/rofunc/learning/RofuncRL/tasks/isaacgymenv/grasp/pick_from_bin.py new file mode 100644 index 00000000..de5894ff --- /dev/null +++ b/rofunc/learning/RofuncRL/tasks/isaacgymenv/grasp/pick_from_bin.py @@ -0,0 +1,341 @@ +#import os + +from isaacgym import gymtorch +from isaacgym import gymapi +from isaacgym.torch_utils import * + +from typing import * +import random +from .empty_bin import EmptyBin +from .lift_object import compute_object_lifting_reward + + +class PickFromBin(EmptyBin): + def __init__(self, + cfg: Dict[str, Any], + sim_device: str, + graphics_device_id: int, + headless: bool) -> None: + """Creates UR5e-SIH robot on the task of picking a specific object from + a bin filled with various objects. The agent is only rewarded for + lifting the goal object, which is shown above each environment. + + Arguments: + cfg: (Dict) Configuration dictionary that contains parameters for + how the simulation should run and defines properties of the + task. + sim_device: (str) Device on which the physics simulation is run + (e.g. "cuda:0" or "cpu"). + graphics_device_id: (int) ID of the device to run the rendering. + headless: (bool) Whether to run the simulation without a viewer. + """ + super().__init__(cfg, sim_device, graphics_device_id, headless) + + def _load_task_assets(self) -> Dict[str, Any]: + self.objects_in_each_env = [] + self.goals = [[] for _ in range(self.num_envs)] + self.goal_indices = torch.zeros( + [self.num_envs, self.cfg["task"]["numObjectsPerBin"]], + dtype=torch.long, device=self.device) + self.goal_object_numbers = torch.zeros(self.num_envs, + device=self.device, + dtype=torch.long) + + assets_dict = super()._load_task_assets() + assets_dict["goal"] = {} + goal_assets, goal_start_poses = self._load_goal_assets() + assets_dict["goal"]["asset"] = goal_assets + assets_dict["goal"]["start_pose"] = goal_start_poses + return assets_dict + + def _add_task(self, env_ptr, i, task_asset_dict: Dict[str, Any], + agg_info) -> None: + self.objects_in_each_env.append(agg_info) + super()._add_task(env_ptr, i, task_asset_dict, agg_info) + self._add_goals(env_ptr, i, task_asset_dict["goal"]["asset"], + agg_info) + + def _process_task_handles(self) -> None: + super()._process_task_handles() + self.objects_in_each_env = to_torch( + self.objects_in_each_env, dtype=torch.long, device=self.device) + + x, y, z = self.cfg["asset"]["goalObjectPosition"] + self.active_goal_state = to_torch( + [[x, y, z, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0]], device=self.device) + + self.passive_goal_state = to_torch( + [[x, y, -1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0]], device=self.device) + self.object_identifiers = torch.arange( + self.cfg["task"]["numObjectsPerBin"]).unsqueeze(0).repeat( + self.num_envs, 1).to(self.device).unsqueeze(2) + + def _load_goal_assets(self): + """Loads visualizations for the target objects to be picked.""" + object_asset_options = gymapi.AssetOptions() + object_asset_options.disable_gravity = True + + goal_assets, goal_start_poses = [], [] + + for ycb_object in self.ycb_objects: + goal_asset_file = os.path.join( + self.ycb_object_asset_root + "/visual", + os.path.normpath(ycb_object + ".urdf")) + goal_start_poses.append(gymapi.Transform( + p=gymapi.Vec3(0, 0, -1))) + goal_assets.append(self.gym.load_asset( + self.sim, self.asset_root, goal_asset_file, + object_asset_options)) + for egad_object in self.egad_objects: + goal_asset_file = os.path.join( + self.egad_object_asset_root + "/visual", + os.path.normpath(egad_object + ".urdf")) + goal_start_poses.append(gymapi.Transform( + p=gymapi.Vec3(0, 0, -1))) + goal_assets.append(self.gym.load_asset( + self.sim, self.asset_root, goal_asset_file, + object_asset_options)) + return goal_assets, goal_start_poses + + def _add_goals(self, env_ptr, i, goal_assets, object_indices) -> None: + for j, object_idx in enumerate(object_indices): + goal_start_pose = gymapi.Transform(p=gymapi.Vec3(0, 0, -1)) + goal_actor = self.gym.create_actor(env_ptr, goal_assets[object_idx], + goal_start_pose, + self.objects_list[object_idx] + "_goal", + -1, 0, 0) + + # Set color of EGAD goals + if self.objects_list[object_idx] in self.egad_objects: + self.gym.set_rigid_body_color( + env_ptr, goal_actor, 0, gymapi.MeshType.MESH_VISUAL, + gymapi.Vec3(*self.egad_colors[ + self.objects_list[object_idx]])) + + goal_idx = self.gym.get_actor_index(env_ptr, goal_actor, + gymapi.DOMAIN_SIM) + self.goal_indices[i, j] = goal_idx + self.goals[i].append(goal_actor) + + def _begin_aggregate(self, env_ptr, env_idx: int) -> List: + possible_objects = list(range(self.num_different_objects)) + object_indices = random.sample(possible_objects, + self.cfg["task"]["numObjectsPerBin"]) + + # Add numObjectsPerBin bodies for the goals in each env + max_agg_bodies = self.num_robot_bodies + self.num_table_bodies + \ + + self.num_bin_bodies + \ + self.cfg["task"]["numObjectsPerBin"] + + max_agg_shapes = self.num_robot_shapes + self.num_table_shapes + \ + self.num_bin_shapes + + for object_idx in object_indices: + max_agg_bodies += self.num_object_bodies[object_idx] + max_agg_shapes += self.num_object_shapes[object_idx] + + if self.aggregate_mode >= 1: + self.gym.begin_aggregate(env_ptr, max_agg_bodies, + max_agg_shapes, True) + return object_indices + + def _reset_objects(self, env_ids) -> None: + self._reset_goals(env_ids, set_root_state_tensor=False) + self.root_state_tensor[self.object_indices[env_ids].flatten()] = self.object_init_state[env_ids].view(-1, 13) + indices = torch.unique( + torch.cat([self.object_indices[env_ids].flatten(), self.goal_indices[env_ids].flatten()]).to(torch.int32)) + self.gym.set_actor_root_state_tensor_indexed( + self.sim, gymtorch.unwrap_tensor(self.root_state_tensor), + gymtorch.unwrap_tensor(indices), len(indices)) + + def _end_of_drop_objects(self, env_ids) -> None: + self._reset_goals(env_ids) + + def _drop_objects_into_done_bin(self, env_ids, skip_steps) -> None: + pass + + def _reset_goals(self, env_ids, set_root_state_tensor=True) -> None: + object_numbers = torch.randint(self.cfg["task"]["numObjectsPerBin"], + (len(env_ids),)).to(self.device) + self.goal_object_numbers[env_ids] = object_numbers + goal_mask = torch.nn.functional.one_hot( + object_numbers, num_classes=self.cfg["task"]["numObjectsPerBin"] + ).to(self.device).bool() + + goal_object_indices = torch.masked_select(self.objects_in_each_env[env_ids], goal_mask) + active_goals = torch.masked_select(self.goal_indices[env_ids], goal_mask) + passive_goals = torch.masked_select(self.goal_indices[env_ids], ~goal_mask) + + self.root_state_tensor[active_goals] = self.active_goal_state.repeat(len(active_goals), 1) + self.root_state_tensor[passive_goals] = self.passive_goal_state.repeat(len(passive_goals), 1) + + if self.cfg["debug"]["verbose"] and not self.headless: + for i, num in enumerate(goal_object_indices): + print(f"Pick {self.objects_list[int(num)]} from Env {i}.") + + if set_root_state_tensor: + indices = torch.unique( + torch.cat([self.goal_indices[env_ids].flatten()]).to( + torch.int32)) + self.gym.set_actor_root_state_tensor_indexed( + self.sim, gymtorch.unwrap_tensor(self.root_state_tensor), + gymtorch.unwrap_tensor(indices), len(indices)) + self.gym.refresh_actor_root_state_tensor(self.sim) + + def compute_observations(self): + """Updates the state tensors and adds the desired information to the + observation buffer.""" + self._refresh_state_tensors() + self._process_state_tensors() + + obs_idx = self.add_object_obs(self.object_obs) + + for obs in self.robot_obs: + if obs == "fingertipContactForces": + self.add_fingertip_contact_forces(obs_idx) + elif obs == "fingertipPos": + self.add_fingertip_pos(obs_idx) + elif obs == "goalIdentifier": + self.add_goal_identifier(obs_idx) + elif obs == "handDofPos": + self.add_hand_dof_pos(obs_idx) + elif obs == "handPose": + self.add_hand_pose(obs_idx) + elif obs == "jointPos": + self.add_joint_pos(obs_idx) + elif obs == "jointVel": + self.add_joint_vel(obs_idx) + elif obs == "previousAction": + self.add_previous_action(obs_idx) + obs_idx += self.num_obs_dict[obs] + + def add_goal_identifier(self, start_idx: int) -> None: + self.obs_buf[:, start_idx] = self.goal_object_numbers + if self.cfg["task"]["returnObsDict"]: + self.add_separate_obs("goalIdentifier", self.goal_object_numbers) + + def add_object_obs(self, object_obs: List) -> int: + obs_idx = 0 + obj_obs = [] + + def add_to_obs(obs_list, key, obs) -> List: + obs_list.append(obs) + if self.cfg["task"]["returnObsDict"]: + self.add_separate_obs(key, obs) + return obs_list + + for obs_key in object_obs: + if obs_key == "objectPos": + obj_obs = add_to_obs(obj_obs, obs_key, self.object_pos) + elif obs_key == "objectLinVel": + obj_obs = add_to_obs(obj_obs, obs_key, self.object_linvel) + elif obs_key == "objectRot": + obj_obs = add_to_obs(obj_obs, obs_key, self.object_rot) + elif obs_key == "objectAngVel": + obj_obs = add_to_obs(obj_obs, obs_key, self.object_angvel) + elif obs_key == "objectGoalOneHot": + goal_one_hot = torch.nn.functional.one_hot( + self.goal_object_numbers, + num_classes=self.cfg["task"]["numObjectsPerBin"] + ).unsqueeze(2).to(self.device) + obj_obs = add_to_obs(obj_obs, obs_key, goal_one_hot) + elif obs_key == "objectIdentifier": + obj_obs = add_to_obs(obj_obs, obs_key, self.object_identifiers) + elif obs_key == "objectBboxBounds": # min, max corners + obj_obs = add_to_obs(obj_obs, obs_key, + self.object_bbox_bounds.flatten(2, 3)) + elif obs_key == "objectBboxCorners": # 8 3D corner points + obj_obs = add_to_obs(obj_obs, obs_key, + self.object_bbox_corners.flatten(2, 3)) + elif obs_key == "objectBboxPose": # Center pose of bounding box + obj_obs = add_to_obs(obj_obs, obs_key, self.object_bbox_pose) + elif obs_key == "objectSurfaceSamples": # Samples on mesh surface + obj_obs = add_to_obs(obj_obs, obs_key, + self.object_surface_samples.flatten(2, 3)) + + obs_idx += self.num_obs_dict[obs_key] + obj_obs = torch.cat(obj_obs, dim=2) + self.obs_buf[:, 0:obs_idx] = obj_obs.view(self.num_envs, -1) + return obs_idx + + def compute_reward(self) -> None: + self.goal_object_pos = self.object_pos.gather( + 1, self.goal_object_numbers.unsqueeze( + 1).unsqueeze(2).repeat(1, 1, 3)).squeeze(1) + self.goal_object_linvel = self.object_linvel.gather( + 1, self.goal_object_numbers.unsqueeze( + 1).unsqueeze(2).repeat(1, 1, 3)).squeeze(1) + self.goal_object_init_pos = self.object_init_pos.gather( + 1, self.goal_object_numbers.unsqueeze( + 1).unsqueeze(2).repeat(1, 1, 3)).squeeze(1) + + if self.cfg["control"]["useRelativeControl"]: + actions = self.actions + else: + actions = self.actions - self.prev_actions + self.prev_actions = self.actions + + self.rew_buf[:], self.reset_buf[:], self.progress_buf[:], \ + self.successes[:], self.success_rate[:], \ + action_reward, \ + fingertips_to_object_distance_reward, \ + object_height_reward, \ + object_velocity_reward, \ + target_height_reward, \ + self.rewards_dict, \ + = compute_object_lifting_reward( + self.rew_buf, self.reset_buf, self.progress_buf, + self.successes, self.success_rate, + self.goal_object_pos, self.goal_object_linvel, + self.goal_object_init_pos, + self.fingertip_pos, actions, + self.cfg["reward"]["scale"]["actionPenalty"], + self.cfg["reward"]["scale"]["fingertipsToObjectDistanceReward"], + self.max_episode_length, + self.cfg["reward"]["scale"]["objectHeightReward"], + self.cfg["reward"]["scale"]["objectVelocityReward"], + self.cfg["reward"]["scale"]["targetHeightReward"], + self.cfg["reward"]["scale"]["objectFallsOffTablePenalty"], + self.cfg["reward"]["liftOffHeight"], + self.cfg["reward"]["maxXyDrift"], + self.cfg["reward"]["targetHeight"], + self.cfg["reward"]["epsFingertips"], + self.cfg["reward"]["epsHeight"], + torch.Tensor(self.cfg["reward"]["xyCenter"]).to( + self.device).unsqueeze(0).repeat(self.num_envs, 1), + self.cfg["reward"]["sparse"], + self.cfg["reset"]["objectFallsOffTable"], + self.cfg["reset"]["objectLifted"], + self.cfg["reward"]["returnRewardsDict"] + ) + + if self.cfg["debug"]["verbose"]: + self.writer.add_scalar( + "action_reward", + torch.mean(action_reward), self.env_steps) + self.writer.add_scalar( + "fingertips_to_object_distance_reward", + torch.mean(fingertips_to_object_distance_reward), + self.env_steps) + self.writer.add_scalar( + "object_height_reward", + torch.mean(object_height_reward), self.env_steps) + self.writer.add_scalar( + "object_velocity_reward", + torch.mean(object_velocity_reward), self.env_steps) + self.writer.add_scalar( + "target_height_reward", + torch.mean(target_height_reward), self.env_steps) + + self.writer.add_scalar("successes", torch.sum(self.successes), + self.env_steps) + self.writer.add_scalar("success_rate", self.success_rate, + self.env_steps) + + def _draw_hand_object_distance(self, i: int) -> None: + hand_pos = self.hand_pos[i].cpu().numpy() + object_pos = self.goal_object_pos[i].cpu().numpy() + self.gym.add_lines(self.viewer, self.envs[i], 1, + [object_pos[0], object_pos[1], object_pos[2], + hand_pos[0], hand_pos[1], hand_pos[2]], + [0.85, 0.1, 0.85]) diff --git a/rofunc/learning/RofuncRL/tasks/isaacgymenv/grasp/pour_cup.py b/rofunc/learning/RofuncRL/tasks/isaacgymenv/grasp/pour_cup.py new file mode 100644 index 00000000..e6eea888 --- /dev/null +++ b/rofunc/learning/RofuncRL/tasks/isaacgymenv/grasp/pour_cup.py @@ -0,0 +1,558 @@ +import os +from isaacgym import gymtorch +from isaacgym import gymapi +from isaacgym.torch_utils import * +import torch +from gym_grasp.tasks.base.grasping_task import GraspingTask +from typing import * +import math + + +class PourCup(GraspingTask): + def __init__(self, + cfg: Dict[str, Any], + sim_device: str, + graphics_device_id: int, + headless: bool) -> None: + """Creates UR5e-SIH robot on the task of pouring from a cup. + + Arguments: + cfg: (Dict) Configuration dictionary that contains parameters for + how the simulation should run and defines properties of the + task. + sim_device: (str) Device on which the physics simulation is run + (e.g. "cuda:0" or "cpu"). + graphics_device_id: (int) ID of the device to run the rendering. + headless: (bool) Whether to run the simulation without a viewer. + """ + super().__init__(cfg, sim_device, graphics_device_id, headless) + + def _parse_cfg(self, cfg: Dict[str, Any]) -> None: + super()._parse_cfg(cfg) + self.obs_types = self._parse_observation_type( + cfg["task"]["observationType"], cfg["task"]["num_observations"]) + self.cfg["env"]["numObservations"] = self.num_observations + + def _init_buffers(self) -> None: + super()._init_buffers() + self.balls_poured = torch.zeros(self.num_envs, + self.cfg["asset"]["numBalls"], + dtype=torch.bool, device=self.device) + self.num_balls_poured = torch.zeros(self.num_envs, dtype=torch.int, + device=self.device) + self.full_cup_in_empty_cup = torch.zeros(self.num_envs, + dtype=torch.bool, + device=self.device) + + def _parse_observation_type(self, observation_type, + num_observations) -> List: + if isinstance(observation_type, str): + observation_type = [observation_type] + + self.num_observations = 0 + observations = [] + for obs in observation_type: + if not (obs in num_observations.keys()): + raise ValueError(f"Unknown observation type '{obs}' given. " + f"Should be in {num_observations.keys()}.") + observations.append(obs) + self.num_observations += num_observations[obs] + self.num_obs_dict = num_observations + + if "fingertipContactForces" in observation_type: + self.cfg["sim"]["useContactForces"] = True + return observations + + def _load_task_assets(self) -> Dict[str, Any]: + self.full_cups, self.empty_cups = [], [] + self.full_cup_indices, self.empty_cup_indices = [], [] + self.full_cup_init_state = [] + self.balls = [[] for _ in range(self.num_envs)] + self.ball_init_state = [] + self.ball_indices = torch.zeros( + [self.num_envs, self.cfg["asset"]["numBalls"]], + dtype=torch.long, device=self.device) + + full_cup_asset, empty_cup_asset, full_cup_start_pose, \ + empty_cup_start_pose = self._load_cup_assets() + ball_asset, ball_start_poses = self._load_ball_assets() + assets_dict = {"full_cup": {}, "empty_cup": {}, "ball": {}} + assets_dict["full_cup"]["asset"] = full_cup_asset + assets_dict["full_cup"]["start_pose"] = full_cup_start_pose + assets_dict["empty_cup"]["asset"] = empty_cup_asset + assets_dict["empty_cup"]["start_pose"] = empty_cup_start_pose + assets_dict["ball"]["asset"] = ball_asset + assets_dict["ball"]["start_pose"] = ball_start_poses + return assets_dict + + def _add_task(self, env_ptr, i, task_asset_dict: Dict[str, Any], + agg_info) -> None: + self._add_cups(env_ptr, i, task_asset_dict["full_cup"]["asset"], + task_asset_dict["empty_cup"]["asset"], + task_asset_dict["full_cup"]["start_pose"], + task_asset_dict["empty_cup"]["start_pose"]) + self._add_balls(env_ptr, i, task_asset_dict["ball"]["asset"], + task_asset_dict["ball"]["start_pose"]) + + def _process_task_handles(self) -> None: + self.ball_init_state = to_torch( + self.ball_init_state, device=self.device, dtype=torch.float + ).view(self.num_envs, self.cfg["asset"]["numBalls"], 13) + self.full_cup_init_state = to_torch( + self.full_cup_init_state, device=self.device, dtype=torch.float + ).view(self.num_envs, 13) + self.full_cup_indices = to_torch( + self.full_cup_indices, dtype=torch.long, device=self.device) + self.empty_cup_indices = to_torch( + self.empty_cup_indices, dtype=torch.long, device=self.device) + + def _load_cup_assets(self): + """Loads the cups.""" + empty_cup_asset_options = gymapi.AssetOptions() + empty_cup_asset_options.fix_base_link = True + empty_cup_asset_options.use_mesh_materials = True + empty_cup_asset_options.vhacd_enabled = True + empty_cup_asset_options.vhacd_params = gymapi.VhacdParams() + empty_cup_asset_options.vhacd_params.resolution = 1000000 + empty_cup_asset = self.gym.load_asset( + self.sim, self.asset_root, self.empty_cup_asset_file, + empty_cup_asset_options) + full_cup_asset_options = gymapi.AssetOptions() + full_cup_asset_options.fix_base_link = False + full_cup_asset_options.use_mesh_materials = True + full_cup_asset_options.vhacd_enabled = True + full_cup_asset_options.vhacd_params = gymapi.VhacdParams() + full_cup_asset_options.vhacd_params.resolution = 1000000 + full_cup_asset = self.gym.load_asset( + self.sim, self.asset_root, self.full_cup_asset_file, + full_cup_asset_options) + + x, y, z = self.cfg["asset"]["emptyCupPosition"] + q_w, q_x, q_y, q_z = self.cfg["asset"]["emptyCupRotation"] + empty_cup_start_pose = gymapi.Transform( + p=gymapi.Vec3(x, y, z), r=gymapi.Quat(q_x, q_y, q_z, q_w)) + + x, y, z = self.cfg["asset"]["fullCupPosition"] + q_w, q_x, q_y, q_z = self.cfg["asset"]["fullCupRotation"] + full_cup_start_pose = gymapi.Transform( + p=gymapi.Vec3(x, y, z), r=gymapi.Quat(q_x, q_y, q_z, q_w)) + + self.num_cups_bodies = self.gym.get_asset_rigid_body_count( + empty_cup_asset) + self.gym.get_asset_rigid_body_count( + full_cup_asset) + self.num_cups_shapes = self.gym.get_asset_rigid_shape_count( + empty_cup_asset) + self.gym.get_asset_rigid_shape_count( + full_cup_asset) + + self.empty_cup_pos = to_torch( + self.cfg["asset"]["emptyCupPosition"], device=self.device + ).unsqueeze(0).repeat(self.num_envs, 1) + return full_cup_asset, empty_cup_asset, full_cup_start_pose, \ + empty_cup_start_pose + + def _load_ball_assets(self): + ball_asset_options = gymapi.AssetOptions() + ball_asset_options.fix_base_link = False + + ball_asset = self.gym.create_sphere( + self.sim, self.cfg["asset"]["ballRadius"], ball_asset_options) + + ball_start_poses = [] + ball_init_room = 0.015 + ls_extent = int(math.ceil(self.cfg["asset"]["numBalls"] ** (1 / 3))) + linspace = torch.linspace(-ball_init_room, ball_init_room, ls_extent) + + x_mid, y_mid, z_mid = self.cfg["asset"]["fullCupPosition"] + for i, x in enumerate(linspace): + for j, y in enumerate(linspace): + for k, z in enumerate(linspace): + ball_idx = k + ls_extent * j + (ls_extent ** 2) * i + if ball_idx < self.cfg["asset"]["numBalls"]: + ball_start_poses.append( + gymapi.Transform( + p=gymapi.Vec3(x_mid + x, y_mid + y, z_mid + z)) + ) + + self.num_ball_bodies = self.cfg["asset"]["numBalls"] * \ + self.gym.get_asset_rigid_body_count(ball_asset) + self.num_ball_shapes = self.cfg["asset"]["numBalls"] * \ + self.gym.get_asset_rigid_shape_count(ball_asset) + + return ball_asset, ball_start_poses + + @property + def full_cup_asset_file(self) -> os.path: + full_cup_asset_file = "urdf/ycb/025_mug.urdf" + if "asset" in self.cfg: + return os.path.normpath(self.cfg["asset"].get( + "fullCupAssetFile", full_cup_asset_file)) + return full_cup_asset_file + + @property + def empty_cup_asset_file(self) -> os.path: + empty_cup_asset_file = "urdf/ycb/024_bowl.urdf" + if "asset" in self.cfg: + return os.path.normpath(self.cfg["asset"].get( + "emptyCupAssetFile", empty_cup_asset_file)) + return empty_cup_asset_file + + def _begin_aggregate(self, env_ptr, env_idx: int) -> None: + max_agg_bodies = self.num_robot_bodies + self.num_table_bodies + \ + self.num_cups_bodies + self.num_ball_bodies + max_agg_shapes = self.num_robot_shapes + self.num_table_shapes + \ + self.num_cups_shapes + self.num_ball_shapes + if self.aggregate_mode >= 1: + self.gym.begin_aggregate(env_ptr, max_agg_bodies, + max_agg_shapes, True) + + def _add_cups(self, env_ptr, i, full_cup_asset, empty_cup_asset, + full_cup_start_pose, empty_cup_start_pose) -> None: + # create cup actors + full_cup_actor = self.gym.create_actor( + env_ptr, full_cup_asset, full_cup_start_pose, 'full_cup', i, 0, 0) + empty_cup_actor = self.gym.create_actor( + env_ptr, empty_cup_asset, empty_cup_start_pose, 'empty_cup', i, 0, + 0) + + self.full_cup_indices.append(self.gym.get_actor_index( + env_ptr, full_cup_actor, gymapi.DOMAIN_SIM)) + self.empty_cup_indices.append(self.gym.get_actor_index( + env_ptr, empty_cup_actor, gymapi.DOMAIN_SIM)) + + self.full_cup_init_state.append( + [full_cup_start_pose.p.x, + full_cup_start_pose.p.y, + full_cup_start_pose.p.z, + full_cup_start_pose.r.x, + full_cup_start_pose.r.y, + full_cup_start_pose.r.z, + full_cup_start_pose.r.w, + 0, 0, 0, 0, 0, 0]) + + self.full_cups.append(full_cup_actor) + self.empty_cups.append(empty_cup_actor) + + def _add_balls(self, env_ptr, i, ball_asset, ball_start_poses) -> None: + # create ball actors + for j in range(self.cfg["asset"]["numBalls"]): + ball_actor = self.gym.create_actor( + env_ptr, ball_asset, ball_start_poses[j], "ball_" + str(j)) + + self.ball_init_state.append( + [ball_start_poses[j].p.x, + ball_start_poses[j].p.y, + ball_start_poses[j].p.z, + ball_start_poses[j].r.x, + ball_start_poses[j].r.y, + ball_start_poses[j].r.z, + ball_start_poses[j].r.w, + 0, 0, 0, 0, 0, 0]) + + ball_idx = self.gym.get_actor_index(env_ptr, ball_actor, + gymapi.DOMAIN_SIM) + self.ball_indices[i, j] = ball_idx + self.balls[i].append(ball_actor) + + def compute_reward(self) -> None: + if self.cfg["control"]["useRelativeControl"]: + actions = self.actions + else: + actions = self.actions - self.prev_actions + self.prev_actions = self.actions + + self.rew_buf[:], self.reset_buf[:], self.progress_buf[:], \ + self.successes[:], self.success_rate[:], \ + self.balls_poured[:], self.num_balls_poured[:], \ + self.full_cup_in_empty_cup[:], \ + action_reward, \ + fingertips_to_full_cup_distance_reward, \ + pouring_reward, \ + self.rewards_dict, \ + = compute_cup_pouring_reward( + self.rew_buf, self.reset_buf, self.progress_buf, + self.successes, self.success_rate, + self.full_cup_pos, self.empty_cup_pos, self.ball_pos, + self.fingertip_pos, actions, + self.cfg["reward"]["scale"]["actionPenalty"], + self.cfg["reward"]["scale"]["fingertipsToFullCupDistanceReward"], + self.max_episode_length, + self.cfg["reward"]["scale"]["pouringReward"] / self.cfg["asset"]["numBalls"], + self.cfg["reward"]["scale"]["pouredSuccessfully"], + self.cfg["reward"]["epsFingertips"], + self.cfg["reward"]["sparse"], + self.cfg["reset"]["cupPoured"], + self.cfg["reward"]["returnRewardsDict"] + ) + + if self.cfg["debug"]["verbose"]: + self.writer.add_scalar( + "action_reward", + torch.mean(action_reward), self.env_steps) + self.writer.add_scalar( + "fingertips_to_full_cup_distance_reward", + torch.mean(fingertips_to_full_cup_distance_reward), + self.env_steps) + self.writer.add_scalar( + "pouring_reward", + torch.mean(pouring_reward), self.env_steps) + + self.writer.add_scalar("successes", torch.sum(self.successes), + self.env_steps) + self.writer.add_scalar("success_rate", self.success_rate, + self.env_steps) + + def compute_observations(self): + """Updates the state tensors and adds the desired information to the + observation buffer.""" + self._refresh_state_tensors() + self._process_state_tensors() + + obs_idx = 0 + for obs in self.obs_types: + if obs == "fingertipContactForces": + self.add_fingertip_contact_forces(obs_idx) + elif obs == "fingertipPos": + self.add_fingertip_pos(obs_idx) + elif obs == "handDofPos": + self.add_hand_dof_pos(obs_idx) + elif obs == "handPose": + self.add_hand_pose(obs_idx) + elif obs == "jointPos": + self.add_joint_pos(obs_idx) + elif obs == "jointVel": + self.add_joint_vel(obs_idx) + elif obs == "previousAction": + self.add_previous_action(obs_idx) + elif obs == "fullCupPose": + self.add_full_cup_pose(obs_idx) + elif obs == "emptyCupPose": + self.add_empty_cup_pose(obs_idx) + obs_idx += self.num_obs_dict[obs] + + def _process_state_tensors(self) -> None: + self.fingertip_state = self.rigid_body_states[ + :, self.fingertip_handles][:, :, 0:13] + self.fingertip_rigid_body_pos = self.rigid_body_states[ + :, self.fingertip_handles][:, :, 0:3] + self.fingertip_rot = self.rigid_body_states[ + :, self.fingertip_handles][:, :, 3:7] + self.fingertip_pos = self.calculate_fingertip_pos() + + self.full_cup_pos = self.root_state_tensor[self.full_cup_indices, 0:3] + self.full_cup_rot = self.root_state_tensor[self.full_cup_indices, 3:7] + self.empty_cup_pos = self.root_state_tensor[self.empty_cup_indices, 0:3] + self.empty_cup_rot = self.root_state_tensor[self.empty_cup_indices, 3:7] + + self.ball_pos = self.root_state_tensor[self.ball_indices, 0:3].view( + self.num_envs, self.cfg["asset"]["numBalls"], 3) + self.ball_rot = self.root_state_tensor[self.ball_indices, 3:7].view( + self.num_envs, self.cfg["asset"]["numBalls"], 4) + + def add_full_cup_pose(self, start_idx: int) -> None: + self.obs_buf[:, start_idx:start_idx + 3] = self.full_cup_pos + self.obs_buf[:, start_idx + 3:start_idx + 7] = self.full_cup_rot + if self.cfg["task"]["returnObsDict"]: + self.add_separate_obs("fullCupPose", torch.cat([self.full_cup_pos, self.full_cup_rot], dim=1)) + + def add_empty_cup_pose(self, start_idx: int) -> None: + self.obs_buf[:, start_idx:start_idx + 3] = self.empty_cup_pos + self.obs_buf[:, start_idx + 3:start_idx + 7] = self.empty_cup_rot + if self.cfg["task"]["returnObsDict"]: + self.add_separate_obs("emptyCupPose", torch.cat([self.empty_cup_pos, self.empty_cup_rot], dim=1)) + + def reset_idx(self, env_ids): + # Domain randomization would be added here ... + if not self.initial_reset and not self.cfg["control"]["teleoperated"]: + self._reset_robot(env_ids) + else: + self._reset_robot(env_ids, initial_sampling=True) + # simulate for one time-step to update the tracker position + self.render() + self.gym.simulate(self.sim) + self._refresh_state_tensors() + + self.initial_tracker_pose = self.tracker_pose + self.initial_finger_angles = self.robot_dof_pos[:, + self.hand_actuated_dof_indices] + self._reset_full_cup(env_ids) + + self.progress_buf[env_ids] = 0 + self.reset_buf[env_ids] = 0 + + def _reset_full_cup(self, env_ids): + # Reset cup itself + self.root_state_tensor[self.full_cup_indices[env_ids]] = \ + self.full_cup_init_state[env_ids].clone() + + # Reset balls + self.root_state_tensor[self.ball_indices[env_ids].flatten()] = \ + self.ball_init_state[env_ids].view(-1, 13) + + # Set root state tensor of the simulation + indices = torch.unique( + torch.cat([self.full_cup_indices[env_ids], + self.ball_indices[env_ids].flatten()]).to(torch.int32)) + self.gym.set_actor_root_state_tensor_indexed( + self.sim, gymtorch.unwrap_tensor(self.root_state_tensor), + gymtorch.unwrap_tensor(indices), len(indices)) + + def _draw_debug_visualization(self, i: int) -> None: + super()._draw_debug_visualization(i) + if self.cfg["debug"]["drawFullCupPose"]: + self._draw_full_cup_pose(i) + if self.cfg["debug"]["drawEmptyCupPose"]: + self._draw_empty_cup_pose(i) + if self.cfg["debug"]["drawBallPose"]: + self._draw_ball_pose(i) + if self.cfg["debug"]["colorBallsOnSuccess"]: + self._color_balls_on_success(i) + if self.cfg["debug"]["colorFullCupInEmptyCup"]: + self._color_full_cup_in_empty_cup(i) + + def _draw_full_cup_pose(self, i: int) -> None: + self._draw_coordinate_system(self.envs[i], self.full_cup_pos[i], + self.full_cup_rot[i]) + + def _draw_empty_cup_pose(self, i: int) -> None: + self._draw_coordinate_system(self.envs[i], self.empty_cup_pos[i], + self.empty_cup_rot[i]) + + def _draw_ball_pose(self, i: int) -> None: + for j in range(self.cfg["asset"]["numBalls"]): + self._draw_coordinate_system(self.envs[i], self.ball_pos[i, j], + self.ball_rot[i, j], axis_length=0.05) + + def _color_balls_on_success(self, i: int) -> None: + for j in range(self.cfg["asset"]["numBalls"]): + if self.balls_poured[i, j]: + self.gym.set_rigid_body_color(self.envs[i], self.balls[i][j], 0, + gymapi.MeshType.MESH_VISUAL, + gymapi.Vec3(0.1, 0.7, 0.1)) + else: + self.gym.set_rigid_body_color(self.envs[i], self.balls[i][j], 0, + gymapi.MeshType.MESH_VISUAL, + gymapi.Vec3(0.9, 0.1, 0.1)) + + def _color_full_cup_in_empty_cup(self, i: int) -> None: + if self.full_cup_in_empty_cup[i]: + self.gym.set_rigid_body_color(self.envs[i], self.full_cups[i], + 0, gymapi.MeshType.MESH_VISUAL, + gymapi.Vec3(0.9, 0.1, 0.1)) + else: + self.gym.set_rigid_body_color(self.envs[i], self.full_cups[i], + 0, gymapi.MeshType.MESH_VISUAL, + gymapi.Vec3(0.1, 0.6, 0.1)) + + +@torch.jit.script +def compute_cup_pouring_reward( + rew_buf, reset_buf, progress_buf, successes, success_rate, + full_cup_pos, empty_cup_pos, ball_pos, + fingertip_pos, actions, + action_penalty_scale: float, + fingertips_to_full_cup_distance_reward_scale: float, + max_episode_length: int, + pouring_reward_scale: float, + poured_successfully_reward_scale: float, + eps_fingertips: float, + sparse: bool, + reset_when_cup_is_poured: bool, + return_rewards_dict: bool, +): + + # ============ computing reward ... ============ + # penalize large actions: r ~ -|| a_t ||^2 + squared_action_norm = torch.sum(actions.pow(2), dim=-1) + action_reward = squared_action_norm * action_penalty_scale + + # penalize fingertips-cup distance: r ~ (1 / (d(ft, handle)^2 + ϵ_ft))^2 + cup_center = full_cup_pos.unsqueeze(1).repeat(1, fingertip_pos.shape[1], 1) + fingertips_to_cup_distance = torch.norm(fingertip_pos - cup_center, dim=-1) + thumb_scaling = 2 + fingertips_to_cup_distance[:, 0] *= thumb_scaling + fingertips_to_cup_distance = torch.sum(fingertips_to_cup_distance, dim=-1) + fingertips_to_full_cup_distance_reward = \ + 1.0 / (eps_fingertips + 5 * fingertips_to_cup_distance.pow(2)) + fingertips_to_full_cup_distance_reward *= fingertips_to_full_cup_distance_reward + fingertips_to_full_cup_distance_reward *= fingertips_to_full_cup_distance_reward_scale + + # count balls poured into empty cup + target_pos = empty_cup_pos.unsqueeze(1).repeat(1, ball_pos.shape[1], 1) + balls_to_target_xy_dist = torch.norm( + ball_pos[:, :, 0:2] - target_pos[:, :, 0:2], dim=2) + balls_to_target_z_dist = torch.norm( + ball_pos[:, :, 2:] - target_pos[:, :, 2:], dim=2) + balls_poured = torch.logical_and(balls_to_target_xy_dist < 0.05, + balls_to_target_z_dist < 0.02) + num_balls_poured = torch.sum(balls_poured, dim=1) + + # check whether full cup is placed inside empty cup + full_cup_to_empty_cup_xy_dist = torch.norm( + full_cup_pos[:, 0:2] - empty_cup_pos[:, 0:2], dim=1) + full_cup_to_empty_cup_z_dist = torch.norm( + full_cup_pos[:, 2:] - empty_cup_pos[:, 2:], dim=1) + + full_cup_in_empty_cup = torch.logical_and( + full_cup_to_empty_cup_xy_dist < 0.05, + full_cup_to_empty_cup_z_dist < 0.03) + + # do not count balls poured when full cup is placed in empty cup + num_balls_poured *= 1 - full_cup_in_empty_cup.long() + + min_success_rate = 0.9 + poured_successfully = (num_balls_poured / ball_pos.shape[ + 1]) > min_success_rate + + # pouring reward: r ~ num_balls_in_target_cup + pouring_reward = num_balls_poured * pouring_reward_scale + + # task completion reward: r ~ 1(poured_successfully == True) + poured_successfully_reward = poured_successfully_reward_scale * poured_successfully.float() + + # compute final reward + rewards_dict = {} + if sparse: + reward = poured_successfully.float() - 1 + if return_rewards_dict: + dense_reward = action_reward + \ + fingertips_to_full_cup_distance_reward + \ + pouring_reward + poured_successfully_reward + rewards_dict["sparse"] = reward + rewards_dict["dense"] = dense_reward + else: + reward = action_reward + \ + fingertips_to_full_cup_distance_reward + \ + pouring_reward + poured_successfully_reward + if return_rewards_dict: + sparse_reward = poured_successfully.float() - 1 + rewards_dict["sparse"] = sparse_reward + rewards_dict["dense"] = reward + + # ============ determining resets ... ============ + # reset environments that have reached the maximum number of steps + resets = torch.where(progress_buf >= max_episode_length, + torch.ones_like(reset_buf), reset_buf) + + # reset environments that emptied the cup + if reset_when_cup_is_poured: + resets = torch.where(poured_successfully, + torch.ones_like(resets), resets) + + # determine total number of successful episodes and success rate + total_resets = torch.sum(resets) + avg_factor = 0.01 + successful_resets = torch.sum(poured_successfully.float()) + successes += poured_successfully.float() + success_rate = torch.where(total_resets > 0, + avg_factor * (successful_resets/total_resets) + + (1. - avg_factor) * success_rate, + success_rate) + + return reward, resets, progress_buf, \ + successes, success_rate, \ + balls_poured, num_balls_poured, \ + full_cup_in_empty_cup, \ + action_reward, \ + fingertips_to_full_cup_distance_reward, \ + pouring_reward, \ + rewards_dict diff --git a/rofunc/learning/RofuncRL/tasks/isaacgymenv/grasp/user_study.py b/rofunc/learning/RofuncRL/tasks/isaacgymenv/grasp/user_study.py new file mode 100644 index 00000000..88714183 --- /dev/null +++ b/rofunc/learning/RofuncRL/tasks/isaacgymenv/grasp/user_study.py @@ -0,0 +1,239 @@ +from isaacgym import gymtorch +from isaacgym import gymapi +from isaacgym.torch_utils import * +from gym_grasp.tasks.pour_cup import PourCup +from typing import * + + +class UserStudy(PourCup): + def __init__(self, + cfg: Dict[str, Any], + sim_device: str, + graphics_device_id: int, + headless: bool) -> None: + """Creates UR5e-SIH robot on a group of user study tasks. + + Arguments: + cfg: (Dict) Configuration dictionary that contains parameters for + how the simulation should run and defines properties of the + task. + sim_device: (str) Device on which the physics simulation is run + (e.g. "cuda:0" or "cpu"). + graphics_device_id: (int) ID of the device to run the rendering. + headless: (bool) Whether to run the simulation without a viewer. + """ + super().__init__(cfg, sim_device, graphics_device_id, headless) + + def _load_task_assets(self) -> Dict[str, Any]: + # Load pour cups assets + assets_dict = super()._load_task_assets() + + self.red_cubes, self.green_cubes, self.blue_cubes = [], [], [] + self.red_cube_indices, self.green_cube_indices, self.blue_cube_indices = [], [], [] + self.red_cube_init_state, self.green_cube_init_state, self.blue_cube_init_state = [], [], [] + + red_cube_asset, red_cube_start_pose = self._load_cube_asset("red") + green_cube_asset, green_cube_start_pose = self._load_cube_asset("green") + blue_cube_asset, blue_cube_start_pose = self._load_cube_asset("blue") + corrugated_plate_asset, corrugated_plate_start_pose = self._load_corrugated_plate_asset() + + assets_dict.update({"red_cube": {}, "green_cube": {}, "blue_cube": {}, + "corrugated_plate": {}}) + assets_dict["red_cube"]["asset"] = red_cube_asset + assets_dict["red_cube"]["start_pose"] = red_cube_start_pose + assets_dict["green_cube"]["asset"] = green_cube_asset + assets_dict["green_cube"]["start_pose"] = green_cube_start_pose + assets_dict["blue_cube"]["asset"] = blue_cube_asset + assets_dict["blue_cube"]["start_pose"] = blue_cube_start_pose + assets_dict["corrugated_plate"]["asset"] = corrugated_plate_asset + assets_dict["corrugated_plate"]["start_pose"] = corrugated_plate_start_pose + return assets_dict + + def _load_cube_asset(self, color: str): + cube_asset_options = gymapi.AssetOptions() + cube_asset_options.fix_base_link = False + cube_asset = self.gym.create_box( + self.sim, self.cfg["asset"]["cubeSize"], + self.cfg["asset"]["cubeSize"], self.cfg["asset"]["cubeSize"], + cube_asset_options) + + if color == "red": + x, y, z = self.cfg["asset"]["redCubePosition"] + elif color == "green": + x, y, z = self.cfg["asset"]["greenCubePosition"] + elif color == "blue": + x, y, z = self.cfg["asset"]["blueCubePosition"] + else: + assert False + cube_start_pose = gymapi.Transform(p=gymapi.Vec3(x, y, z)) + return cube_asset, cube_start_pose + + def _load_corrugated_plate_asset(self): + asset_options = gymapi.AssetOptions() + asset_options.fix_base_link = True + + corrugated_plate_asset = self.gym.load_asset( + self.sim, self.asset_root, + self.cfg["asset"]["corrugatedPlateAssetFile"], asset_options) + + x, y, z = self.cfg["asset"]["corrugatedPlatePosition"] + q_w, q_x, q_y, q_z = self.cfg["asset"]["corrugatedPlateRotation"] + corrugated_plate_start_pose = gymapi.Transform( + p=gymapi.Vec3(x, y, z), r=gymapi.Quat(q_x, q_y, q_z, q_w)) + return corrugated_plate_asset, corrugated_plate_start_pose + + def _add_task(self, env_ptr, i, task_asset_dict: Dict[str, Any], + agg_info) -> None: + super()._add_task(env_ptr, i, task_asset_dict, agg_info) + + self._add_cubes(env_ptr, i, + task_asset_dict["red_cube"]["asset"], + task_asset_dict["green_cube"]["asset"], + task_asset_dict["blue_cube"]["asset"], + task_asset_dict["red_cube"]["start_pose"], + task_asset_dict["green_cube"]["start_pose"], + task_asset_dict["blue_cube"]["start_pose"]) + + corrugated_plate_actor = self.gym.create_actor( + env_ptr, task_asset_dict["corrugated_plate"]["asset"], + task_asset_dict["corrugated_plate"]["start_pose"], + 'corrugated_plate', i, 0, 0) + + def _add_cubes(self, env_ptr, i, red_cube_asset, green_cube_asset, + blue_cube_asset, red_cube_start_pose, green_cube_start_pose, + blue_cube_start_pose) -> None: + # create cube actors + red_cube_actor = self.gym.create_actor( + env_ptr, red_cube_asset, red_cube_start_pose, 'red_cube', i, 0, 0) + green_cube_actor = self.gym.create_actor( + env_ptr, green_cube_asset, green_cube_start_pose, 'green_cube', i, 0, 0) + blue_cube_actor = self.gym.create_actor( + env_ptr, blue_cube_asset, blue_cube_start_pose, 'blue_cube', i, 0, 0) + + self.gym.set_rigid_body_color(env_ptr, red_cube_actor, 0, + gymapi.MeshType.MESH_VISUAL, + gymapi.Vec3(0.7, 0.1, 0.1)) + self.gym.set_rigid_body_color(env_ptr, green_cube_actor, 0, + gymapi.MeshType.MESH_VISUAL, + gymapi.Vec3(0.1, 0.7, 0.1)) + self.gym.set_rigid_body_color(env_ptr, blue_cube_actor, 0, + gymapi.MeshType.MESH_VISUAL, + gymapi.Vec3(0.1, 0.1, 0.7)) + + self.red_cube_indices.append(self.gym.get_actor_index( + env_ptr, red_cube_actor, gymapi.DOMAIN_SIM)) + self.green_cube_indices.append(self.gym.get_actor_index( + env_ptr, green_cube_actor, gymapi.DOMAIN_SIM)) + self.blue_cube_indices.append(self.gym.get_actor_index( + env_ptr, blue_cube_actor, gymapi.DOMAIN_SIM)) + + self.red_cube_init_state.append( + [red_cube_start_pose.p.x, + red_cube_start_pose.p.y, + red_cube_start_pose.p.z, + red_cube_start_pose.r.x, + red_cube_start_pose.r.y, + red_cube_start_pose.r.z, + red_cube_start_pose.r.w, + 0, 0, 0, 0, 0, 0]) + self.green_cube_init_state.append( + [green_cube_start_pose.p.x, + green_cube_start_pose.p.y, + green_cube_start_pose.p.z, + green_cube_start_pose.r.x, + green_cube_start_pose.r.y, + green_cube_start_pose.r.z, + green_cube_start_pose.r.w, + 0, 0, 0, 0, 0, 0]) + self.blue_cube_init_state.append( + [blue_cube_start_pose.p.x, + blue_cube_start_pose.p.y, + blue_cube_start_pose.p.z, + blue_cube_start_pose.r.x, + blue_cube_start_pose.r.y, + blue_cube_start_pose.r.z, + blue_cube_start_pose.r.w, + 0, 0, 0, 0, 0, 0]) + + self.red_cubes.append(red_cube_actor) + self.green_cubes.append(green_cube_actor) + self.blue_cubes.append(blue_cube_actor) + + def _process_task_handles(self) -> None: + self.ball_init_state = to_torch( + self.ball_init_state, device=self.device, dtype=torch.float + ).view(self.num_envs, self.cfg["asset"]["numBalls"], 13) + self.full_cup_init_state = to_torch( + self.full_cup_init_state, device=self.device, dtype=torch.float + ).view(self.num_envs, 13) + self.full_cup_indices = to_torch( + self.full_cup_indices, dtype=torch.long, device=self.device) + self.empty_cup_indices = to_torch( + self.empty_cup_indices, dtype=torch.long, device=self.device) + + self.red_cube_indices = to_torch( + self.red_cube_indices, dtype=torch.long, device=self.device) + self.green_cube_indices = to_torch( + self.green_cube_indices, dtype=torch.long, device=self.device) + self.blue_cube_indices = to_torch( + self.blue_cube_indices, dtype=torch.long, device=self.device) + + self.red_cube_init_state = to_torch( + self.red_cube_init_state, device=self.device, dtype=torch.float + ).view(self.num_envs, 13) + self.green_cube_init_state = to_torch( + self.green_cube_init_state, device=self.device, dtype=torch.float + ).view(self.num_envs, 13) + self.blue_cube_init_state = to_torch( + self.blue_cube_init_state, device=self.device, dtype=torch.float + ).view(self.num_envs, 13) + + def _begin_aggregate(self, env_ptr, env_idx: int) -> None: + max_agg_bodies = self.num_robot_bodies + self.num_table_bodies + \ + self.num_cups_bodies + self.num_ball_bodies + max_agg_shapes = self.num_robot_shapes + self.num_table_shapes + \ + self.num_cups_shapes + self.num_ball_shapes + + self.aggregate_mode = 0 + + if self.aggregate_mode >= 1: + self.gym.begin_aggregate(env_ptr, max_agg_bodies, + max_agg_shapes, True) + + def reset_idx(self, env_ids): + # Domain randomization would be added here ... + + if not self.initial_reset and not self.cfg["control"]["teleoperated"]: + self._reset_robot(env_ids) + else: + self._reset_robot(env_ids, initial_sampling=True) + # simulate for one time-step to update the tracker position + self.render() + self.gym.simulate(self.sim) + self._refresh_state_tensors() + self.initial_tracker_pose = self.tracker_pose + self.initial_finger_angles = self.robot_dof_pos[:, + self.hand_actuated_dof_indices] + + self._reset_full_cup(env_ids) + self._reset_cubes(env_ids) + + self.progress_buf[env_ids] = 0 + self.reset_buf[env_ids] = 0 + + def _reset_cubes(self, env_ids) -> None: + self.root_state_tensor[self.red_cube_indices[env_ids]] = \ + self.red_cube_init_state[env_ids].clone() + self.root_state_tensor[self.green_cube_indices[env_ids]] = \ + self.green_cube_init_state[env_ids].clone() + self.root_state_tensor[self.blue_cube_indices[env_ids]] = \ + self.blue_cube_init_state[env_ids].clone() + + # Set root state tensor of the simulation + cube_indices = torch.unique( + torch.cat([self.red_cube_indices[env_ids], + self.green_cube_indices[env_ids], + self.blue_cube_indices[env_ids]]).to(torch.int32)) + self.gym.set_actor_root_state_tensor_indexed( + self.sim, gymtorch.unwrap_tensor(self.root_state_tensor), + gymtorch.unwrap_tensor(cube_indices), len(cube_indices)) diff --git a/rofunc/learning/RofuncRL/tasks/isaacgymenv/grasp/utils/config.py b/rofunc/learning/RofuncRL/tasks/isaacgymenv/grasp/utils/config.py new file mode 100644 index 00000000..37cf5d6a --- /dev/null +++ b/rofunc/learning/RofuncRL/tasks/isaacgymenv/grasp/utils/config.py @@ -0,0 +1,43 @@ +import numpy as np +from omegaconf import OmegaConf +import os +import random +import torch + + +def register_omegaconf_resolvers() -> None: + """Registers custom resolvers for OmegaConf.""" + OmegaConf.register_new_resolver('eq', lambda x, y: x.lower() == y.lower()) + OmegaConf.register_new_resolver( + 'contains', lambda x, y: x.lower() in y.lower()) + OmegaConf.register_new_resolver('if', lambda pred, a, b: a if pred else b) + OmegaConf.register_new_resolver( + 'resolve_default', lambda default, arg: default if arg == '' else arg) + + +def set_seed(seed, torch_deterministic=False): + """Sets the seed for all used modules. + + Will choose a random seed if seed=-1 and not deterministic. + """ + if seed == -1 and torch_deterministic: + seed = 0 + elif seed == -1: + seed = np.random.randint(0, 10000) + + random.seed(seed) + np.random.seed(seed) + torch.manual_seed(seed) + os.environ['PYTHONHASHSEED'] = str(seed) + torch.cuda.manual_seed(seed) + torch.cuda.manual_seed_all(seed) + + if torch_deterministic: + os.environ['CUBLAS_WORKSPACE_CONFIG'] = ':4096:8' + torch.backends.cudnn.benchmark = False + torch.backends.cudnn.deterministic = True + torch.use_deterministic_algorithms(True) + else: + torch.backends.cudnn.benchmark = True + torch.backends.cudnn.deterministic = False + return seed \ No newline at end of file diff --git a/rofunc/learning/RofuncRL/tasks/isaacgymenv/grasp/utils/demo.py b/rofunc/learning/RofuncRL/tasks/isaacgymenv/grasp/utils/demo.py new file mode 100644 index 00000000..f103f006 --- /dev/null +++ b/rofunc/learning/RofuncRL/tasks/isaacgymenv/grasp/utils/demo.py @@ -0,0 +1,109 @@ +import os + +import h5py +import json +import torch +from robomimic.envs.env_base import EnvType +from typing import * + +def write_to_hdf5(cfg: Dict[str, Any], actions: torch.Tensor, + rewards: Dict[str, torch.Tensor], observations: Dict[str, torch.Tensor], + dones: torch.Tensor) -> None: + hdf5_dir = cfg["demo"]["collection"]["dir"] + if not os.path.exists(hdf5_dir): + os.mkdir(hdf5_dir) + hdf5_path = hdf5_dir + f"/{cfg['task_name']}.hdf5" + f = h5py.File(hdf5_path, "a") + if len(f.keys()) == 0: + grp = f.create_group("data") + grp.attrs["total"] = 0 + env_meta = dict( + type=EnvType.GYMGRASP_TYPE, + env_name=cfg["task_name"], + env_kwargs={"task": cfg["task"], + "task_name": cfg["task_name"], + "sim_device": cfg["sim_device"], + "rl_device": cfg["rl_device"], + "graphics_device_id": cfg["graphics_device_id"], + "headless": cfg["headless"], + "multi_gpu": cfg["multi_gpu"]} + ) + grp.attrs["env_args"] = json.dumps(env_meta, indent=4) + demo_group = grp.create_group("demo_0") + else: + grp = f["data"] + num_demos = len(grp.keys()) + demo_group = grp.create_group(f"demo_{num_demos}") + demo_group.attrs["seed"] = cfg["seed"] + demo_group.attrs["num_samples"] = actions.shape[0] + demo_group.create_dataset("actions", data=actions) + demo_group.create_dataset("sparse_rewards", data=rewards["sparse"].flatten().cpu()) + demo_group.create_dataset("dense_rewards", data=rewards["dense"].flatten().cpu()) + demo_group.create_dataset("dones", data=dones.flatten().cpu()) + obs_group = demo_group.create_group("obs") + next_obs_group = demo_group.create_group("next_obs") + for k, v in observations.items(): + obs_group.create_dataset(k, data=v[:-1].cpu()) + next_obs_group.create_dataset(k, data=v[1:].cpu()) + grp.attrs["total"] += demo_group.attrs["num_samples"] + print(f"Collected {demo_group.name}.") + f.close() + +def dict_key_equal_rec(key: str, old_dict: Dict[str, Any], new_dict: Dict[str, Any]) -> Tuple[bool, Dict[str, Any]]: + """Recursively checks whether values of key are equal in two dictionaries.""" + differences = {} + try: + old_val = old_dict[key] + except KeyError: + differences[key] = "KeyError in old configuration" + return False, differences + try: + new_val = new_dict[key] + except KeyError: + differences[key] = "KeyError in new configuration" + return False, differences + if type(old_val) == dict: + differences[key] = {} + for k in old_val.keys(): + equal, differences_k = dict_key_equal_rec(k, old_val, new_val) + if not equal: + differences[key][k] = differences_k[k] + if differences[key] == {}: # no differences + return True, differences + else: + return False, differences + else: # not dict + equal = (old_val == new_val) + if not equal: + differences[key] = f"{old_val} -> {new_val}" + return equal, differences + +def check_config_equality(cfg: Dict[str, Any]) -> bool: + hdf5_dir = cfg["demo"]["collection"]["dir"] + if not os.path.exists(hdf5_dir): # did not collect demos before + return True + hdf5_path = hdf5_dir + f"/{cfg['task_name']}.hdf5" + f = h5py.File(hdf5_path, "a") + if len(f.keys()) == 0: # did not collect demos for this task before + f.close() + return True + grp = f["data"] + env_meta_old = json.loads(grp.attrs["env_args"]) + env_meta_new = dict( + type=EnvType.GYMGRASP_TYPE, + env_name=cfg["task_name"], + env_kwargs={"task": cfg["task"], + "task_name": cfg["task_name"], + "sim_device": cfg["sim_device"], + "rl_device": cfg["rl_device"], + "graphics_device_id": cfg["graphics_device_id"], + "headless": cfg["headless"], + "multi_gpu": cfg["multi_gpu"]} + ) + if env_meta_old == env_meta_new: + return True + else: + for k in env_meta_old.keys(): + _, differences = dict_key_equal_rec(k, env_meta_old, env_meta_new) + print(f"Detected differences between environment configurations:\n{json.dumps(differences, indent=4)}") + return False \ No newline at end of file diff --git a/rofunc/learning/RofuncRL/tasks/isaacgymenv/grasp/utils/rlgames_utils.py b/rofunc/learning/RofuncRL/tasks/isaacgymenv/grasp/utils/rlgames_utils.py new file mode 100644 index 00000000..19d53ce0 --- /dev/null +++ b/rofunc/learning/RofuncRL/tasks/isaacgymenv/grasp/utils/rlgames_utils.py @@ -0,0 +1,183 @@ +# Copyright (c) 2018-2021, NVIDIA Corporation +# All rights reserved. +# +# Redistribution and use in source and binary forms, with or without +# modification, are permitted provided that the following conditions are met: +# +# 1. Redistributions of source code must retain the above copyright notice, this +# list of conditions and the following disclaimer. +# +# 2. Redistributions in binary form must reproduce the above copyright notice, +# this list of conditions and the following disclaimer in the documentation +# and/or other materials provided with the distribution. +# +# 3. Neither the name of the copyright holder nor the names of its +# contributors may be used to endorse or promote products derived from +# this software without specific prior written permission. +# +# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE +# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, +# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +from rl_games.common import env_configurations, vecenv +from rl_games.common.algo_observer import AlgoObserver +from rl_games.algos_torch import torch_ext +import torch +import numpy as np +from typing import Callable +import wandb +from tensorboardX import SummaryWriter +from typing import * +from gym_grasp.tasks import task_map + + +def get_rlgames_env_creator( + # used to create the vec task + task_config: dict, + task_name: str, + sim_device: str, + rl_device: str, + graphics_device_id: int, + headless: bool, + # Used to handle multi-gpu case + multi_gpu: bool = False, + post_create_hook: Callable = None, +): + """Parses the configuration parameters for the environment task and creates a VecTask + + Args: + task_config: environment configuration. + task_name: Name of the task, used to evaluate based on the imported name (eg 'Trifinger') + sim_device: The type of env device, eg 'cuda:0' + rl_device: Device that RL will be done on, eg 'cuda:0' + graphics_device_id: Graphics device ID. + headless: Whether to run in headless mode. + multi_gpu: Whether to use multi gpu + post_create_hook: Hooks to be called after environment creation. + [Needed to setup WandB only for one of the RL Games instances when doing multiple GPUs] + Returns: + A VecTaskPython object. + """ + def create_rlgpu_env(_sim_device=sim_device, _rl_device=rl_device, **kwargs): + """ + Creates the task from configurations and wraps it using RL-games wrappers if required. + """ + + if multi_gpu: + import horovod.torch as hvd + + rank = hvd.rank() + print("Horovod rank: ", rank) + + _sim_device = f'cuda:{rank}' + _rl_device = f'cuda:{rank}' + + task_config['rank'] = rank + task_config['rl_device'] = 'cuda:' + str(rank) + else: + _sim_device = sim_device + _rl_device = rl_device + + # create native task and pass custom config + env = task_map[task_name]( + cfg=task_config, + sim_device=_sim_device, + graphics_device_id=graphics_device_id, + headless=headless + ) + + if post_create_hook is not None: + post_create_hook() + + return env + return create_rlgpu_env + + +class GymGraspAlgoObserver(AlgoObserver): + """Allows us to log stats from the env along with the algorithm running stats. """ + + def __init__(self): + pass + + def after_init(self, algo): + self.algo = algo + self.mean_scores = torch_ext.AverageMeter(1, self.algo.games_to_track).to(self.algo.ppo_device) + self.ep_infos = [] + self.direct_info = {} + self.writer = self.algo.writer + + def process_infos(self, infos, done_indices): + assert isinstance(infos, dict), "RLGPUAlgoObserver expects dict info" + if isinstance(infos, dict): + if 'episode' in infos: + self.ep_infos.append(infos['episode']) + + if len(infos) > 0 and isinstance(infos, dict): # allow direct logging from env + self.direct_info = {} + for k, v in infos.items(): + # only log scalars + if isinstance(v, float) or isinstance(v, int) or (isinstance(v, torch.Tensor) and len(v.shape) == 0): + self.direct_info[k] = v + + def after_clear_stats(self): + self.mean_scores.clear() + + def after_print_stats(self, frame, epoch_num, total_time): + if self.ep_infos: + for key in self.ep_infos[0]: + infotensor = torch.tensor([], device=self.algo.device) + for ep_info in self.ep_infos: + # handle scalar and zero dimensional tensor infos + if not isinstance(ep_info[key], torch.Tensor): + ep_info[key] = torch.Tensor([ep_info[key]]) + if len(ep_info[key].shape) == 0: + ep_info[key] = ep_info[key].unsqueeze(0) + infotensor = torch.cat((infotensor, ep_info[key].to(self.algo.device))) + value = torch.mean(infotensor) + self.writer.add_scalar('Episode/' + key, value, epoch_num) + self.ep_infos.clear() + + for k, v in self.direct_info.items(): + self.writer.add_scalar(f'{k}/frame', v, frame) + self.writer.add_scalar(f'{k}/iter', v, epoch_num) + self.writer.add_scalar(f'{k}/time', v, total_time) + + if self.mean_scores.current_size > 0: + mean_scores = self.mean_scores.get_mean() + self.writer.add_scalar('scores/mean', mean_scores, frame) + self.writer.add_scalar('scores/iter', mean_scores, epoch_num) + self.writer.add_scalar('scores/time', mean_scores, total_time) + + +class GymGraspEnv(vecenv.IVecEnv): + def __init__(self, config_name, num_actors, **kwargs): + self.env = env_configurations.configurations[config_name]['env_creator'](**kwargs) + + def step(self, action): + return self.env.step(action) + + def reset(self): + return self.env.reset() + + def get_number_of_agents(self): + return self.env.get_number_of_agents() + + def get_env_info(self): + info = {} + info['action_space'] = self.env.action_space + info['observation_space'] = self.env.observation_space + + if self.env.num_states > 0: + info['state_space'] = self.env.state_space + print(info['action_space'], info['observation_space'], info['state_space']) + else: + print(info['action_space'], info['observation_space']) + + return info diff --git a/rofunc/learning/RofuncRL/tasks/isaacgymenv/grasp/utils/transform.py b/rofunc/learning/RofuncRL/tasks/isaacgymenv/grasp/utils/transform.py new file mode 100644 index 00000000..6514c81a --- /dev/null +++ b/rofunc/learning/RofuncRL/tasks/isaacgymenv/grasp/utils/transform.py @@ -0,0 +1,122 @@ +import torch +from torch import Tensor +from typing import * + + +def fuse_rgbd_to_pointcloud(rgbd: Tensor, k: Tensor, v: Tensor, + convert_rgb_to_camera_id: bool = False + ) -> Tuple[Tensor, Tensor]: + """Fuses multiple depth views into a point could. + + Args: + rgbd (tensor [B,V,H,W,4]): depth images from V different views + k: (tensor [4, 4]): camera projection matrix + v: (tensor [V, 4, 4]): view matrix of V different cameras + convert_rgb_to_camera_id (bool): Use colors to identify cameras + Returns: + """ + xyz, features = [], [] + num_cameras = rgbd.shape[1] + for c in range(num_cameras): + xyz_view, features_view = rgbd_to_pointcloud(rgbd[:, c], k, v[c], c, + convert_rgb_to_camera_id) + xyz.append(xyz_view) + features.append(features_view) + return torch.stack(xyz, dim=1), torch.stack(features, dim=1) + + +def rgbd_to_pointcloud(rgbd: Tensor, k: Tensor, v: Tensor, + camera_id: int, + convert_rgb_to_camera_id: bool + ) -> Tuple[Tensor, Tensor]: + color = rgbd[..., 0:3] + depth = rgbd[..., -1] + batch_size, height, width = depth.shape + + sparse_depth = depth.to_sparse() + indices = sparse_depth.indices() + values = sparse_depth.values() + + if convert_rgb_to_camera_id: + color_map = torch.Tensor( + [[0, 1, 0], [0, 0, 1], [1, 0, 0], [0, 1, 1]]).to(depth.device) + features = color_map[camera_id].unsqueeze(0).repeat(values.shape[0], 1) + else: + features = color.reshape(-1, 3) + + xy_depth = torch.cat([indices.T[:, 1:].flip(1), values[..., None]], dim=-1) + + fu = 2 / k[0, 0] + fv = 2 / k[1, 1] + center_u = width / 2 + center_v = height / 2 + + k_new = torch.Tensor([[fu, 0, 0], + [0, fv, 0], + [0, 0, 1]]).to(depth.device) + + xy_depth[:, 0] = -(xy_depth[:, 0] - center_u) / width + xy_depth[:, 1] = (xy_depth[:, 1] - center_v) / height + xy_depth[:, 0] *= xy_depth[:, 2] + xy_depth[:, 1] *= xy_depth[:, 2] + + x2 = xy_depth @ k_new + x2_hom = torch.cat([x2, torch.ones_like(x2[:, 0:1])], dim=1) + + xyz = (x2_hom @ v.inverse())[:, 0:3] + + xyz = xyz.view(batch_size, -1, 3) + features = features.view(batch_size, -1, 3) + return xyz, features + + +def subsample_valid(xyz, features, start=(-1, -1, 0.75), end=(1, 1, 1.75)): + valid = (xyz[..., 0] >= start[0]) & (xyz[..., 0] <= end[0]) & \ + (xyz[..., 1] >= start[1]) & (xyz[..., 1] <= end[1]) & \ + (xyz[..., 2] >= start[2]) & (xyz[..., 2] <= end[2]) + xyz_valid = xyz[valid] + features_valid = features[valid] + return xyz_valid, features_valid + + +def pointcloud_to_voxelgrid(xyz: Tensor, features: Tensor, + voxel_size: float = 0.01, + start: Tuple[float, float, float] = (-0.5, -0.5, 0.5), + end: Tuple[float, float, float] = (0.5, 0.5, 1.5) + ) -> Tuple[Tensor, Tensor]: + num_envs = xyz.shape[0] + + # I should be able to avoid iterating through envs if I adjust the subsample + # function to pad the smaller pointclouds with dummy values + + vg, vf = [], [] + for env_id in range(num_envs): + xyz_valid, features_valid = subsample_valid( + xyz[env_id].view(-1, 3), features[env_id].view(-1, 3), start, end) + + xyz_valid[..., 0] -= start[0] + xyz_valid[..., 1] -= start[1] + xyz_valid[..., 2] -= start[2] + + # quantize + xyz_indices = torch.round(xyz_valid / voxel_size).long() + + voxel_grid = torch.zeros([int(((end[0] - start[0]) / voxel_size) + 1), + int(((end[1] - start[1]) / voxel_size) + 1), + int(((end[2] - start[2]) / voxel_size) + 1)], + device=xyz.device) + + voxel_features = torch.zeros( + [int(((end[0] - start[0]) / voxel_size) + 1), + int(((end[1] - start[1]) / voxel_size) + 1), + int(((end[2] - start[2]) / voxel_size) + 1), 3], + device=xyz.device) + + voxel_grid[xyz_indices[:, 0], xyz_indices[:, 1], xyz_indices[:, 2]] = 1 + + voxel_features[xyz_indices[:, 0], xyz_indices[:, 1], xyz_indices[:, 2]] = features_valid + + vg.append(voxel_grid) + vf.append(voxel_features) + + return torch.stack(vg), torch.stack(vf) diff --git a/rofunc/learning/RofuncRL/tasks/isaacgymenv/grasp/utils/utils.py b/rofunc/learning/RofuncRL/tasks/isaacgymenv/grasp/utils/utils.py new file mode 100644 index 00000000..8b5b4ecc --- /dev/null +++ b/rofunc/learning/RofuncRL/tasks/isaacgymenv/grasp/utils/utils.py @@ -0,0 +1,19 @@ +import numpy as np +from omegaconf import OmegaConf +import os +import random +import torch + + +def set_printoptions(precision: int = 2, threshold: int = 10000, + edgeitems: int = 16, linewidth: int = 120, + suppress: bool = False) -> None: + """Sets printoptions of NumPy and PyTorch.""" + np.set_printoptions(precision=precision, threshold=threshold, + edgeitems=edgeitems, linewidth=linewidth, + suppress=suppress) + torch.set_printoptions(precision=precision, threshold=threshold, + edgeitems=edgeitems, linewidth=linewidth) + + + diff --git a/rofunc/learning/RofuncRL/tasks/isaacgymenv/hands/qbhand_synergy_grasp.py b/rofunc/learning/RofuncRL/tasks/isaacgymenv/hands/qbhand_synergy_grasp.py index e11af262..aab4e92d 100644 --- a/rofunc/learning/RofuncRL/tasks/isaacgymenv/hands/qbhand_synergy_grasp.py +++ b/rofunc/learning/RofuncRL/tasks/isaacgymenv/hands/qbhand_synergy_grasp.py @@ -297,9 +297,9 @@ def _create_envs(self, num_envs, spacing, num_per_row): # set shadow_hand dof properties hand_dof_props = self.gym.get_asset_dof_properties(hand_asset) - hand_dof_props['driveMode'] = gymapi.DOF_MODE_POS - hand_dof_props['stiffness'] = 1000000.0 - hand_dof_props['damping'] = 1000.0 + # hand_dof_props['driveMode'] = gymapi.DOF_MODE_POS + # hand_dof_props['stiffness'] = 1000000.0 + # hand_dof_props['damping'] = 1000.0 self.hand_dof_lower_limits = [] self.hand_dof_upper_limits = [] @@ -322,24 +322,30 @@ def _create_envs(self, num_envs, spacing, num_per_row): # object_asset_options = gymapi.AssetOptions() - object_asset_options.density = 100 + object_asset_options.density = 10 object_asset_options.fix_base_link = False # object_asset_options.collapse_fixed_joints = True - # object_asset_options.disable_gravity = True object_asset_options.use_mesh_materials = True object_asset_options.mesh_normal_mode = gymapi.COMPUTE_PER_VERTEX object_asset_options.override_com = True object_asset_options.override_inertia = True object_asset_options.vhacd_enabled = True + object_asset_options.disable_gravity = False object_asset_options.vhacd_params = gymapi.VhacdParams() object_asset_options.vhacd_params.resolution = 100000 object_asset_options.default_dof_drive_mode = gymapi.DOF_MODE_NONE + # object_asset_options.override_com = True + # object_asset_options.override_inertia = True + # # Enable convex decomposition + # object_asset_options.vhacd_enabled = True + # object_asset_options.vhacd_params = gymapi.VhacdParams() + # object_asset_options.vhacd_params.resolution = 1000000 object_asset = self.gym.load_asset(self.sim, asset_root, object_asset_file, object_asset_options) # block_asset_file = "urdf/objects/cube_multicolor1.urdf" # block_asset = self.gym.load_asset(self.sim, asset_root, block_asset_file, object_asset_options) - object_asset_options.disable_gravity = True + # object_asset_options.disable_gravity = True self.num_object_bodies = self.gym.get_asset_rigid_body_count(object_asset) self.num_object_shapes = self.gym.get_asset_rigid_shape_count(object_asset) @@ -373,12 +379,12 @@ def _create_envs(self, num_envs, spacing, num_per_row): # hand_start_pose = gymapi.Transform() - hand_start_pose.p = gymapi.Vec3(0.2, -0.1, 0.7) + hand_start_pose.p = gymapi.Vec3(0.1, -0.04, 0.65) hand_start_pose.r = gymapi.Quat().from_euler_zyx(-1.57, -1.57, 1.57) object_start_pose = gymapi.Transform() - object_start_pose.p = gymapi.Vec3(0.0, 0, 0.6) - object_start_pose.r = gymapi.Quat().from_euler_zyx(1.57, 0, 0) + object_start_pose.p = gymapi.Vec3(0.0, 0, 0.7) + object_start_pose.r = gymapi.Quat().from_euler_zyx(1.57, 0, -1.57) if self.object_type == "pen": object_start_pose.p.z = hand_start_pose.p.z + 0.02 @@ -446,9 +452,15 @@ def _create_envs(self, num_envs, spacing, num_per_row): hand_start_pose.r.w, 0, 0, 0, 0, 0, 0]) + hand_dof_props = self.gym.get_actor_dof_properties(env_ptr, hand_actor) + hand_dof_props["driveMode"].fill(gymapi.DOF_MODE_POS) + hand_dof_props["stiffness"].fill(10000.0) + hand_dof_props["damping"].fill(20.0) self.gym.set_actor_dof_properties(env_ptr, hand_actor, hand_dof_props) hand_idx = self.gym.get_actor_index(env_ptr, hand_actor, gymapi.DOMAIN_SIM) self.hand_indices.append(hand_idx) + self.gym.set_actor_scale(env_ptr, hand_actor, 1) + # num_bodies = self.gym.get_actor_rigid_body_count(env_ptr, hand_actor) @@ -482,9 +494,9 @@ def _create_envs(self, num_envs, spacing, num_per_row): table_idx = self.gym.get_actor_index(env_ptr, table_handle, gymapi.DOMAIN_SIM) self.table_indices.append(table_idx) - if self.object_type != "block": - self.gym.set_rigid_body_color( - env_ptr, object_handle, 0, gymapi.MESH_VISUAL, gymapi.Vec3(0.6, 0.72, 0.98)) + # if self.object_type != "block": + # self.gym.set_rigid_body_color( + # env_ptr, object_handle, 0, gymapi.MESH_VISUAL, gymapi.Vec3(0.6, 0.72, 0.98)) if self.obs_type in ["point_cloud"]: camera_handle = self.gym.create_camera_sensor(env_ptr, self.camera_props) @@ -543,11 +555,11 @@ def compute_reward(self, actions): self.rew_buf, self.reset_buf, self.reset_goal_buf, self.progress_buf, self.successes, self.consecutive_successes, self.max_episode_length, self.object_pos, self.object_rot, - self.hand_pos, self.hand_ff_pos, self.hand_mf_pos, + self.rigid_body_states[:, 1, 0:3], self.hand_ff_pos, self.hand_mf_pos, self.hand_rf_pos, self.hand_lf_pos, self.hand_th_pos, self.dist_reward_scale, self.rot_reward_scale, self.rot_eps, self.actions, self.action_penalty_scale, self.success_tolerance, self.reach_goal_bonus, self.fall_dist, self.fall_penalty, - self.max_consecutive_successes, self.av_factor, (self.object_type == "pen") + self.max_consecutive_successes, self.av_factor, (self.object_type == "pen"), self.prev_synergy_actions ) self.extras['successes'] = self.successes @@ -1009,8 +1021,12 @@ def pre_physics_step(self, actions): else: # assert list(self.actuated_dof_indices) == self.right_useful_joint_index synergy_action = self.actions[:, 6:8] - synergy_action[:, 0] = torch.abs(synergy_action[:, 0]) + # synergy_action[:, 0] = torch.abs(synergy_action[:, 0]) synergy_action = self.prev_synergy_actions * 0.9 + 0.1 * synergy_action + synergy_action[:, 0] = torch.abs(synergy_action[:, 0]) + # synergy_action = torch.zeros_like(self.actions[:, 6:8]).to(self.device) + # synergy_action[:, 0] = torch.ones_like(self.actions[:, 6]).to(self.device) + # synergy_action[:, 0] = self.actions[:, 6] self.prev_synergy_actions = synergy_action synergy_action_matrix = torch.tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [2, 2, 2, 1, 1, 1, 0, 0, 0, -1, -1, -1, -2, -2, -2]], @@ -1043,7 +1059,23 @@ def pre_physics_step(self, actions): # self.object_dof_lower_limits[1], self.object_dof_upper_limits[1]) # angle_offsets = self.actions[:, 26:32] * self.dt * self.orientation_scale - self.apply_forces[:, 0, :] = self.actions[:, 0:3] * self.dt * self.transition_scale * 100000 + self.object_pos = self.root_state_tensor[self.object_indices, 0:3] + hand_pos = self.rigid_body_states[:, 1, 0:3] + + hand_dist = torch.norm(self.object_pos - hand_pos, p=2, dim=-1) + # self.apply_forces[:, 0, :] = self.actions[:, 0:3] * self.dt * self.transition_scale * 100000 + # self.apply_torque[:, 0, :] = self.actions[:, 3:6] * self.dt * self.orientation_scale * 1000 + + # self.apply_forces[:, 0, :] = torch.zeros_like(self.actions[:, 0:3]) + # self.apply_torque[:, 0, :] = torch.zeros_like(self.actions[:, 3:6]) + + self.apply_forces[:, 0, :] = torch.where(hand_dist.unsqueeze(-1) < 0.05, + torch.zeros_like(self.actions[:, 0:3]) + torch.tensor( + [0., 0., 1000]).to(self.device), + self.actions[:, 0:3] * self.dt * self.transition_scale * 100000) + # self.apply_torque[:, 0, :] = torch.where(hand_dist.unsqueeze(-1) < 0.2, + # torch.zeros_like(self.actions[:, 3:6]), + # self.actions[:, 3:6] * self.dt * self.orientation_scale * 1000) self.apply_torque[:, 0, :] = self.actions[:, 3:6] * self.dt * self.orientation_scale * 1000 self.gym.apply_rigid_body_force_tensors(self.sim, gymtorch.unwrap_tensor(self.apply_forces), @@ -1194,8 +1226,8 @@ def compute_hand_reward( dist_reward_scale: float, rot_reward_scale: float, rot_eps: float, actions, action_penalty_scale: float, success_tolerance: float, reach_goal_bonus: float, fall_dist: float, - fall_penalty: float, max_consecutive_successes: int, av_factor: float, ignore_z_rot: bool -): + fall_penalty: float, max_consecutive_successes: int, av_factor: float, ignore_z_rot: bool, + prev_synergy_actions): """ Compute the reward of all environment. @@ -1269,6 +1301,10 @@ def compute_hand_reward( action_penalty = torch.sum(actions ** 2, dim=-1) + synergy = prev_synergy_actions + synergy_target = torch.tensor([1, 0]).to(synergy.device) + synergy_dist = torch.norm(synergy - synergy_target, p=2, dim=-1) + # # Total reward is: position distance + orientation alignment + action regularization + success bonus + fall penalty # # reward = torch.exp(-0.05*(up_rew * dist_reward_scale)) + torch.exp(-0.05*(right_hand_dist_rew * dist_reward_scale)) + torch.exp(-0.05*(left_hand_dist_rew * dist_reward_scale)) # # up_rew = torch.zeros_like(right_hand_dist_rew) @@ -1280,14 +1316,18 @@ def compute_hand_reward( # # # reward = torch.exp(-0.1*(right_hand_dist_rew * dist_reward_scale)) + torch.exp(-0.1*(left_hand_dist_rew * dist_reward_scale)) - up_rew = object_pos[:, 2] - 0.6 + up_rew = object_pos[:, 2] - 0.7 + up_rew = torch.where(up_rew > 0, up_rew, torch.zeros_like(up_rew))* 100 # print("Max height: {}, Avg height: {}".format(torch.max(object_pos[:, 2]), torch.mean(object_pos[:, 2]))) - reward = up_rew * 10 + reward = up_rew - synergy_dist - hand_dist + print("up_rew: {}, synergy_rew: {}, hand_dist: {}".format(float(torch.mean(up_rew).cpu()), + float(torch.mean(synergy_dist).cpu()), + float(torch.mean(hand_dist).cpu()))) # resets = torch.where(hand_dist_rew <= 0, torch.ones_like(reset_buf), reset_buf) # resets = torch.where(object_rot[:, 3] > 0.9, torch.ones_like(resets), resets) # resets = torch.where(right_hand_finger_dist >= 1.5, torch.ones_like(resets), resets) - resets = torch.where(hand_finger_dist >= 1.5, torch.ones_like(reset_buf), reset_buf) + resets = torch.where(hand_dist >= 0.2, torch.ones_like(reset_buf), reset_buf) # Find out which envs hit the goal and update successes count successes = torch.where(successes == 0, torch.where(object_pos[:, 2] > 1.0, diff --git a/rofunc/simulator/assets/mjcf/qbhand_full_left.xml b/rofunc/simulator/assets/mjcf/qbhand_full_left.xml index b6622806..7810808a 100644 --- a/rofunc/simulator/assets/mjcf/qbhand_full_left.xml +++ b/rofunc/simulator/assets/mjcf/qbhand_full_left.xml @@ -1,19 +1,25 @@ - + diff --git a/rofunc/simulator/assets/urdf/sih/meshes/schunk_sih_right/collision/if_distal.stl b/rofunc/simulator/assets/urdf/sih/meshes/schunk_sih_right/collision/if_distal.stl new file mode 100644 index 00000000..54a2de9d Binary files /dev/null and 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00000000..ea34dd9c Binary files /dev/null and b/rofunc/simulator/assets/urdf/sih/meshes/schunk_sih_right/collision/lf_proximal.stl differ diff --git a/rofunc/simulator/assets/urdf/sih/meshes/schunk_sih_right/collision/mf_distal.stl b/rofunc/simulator/assets/urdf/sih/meshes/schunk_sih_right/collision/mf_distal.stl new file mode 100644 index 00000000..8e56b55c Binary files /dev/null and b/rofunc/simulator/assets/urdf/sih/meshes/schunk_sih_right/collision/mf_distal.stl differ diff --git a/rofunc/simulator/assets/urdf/sih/meshes/schunk_sih_right/collision/mf_proximal.stl b/rofunc/simulator/assets/urdf/sih/meshes/schunk_sih_right/collision/mf_proximal.stl new file mode 100644 index 00000000..bcdd5b17 Binary files /dev/null and b/rofunc/simulator/assets/urdf/sih/meshes/schunk_sih_right/collision/mf_proximal.stl differ diff --git a/rofunc/simulator/assets/urdf/sih/meshes/schunk_sih_right/collision/palm.stl b/rofunc/simulator/assets/urdf/sih/meshes/schunk_sih_right/collision/palm.stl new file mode 100644 index 00000000..93d1c227 Binary files /dev/null and b/rofunc/simulator/assets/urdf/sih/meshes/schunk_sih_right/collision/palm.stl differ diff --git a/rofunc/simulator/assets/urdf/sih/meshes/schunk_sih_right/collision/rf_distal.stl b/rofunc/simulator/assets/urdf/sih/meshes/schunk_sih_right/collision/rf_distal.stl new file mode 100644 index 00000000..8deddf34 Binary files /dev/null and b/rofunc/simulator/assets/urdf/sih/meshes/schunk_sih_right/collision/rf_distal.stl differ diff --git a/rofunc/simulator/assets/urdf/sih/meshes/schunk_sih_right/collision/rf_proximal.stl b/rofunc/simulator/assets/urdf/sih/meshes/schunk_sih_right/collision/rf_proximal.stl new file mode 100644 index 00000000..16ae4ca7 Binary files /dev/null and b/rofunc/simulator/assets/urdf/sih/meshes/schunk_sih_right/collision/rf_proximal.stl differ diff --git a/rofunc/simulator/assets/urdf/sih/meshes/schunk_sih_right/collision/th_distal.stl b/rofunc/simulator/assets/urdf/sih/meshes/schunk_sih_right/collision/th_distal.stl new file mode 100644 index 00000000..91f7f5cb Binary files /dev/null and b/rofunc/simulator/assets/urdf/sih/meshes/schunk_sih_right/collision/th_distal.stl differ diff --git a/rofunc/simulator/assets/urdf/sih/meshes/schunk_sih_right/collision/th_inter.stl b/rofunc/simulator/assets/urdf/sih/meshes/schunk_sih_right/collision/th_inter.stl new file mode 100644 index 00000000..7bf1ba19 Binary files /dev/null and b/rofunc/simulator/assets/urdf/sih/meshes/schunk_sih_right/collision/th_inter.stl differ diff --git a/rofunc/simulator/assets/urdf/sih/meshes/schunk_sih_right/collision/th_inter_cvx.stl b/rofunc/simulator/assets/urdf/sih/meshes/schunk_sih_right/collision/th_inter_cvx.stl new file mode 100644 index 00000000..c33424e5 Binary files /dev/null and b/rofunc/simulator/assets/urdf/sih/meshes/schunk_sih_right/collision/th_inter_cvx.stl differ diff --git 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146.047 29.32814 3.477758 146.897 29.32814 4.100001 146.2748 29.32814 3.250001 147.747 29.32814 1.938502 150.1162 29.32814 4.950001 146.047 29.32561 8.800002 145.1239 32.02814 5.300001 142.85 32.65314 5.300001 143.0175 31.40314 5.300001 145.1825 31.40314 5.300001 143.0175 33.11067 5.300001 143.475 30.94561 5.300001 143.475 33.27814 5.300001 144.1 30.77814 5.300001 144.1 33.11067 5.300001 144.725 30.94561 0.8000013 144.725 32.65314 5.300001 145.1825 32.02814 5.300001 145.35 30.94561 5.300001 144.725 39.83866 -5.295095 148.2738 23.02814 -5.273321 147.8647 32.65314 0.8000014 145.1825 39.58342 0.8000017 150.0451 31.40314 0.8000013 145.1825 34.72814 8.800002 149.347 36.00453 8.800002 145.8396 35.52814 8.800002 145.2767 35.52814 11.8 145.2767 34.72814 6.422244 148.597 34.72815 6.664526 147.5695 34.72814 6.422244 146.897 29.32824 8.651195 150.1734 28.02814 8.800001 149.347 28.05492 8.800002 145.799 29.32814 6.422244 146.897 29.32814 5.800001 146.2748 28.52814 8.800001 145.2767 36.00136 11.8 145.799 36.02814 11.8 150.1 28.05176 11.8 145.8396 28.52814 11.8 145.2767 32.02814 0.8000013 145.35 37.76707 0.8000016 152.5451 36.13599 0.7994052 153.3305 38.87322 0.8000017 151.4389 28.25839 0.799805 153.4456 26.28921 0.8000011 152.5451 25.18306 0.800001 151.4389 36.02832 8.651186 150.1734 36.02817 10.08453 146.262 36.0274 9.322669 146.9506 28.02874 9.295872 147.0506 28.02811 10.51547 146.262 28.02814 9.8 146.4001 36.02814 10.8 146.4001 36.02759 11.30413 147.0506 28.02814 11.8 150.1 28.02868 11.27733 146.9506 34.72814 3.250001 147.747 29.32814 6.650001 147.747 36.02814 9.538125 147.9548 34.02815 9.611328 148.028 30.0282 9.538122 147.9548 28.02814 9.611326 148.028 34.02814 11.06188 147.9548 36.02814 10.98868 148.028 30.02814 10.98868 148.028 28.02814 11.06188 147.9548 28.02814 11 150.1 34.72814 3.477758 148.597 29.32814 3.477758 148.597 29.32814 6.422244 148.597 36.02814 11 150.1 41.03064 -6.016539 152.137 39.68292 -5.510063 149.7902 23.02399 -6.006684 152.1321 36.02814 10.3 148.2661 34.02814 10.3 148.2661 30.02814 10.3 148.2661 28.02814 10.3 148.2661 28.02814 8.532051 150.347 28.02814 10.15858 150.7755 34.72814 4.100001 149.2193 29.32814 4.100001 149.2193 34.72814 5.800001 149.2193 29.32814 5.800001 149.2193 34.72814 8.532052 150.347 34.72814 7.800001 151.0791 29.32814 7.569172 151.2085 34.72814 4.950001 149.447 34.72814 3.030831 151.2085 29.32814 4.950001 149.447 29.32814 2.800001 151.0791 34.72814 6.800001 151.347 29.32814 3.800001 151.347 24.59449 -5.625159 150.4189 41.02814 2.067951 150.347 23.02814 2.06795 150.347 39.04305 -5.80195 151.2351 41.02814 11 150.1 41.02814 11.8 150.1 36.02814 10.15858 150.7755 41.02814 10.15858 150.7755 41.02977 11.79098 150.9264 23.02814 11 150.1 23.02814 11.8 150.1 23.02814 10.15858 150.7755 23.02519 11.7911 150.9292 33.77386 7.075949 151.1079 30.31437 7.212177 150.8008 41.02814 2.800002 151.0791 23.02814 2.800001 151.0791 36.02814 7.800001 151.0791 36.02814 8.96102 151.2686 28.02814 7.800001 151.0791 28.02814 8.817319 151.2979 25.63829 -5.992419 152.0164 41.02822 8.817323 151.2979 41.02834 10.74821 151.9522 23.02814 8.961019 151.2686 23.02922 10.7117 151.9987 31.134 6.55798 151.5506 29.79396 7.09234 151.2951 34.57038 11.17653 151.6703 29.48132 11.18455 151.661 28.37731 10.03688 153.0771 33.9008 6.496286 151.6329 41.02814 3.800002 151.347 23.02814 3.800001 151.347 36.02814 6.800001 151.347 28.02814 6.800001 151.347 34.30403 6.98851 151.4032 30.0393 6.539356 151.6558 34.91756 6.30548 152.2045 29.4603 6.581436 151.8496 35.68156 10.04388 153.0685 40.15422 10.21605 152.7614 37.78121 -6.170577 152.588 33.83943 5.943123 151.7942 28.50867 6.878253 152.8407 24.61018 9.287933 153.9024 33.10094 4.977692 151.8941 31.09137 4.923563 151.8918 29.05921 6.174125 152.3469 35.55375 6.938769 152.8308 32.89487 4.236283 152.3261 31.22312 4.21948 152.3261 34.34124 5.119934 152.3175 29.76579 5.064585 152.3205 23.97576 -6.449615 153.4353 39.84053 8.78214 153.7968 24.19436 8.863238 153.7287 33.46913 3.912288 153.184 32.45412 3.655408 153.1283 31.53474 3.661143 153.1543 34.26154 4.374083 153.1584 30.53386 3.943368 153.1396 34.99442 5.108274 153.2019 29.79441 4.373754 153.1824 35.45872 5.952345 153.1793 29.07721 5.10291 153.1584 28.58966 5.979705 153.1975 39.58172 -6.767002 154.3294 27.30759 -6.343635 153.133 24.69297 -7.01736 154.9067 35.64138 -6.450316 153.45 35.74992 6.785617 153.7606 28.30194 6.814818 153.7528 35.6297 -5.792233 153.4358 35.65194 -5.240118 153.4323 35.24809 -6.192324 153.4864 28.76136 -6.466869 153.492 35.21957 -4.797086 153.4864 34.7266 -4.75634 153.5045 25.03953 7.24417 155.7449 38.39385 7.60406 156.047 39.15116 8.726133 154.5494 35.66055 -5.744381 157.8046 35.63419 -5.21717 157.7347 34.67766 -6.225491 153.5043 35.12874 -4.751737 156.9907 35.12464 -6.25527 156.9737 34.27862 -5.874998 153.5 34.19163 -5.274442 153.5001 34.33095 -6.006916 156.5743 34.18468 -5.172523 156.5383 31.19304 3.583122 154.9145 32.88756 3.596682 154.9143 34.02614 4.047933 155.1897 30.13238 4.067891 154.6511 29.5355 4.446596 155.4966 35.03154 5.030413 155.9922 28.8369 5.262474 156.5459 35.62788 6.087202 157.6319 28.40583 6.063519 157.7149 28.27723 7.234215 157.1191 35.77754 7.226167 157.1995 35.77959 7.617255 156.1145 28.27795 7.574615 156.1757 39.04963 7.365526 155.6548 25.589 7.622374 156.0117 29.90212 -7.789928 156.5298 28.88887 -8.068019 157.0307 35.26996 -8.121086 157.1308 34.26163 -7.798036 156.5464 25.12449 6.445088 156.4472 25.26369 -8.01438 156.9817 38.80209 -8.003749 156.9949 32.02814 3.158699 155.6441 33.52186 3.437539 155.7038 30.5279 3.574537 155.4968 34.44026 3.907455 156.1374 38.89928 6.723602 156.4595 30.4535 3.02013 156.1039 29.60896 3.941435 156.1147 32.02814 2.669408 156.1329 33.68374 2.903795 156.206 38.61231 7.103169 156.5823 25.42713 7.093093 156.569 34.56155 3.576424 156.4826 29.74417 2.902022 156.5191 29.0036 3.906107 156.9459 32.03106 1.916972 156.4682 25.28914 6.447651 157.4383 33.62258 2.245956 156.4601 30.44196 2.260787 156.4544 38.12076 7.279808 156.8619 25.95953 7.270132 156.9269 35.22275 4.684381 157.0307 34.42927 2.356792 156.6101 38.64125 -8.264105 157.3944 38.755 6.469649 157.7477 38.04642 7.16019 157.6557 38.48442 6.899734 157.7054 25.57186 6.899733 157.7054 26.02107 7.168581 157.6872 28.31172 -8.708324 158.1321 25.79622 -8.608597 157.9652 35.75281 -8.707472 158.1232 35.77462 7.274615 158.4062 28.2657 7.26901 158.4496 38.07464 -8.668303 158.0493 38.24922 -8.590663 166.2382 35.77814 -8.699997 165.5 25.99526 -8.656173 166.1407 38.75101 -7.966322 166.0003 25.56663 -8.383665 166.1607 25.30281 -7.80268 167.3628 38.72666 0.1990691 160.9084 38.70615 -1.343807 161.2289 38.68932 -2.634023 162.0214 38.71282 -3.634174 163.1769 25.27814 -4.328595 164.6838 25.36858 -3.637347 163.2129 25.36918 -2.620644 162.0073 25.36958 -1.2536 161.2207 25.32411 0.4270628 160.8946 38.71414 1.788074 161.1598 38.71796 3.191455 161.9302 25.33267 1.929499 161.1938 38.05702 8.264612 164.972 25.99284 8.261244 164.9876 28.27814 8.202959 164.2577 38.46803 8.05789 165.7272 25.46966 7.912985 165.0452 38.7557 7.549948 164.9248 25.28674 7.366388 167.2406 38.72194 4.248016 163.14 38.73767 4.872507 164.8014 25.33683 3.310493 162.0268 25.32536 4.333236 163.2858 35.77814 -0.4276893 162.0765 35.77814 -1.757247 162.6684 35.77814 -2.731087 163.75 35.77814 -3.180825 165.1341 28.27814 1.027692 162.0765 28.27814 -0.4276897 162.0765 28.27814 -1.757247 162.6684 28.27814 -2.731087 163.75 28.27607 -8.564116 167.1759 35.77914 8.317449 165.8338 35.77814 1.027692 162.0765 35.77814 2.35725 162.6684 35.77814 3.33109 163.75 28.27814 3.780828 165.1341 28.27814 3.33109 163.75 28.27814 2.357249 162.6684 38.17814 0.7152093 161.0192 25.87814 -0.5268716 161.0766 38.11167 0.2999895 161.9336 38.17814 -0.9314817 161.1718 38.11171 1.657232 162.2018 38.17814 2.305824 161.4718 25.87814 0.3000011 161.8 25.87814 1.126874 161.0766 25.94457 -1.057228 162.2018 25.87814 -2.068943 161.674 25.87814 2.668946 161.674 38.11171 -1.057228 162.2018 38.17814 -2.411854 161.9089 38.11184 2.810459 162.9663 38.17814 3.625541 162.4683 25.94444 -2.210456 162.9663 25.87814 -3.291076 162.7881 25.94457 1.657231 162.2018 25.87814 3.891078 162.7881 26.07814 0.3000011 162 38.11185 -2.210459 162.9663 38.17814 -3.525975 163.131 25.94444 2.810464 162.9663 38.11206 3.586247 164.1124 38.17814 4.496126 163.8744 25.94422 -2.986247 164.1124 25.87814 -4.028214 164.2685 25.87814 4.628216 164.2685 25.34456 4.829394 164.7893 38.11206 -2.986246 164.1124 25.94422 3.58625 164.1124 38.17814 -4.123377 164.6731 38.69623 -4.183479 164.6575 25.47814 -4.131634 164.7186 28.27814 -3.180825 165.1341 35.77814 3.780828 165.1341 38.11237 3.867655 165.4678 38.17814 4.800001 165.5 38.17814 -3.399998 165.5 25.94391 -3.267652 165.4678 25.94391 3.867655 165.4678 25.87814 4.780804 165.9152 37.97814 -3.199999 165.5 28.27814 8.24605 166.4275 25.87814 -4.180802 165.9152 25.34821 4.794024 166.3796 38.17814 -4.123377 166.3269 38.72246 -4.219372 166.2866 25.36646 -4.235976 166.0606 38.77814 4.928598 166.3161 35.77814 -3.028696 166.5815 28.27814 -3.028696 166.5815 35.77814 3.628699 166.5815 28.27814 3.628699 166.5815 38.02653 8.179426 166.7433 26.00034 8.083174 167.332 38.62067 7.585125 167.6091 25.57104 7.920317 166.6534 35.77814 -8.411052 167.7622 38.17814 -3.525975 167.8689 38.11277 -3.011915 166.8288 25.94352 -3.011917 166.8288 38.11277 3.61192 166.8288 25.94352 3.611916 166.8288 25.87814 4.328235 167.5058 38.57814 4.731636 166.2814 38.17814 4.496126 167.1256 25.87814 -3.728233 167.5058 25.33356 4.221333 167.8765 38.76428 -7.417995 168.4986 38.49097 -7.897983 168.5069 25.47814 -3.597118 167.75 38.57814 4.197111 167.75 38.08751 -8.178496 168.4843 25.56532 -7.936268 168.4004 38.6935 -3.627625 167.8149 25.27814 -3.770318 167.85 38.77814 4.370321 167.85 38.76391 6.293415 169.632 25.95431 -8.150033 168.5433 35.77814 7.704381 168.529 28.27814 7.704381 168.529 35.77814 -2.301005 167.8419 35.77814 -7.562772 169.8791 28.27814 -2.301006 167.8419 28.27814 -7.562772 169.8791 35.77814 2.901008 167.8419 35.77814 6.617811 170.4076 28.27814 2.901008 167.8419 25.58384 7.305266 168.8174 38.03884 7.535624 168.9247 38.11324 -2.25697 167.9905 25.94304 -2.256958 167.9906 38.11324 2.85696 167.9906 25.94305 2.856971 167.9905 38.17814 -2.411854 169.0911 25.87814 3.331631 168.8255 38.17814 3.625541 168.5316 25.87814 -2.731629 168.8255 25.47654 3.197867 168.9443 25.34383 -2.668942 168.9721 38.68806 3.234655 168.9819 25.27814 3.321103 169.1004 25.30822 6.174734 169.8387 35.77814 -1.123576 168.6974 35.77814 -4.438489 173.1516 35.77814 -6.209622 171.7149 28.27814 -1.123577 168.6974 28.27814 -6.209622 171.7149 28.27814 -4.438489 173.1516 35.77814 1.72358 168.6974 35.779 4.70765 172.2153 28.27814 1.723579 168.6974 28.27814 6.617811 170.4076 28.27814 5.066304 171.9251 38.71094 -2.606199 169.0228 25.31297 -5.980931 171.0627 38.76014 4.204926 171.6492 38.11381 -1.115997 168.7777 25.94248 -1.115986 168.7777 38.1138 1.715988 168.7777 25.94248 1.716001 168.7777 38.51517 6.637449 169.8953 26.01421 6.883651 170.0531 38.17814 -0.9314817 169.8282 25.87814 1.925588 169.6961 38.17814 2.305824 169.5282 35.77814 0.3000015 169 35.77814 -2.363098 174.097 35.77814 0.3028551 174.0926 28.27814 0.3000012 169 28.27834 -0.1371376 174.4593 28.27814 -2.363098 174.097 28.27814 3.164037 172.9697 38.12226 0.3000024 169.0871 25.93402 0.3000003 169.0871 25.87814 -1.325586 169.6961 38.61136 -6.323671 171.0196 38.17814 0.7152093 169.9808 25.87814 0.3000011 170 25.34254 -1.316627 169.775 38.6889 1.871165 169.7748 25.34257 1.914181 169.7757 38.70992 -1.230341 169.8016 25.30513 -2.960326 173.1307 25.30147 4.06513 171.7385 38.08767 -6.716418 171.1197 25.56532 -6.51178 170.9633 25.58161 5.600152 171.15 25.96297 -6.656538 171.1928 38.06662 5.762481 171.3348 25.35192 0.3000162 170.0762 38.57814 0.3000017 170 38.77814 0.3000017 170.2 38.74879 -0.4003186 173.6937 38.76149 -2.783406 173.194 25.29055 -0.241218 173.5261 38.71922 -5.133652 171.8643 26.02975 5.306841 171.7099 38.49972 4.509847 171.9855 25.56418 3.735135 172.4005 38.49096 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146.1829 34.72216 5.290546 146.0472 34.72817 7.98331 144.4762 34.72814 6.217855 146.5951 34.72817 8.290052 144.7846 39.42456 4.755291 146.0449 39.42523 5.712848 146.1959 39.42735 4.064019 146.2901 40.93266 7.227508 145.4743 34.76818 8.299643 148.8206 37.80667 8.344603 147.6168 38.52814 10.60866 145.3851 38.52814 10.8 145.347 41.02814 10.8 145.347 41.02814 10.55 145.414 38.52814 11.3 145.347 41.02814 11.3 145.347 41.02814 11.8 145.847 41.02814 10.3 145.847 38.3368 10.62322 145.4203 38.3368 10.8 145.3851 38.3368 11.3 145.3851 38.52814 11.49134 145.3851 41.02814 11.55 145.414 41.02814 11.73301 145.597 38.40188 10.45366 145.5055 38.28289 10.8 145.4211 38.28289 11.3 145.4211 38.3368 11.47678 145.4203 38.40188 11.64153 145.5007 41.02814 10.36699 145.597 38.17459 10.6647 145.5204 38.17459 10.55 145.597 38.17459 11.4353 145.5204 38.17459 10.8 145.4935 38.17459 11.3 145.4935 38.10223 10.8 145.6018 38.10223 11.3 145.6018 38.3368 10.37322 145.6703 38.17459 10.47336 145.7117 38.05945 10.65632 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3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 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