From 5c254697375d599a5a32530ec91cac58df8b53a7 Mon Sep 17 00:00:00 2001
From: Arnav G <73305591+arnavg115@users.noreply.github.com>
Date: Mon, 7 Oct 2024 04:28:03 +0000
Subject: [PATCH 01/14] Added draw triangle with success condition

---
 mani_skill/envs/tasks/drawing/__init__.py     |   1 +
 .../envs/tasks/drawing/draw_triangle.py       | 301 ++++++++++++++++++
 .../motionplanning/panda_stick/run.py         |  92 ++++++
 .../panda_stick/solutions/__init__.py         |   1 +
 .../panda_stick/solutions/draw_triangle.py    |  41 +++
 5 files changed, 436 insertions(+)
 create mode 100644 mani_skill/envs/tasks/drawing/draw_triangle.py
 create mode 100644 mani_skill/examples/motionplanning/panda_stick/run.py
 create mode 100644 mani_skill/examples/motionplanning/panda_stick/solutions/__init__.py
 create mode 100644 mani_skill/examples/motionplanning/panda_stick/solutions/draw_triangle.py

diff --git a/mani_skill/envs/tasks/drawing/__init__.py b/mani_skill/envs/tasks/drawing/__init__.py
index c6df1f738..f90b9a236 100644
--- a/mani_skill/envs/tasks/drawing/__init__.py
+++ b/mani_skill/envs/tasks/drawing/__init__.py
@@ -1 +1,2 @@
 from .draw import *
+from .draw_triangle import *
\ No newline at end of file
diff --git a/mani_skill/envs/tasks/drawing/draw_triangle.py b/mani_skill/envs/tasks/drawing/draw_triangle.py
new file mode 100644
index 000000000..8275c90b0
--- /dev/null
+++ b/mani_skill/envs/tasks/drawing/draw_triangle.py
@@ -0,0 +1,301 @@
+import random
+from typing import Dict
+
+import numpy as np
+import sapien
+import torch
+from transforms3d.euler import euler2quat
+
+from mani_skill.agents.robots.panda.panda_stick import PandaStick
+from mani_skill.envs.sapien_env import BaseEnv
+from mani_skill.sensors.camera import CameraConfig
+from mani_skill.utils import sapien_utils
+from mani_skill.utils.registration import register_env
+from mani_skill.utils.scene_builder.table.scene_builder import TableSceneBuilder
+from mani_skill.utils.structs.actor import Actor
+from mani_skill.utils.structs.pose import Pose
+from mani_skill.utils.structs.types import SceneConfig, SimConfig
+import math
+
+@register_env("DrawTriangle-v1", max_episode_steps=1000)
+class DrawTriangle(BaseEnv):
+    """
+    This is a simple environment demonstrating drawing simulation on a table with a robot arm. There are no success/rewards defined, users can use this code as a starting point
+    to create their own drawing type tasks.
+    """
+
+    MAX_DOTS = 1010
+    """
+    The total "ink" available to use and draw with before you need to call env.reset. NOTE that on GPU simulation it is not recommended to have a very high value for this as it can slow down rendering
+    when too many objects are being rendered in many scenes.
+    """
+    DOT_THICKNESS = 0.003
+    """thickness of the paint drawn on to the canvas"""
+    CANVAS_THICKNESS = 0.02
+    """How thick the canvas on the table is"""
+    BRUSH_RADIUS = 0.01
+    """The brushes radius"""
+    BRUSH_COLORS = [[0.8, 0.2, 0.2, 1]]
+    """The colors of the brushes. If there is more than one color, each parallel environment will have a randomly sampled color."""
+    THRESHOLD = 0.1
+
+
+    SUPPORTED_REWARD_MODES = ["none"]
+
+    SUPPORTED_ROBOTS: ["panda_stick"] # type: ignore
+    agent: PandaStick
+
+    def __init__(self, *args, robot_uids="panda_stick", **kwargs):
+        super().__init__(*args, robot_uids=robot_uids, **kwargs)
+
+    @property
+    def _default_sim_config(self):
+        # we set contact_offset to a small value as we are not expecting to make any contacts really apart from the brush hitting the canvas too hard.
+        # We set solver iterations very low as this environment is not doing a ton of manipulation (the brush is attached to the robot after all)
+        return SimConfig(
+            sim_freq=100,
+            control_freq=20,
+            scene_config=SceneConfig(
+                contact_offset=0.01,
+                solver_position_iterations=4,
+                solver_velocity_iterations=0,
+            ),
+        )
+
+    @property
+    def _default_sensor_configs(self):
+        pose = sapien_utils.look_at(eye=[0.3, 0, 0.8], target=[0, 0, 0.1])
+        return [
+            CameraConfig(
+                "base_camera",
+                pose=pose,
+                width=320,
+                height=240,
+                fov=1.2,
+                near=0.01,
+                far=100,
+            )
+        ]
+
+    @property
+    def _default_human_render_camera_configs(self):
+        pose = sapien_utils.look_at(eye=[0.3, 0, 0.8], target=[0, 0, 0.1])
+        return CameraConfig(
+            "render_camera",
+            pose=pose,
+            width=1280,
+            height=960,
+            fov=1.2,
+            near=0.01,
+            far=100,
+        )
+
+    def _load_scene(self, options: dict):
+        
+        self.table_scene = TableSceneBuilder(self, robot_init_qpos_noise=0)
+        self.table_scene.build()
+        def create_goal_triangle(name="tri", base_color=None):
+           
+            box1_half_w = 0.3 / 2
+            box1_half_h = 0.01 / 2
+            half_thickness = 0.04 / 2 
+
+            # change, rn too lazy to import pi correctly
+            pi = 3.1415926
+
+            # approx div by sqrt(3)
+            radius = (box1_half_w) / 1.73205080757
+
+            theta = random.random() * (2/3 * pi)
+
+            # define centers and compute verticies, might need to adjust how centers are calculated or add a theta arg for variation
+            c1 = np.array([radius* math.cos(theta),radius* math.sin(theta), 0.01])
+            c2 = np.array([radius * math.cos(theta + (2* pi/3)), radius * math.sin(theta + (2* pi /3)), 0.01])
+            c3 = np.array([radius * math.cos((theta+ (4*pi/3))), radius * math.sin(theta + (4*pi/3)), 0.01])
+            self.v1 = (c1 + c3) - c2
+            self.v2 = (c1 + c2) - c3
+            self.v3 = (c2 + c3) - c1
+
+            builder = self.scene.create_actor_builder()
+            first_block_pose = sapien.Pose(list(c1), euler2quat(0,0,theta - (pi/2)))
+            first_block_size = [box1_half_w, box1_half_h, half_thickness]
+            builder.add_box_visual(
+                pose=first_block_pose,
+                half_size=first_block_size,
+                material=sapien.render.RenderMaterial(
+                    base_color=base_color,
+                ),
+            )
+            
+            second_block_pose = sapien.Pose(list(c2), euler2quat(0,0,theta-(5*pi/6)))
+            second_block_size = [box1_half_w, box1_half_h, half_thickness]
+                # builder.add_box_collision(pose=second_block_pose, half_size=second_block_size)
+            builder.add_box_visual(
+                pose=second_block_pose,
+                half_size=second_block_size,
+                material=sapien.render.RenderMaterial(
+                    base_color=base_color,
+                ),
+            )
+
+            third_block_pose = sapien.Pose(list(c3),euler2quat(0,0,theta -(pi/6)))
+            third_block_size = [box1_half_w, box1_half_h, half_thickness]
+                # builder.add_box_collision(pose=second_block_pose, half_size=second_block_size)
+            builder.add_box_visual(
+                pose=third_block_pose,
+                half_size=third_block_size,
+                material=sapien.render.RenderMaterial(
+                    base_color=base_color,
+                ),
+            )
+            return builder.build_kinematic(name=name)
+        
+       
+        # build a white canvas on the table
+        self.canvas = self.scene.create_actor_builder()
+        self.canvas.add_box_visual(
+            half_size=[0.4, 0.6, self.CANVAS_THICKNESS / 2],
+            material=sapien.render.RenderMaterial(base_color=[1, 1, 1, 1]),
+        )
+        self.canvas.add_box_collision(
+            half_size=[0.4, 0.6, self.CANVAS_THICKNESS / 2],
+        )
+        self.canvas.initial_pose = sapien.Pose(p=[-0.1, 0, self.CANVAS_THICKNESS / 2])
+        self.canvas = self.canvas.build_static(name="canvas")
+
+        self.dots = []
+        self.dot_pos = []
+        color_choices = torch.randint(0, len(self.BRUSH_COLORS), (self.num_envs,))
+        for i in range(self.MAX_DOTS):
+            actors = []
+            if len(self.BRUSH_COLORS) > 1:
+                for env_idx in range(self.num_envs):
+                    builder = self.scene.create_actor_builder()
+                    builder.add_cylinder_visual(
+                        radius=self.BRUSH_RADIUS,
+                        half_length=self.DOT_THICKNESS / 2,
+                        material=sapien.render.RenderMaterial(
+                            base_color=self.BRUSH_COLORS[color_choices[env_idx]]
+                        ),
+                    )
+                    builder.set_scene_idxs([env_idx])
+                    actor = builder.build_kinematic(name=f"dot_{i}_{env_idx}")
+                    actors.append(actor)
+                self.dots.append(Actor.merge(actors))
+            else:
+                builder = self.scene.create_actor_builder()
+                builder.add_cylinder_visual(
+                    radius=self.BRUSH_RADIUS,
+                    half_length=self.DOT_THICKNESS / 2,
+                    material=sapien.render.RenderMaterial(
+                        base_color=self.BRUSH_COLORS[0]
+                    ),
+                )
+                actor = builder.build_kinematic(name=f"dot_{i}")
+                self.dots.append(actor)
+        self.goal_tee = create_goal_triangle(
+            name="goal_tri",
+            base_color=np.array([255, 255, 255, 255/4]) / 255,
+        )
+
+    def _initialize_episode(self, env_idx: torch.Tensor, options: dict):
+        # NOTE (stao): for simplicity this task cannot handle partial resets
+        self.draw_step = 0
+        with torch.device(self.device):
+            self.table_scene.initialize(env_idx)
+            for dot in self.dots:
+                # initially spawn dots in the table so they aren't seen
+                dot.set_pose(
+                    sapien.Pose(
+                        p=[0, 0, -self.DOT_THICKNESS], q=euler2quat(0, np.pi / 2, 0)
+                    )
+                )
+
+    def _after_control_step(self):
+        if self.gpu_sim_enabled:
+            self.scene._gpu_fetch_all()
+
+        # This is the actual, GPU parallelized, drawing code.
+        # This is not real drawing but seeks to mimic drawing by placing dots on the canvas whenever the robot is close enough to the canvas surface
+        # We do not actually check if the robot contacts the table (although that is possible) and instead use a fast method to check.
+        # We add a 0.005 meter of leeway to make it easier for the robot to get close to the canvas and start drawing instead of having to be super close to the table.
+        robot_touching_table = (
+            self.agent.tcp.pose.p[:, 2]
+            < self.CANVAS_THICKNESS + self.DOT_THICKNESS + 0.005
+        )
+        robot_brush_pos = torch.zeros((self.num_envs, 3), device=self.device)
+        robot_brush_pos[:, 2] = -self.DOT_THICKNESS
+        robot_brush_pos[robot_touching_table, :2] = self.agent.tcp.pose.p[
+            robot_touching_table, :2
+        ]
+        robot_brush_pos[robot_touching_table, 2] = (
+            self.DOT_THICKNESS / 2 + self.CANVAS_THICKNESS
+        )
+        # move the next unused dot to the robot's brush position. All unused dots are initialized inside the table so they aren't visible
+        new_dot_pos = Pose.create_from_pq(robot_brush_pos, euler2quat(0, np.pi / 2, 0))
+        self.dots[self.draw_step].set_pose(
+            new_dot_pos
+        )
+        if new_dot_pos.get_p()[:,-1] > 0:
+            self.dot_pos.append(new_dot_pos.get_p())
+
+        self.draw_step += 1
+
+        # on GPU sim we have to call _gpu_apply_all() to apply the changes we make to object poses.
+        if self.gpu_sim_enabled:
+            self.scene._gpu_apply_all()
+
+    def evaluate(self):
+        out = self.success_check()
+        return { "success" : out}
+
+    def _get_obs_extra(self, info: Dict):
+        return dict(
+            tcp_pose=self.agent.tcp.pose.raw_pose,
+        )
+
+    def generate_triangle_with_points(self, n, vertices, interpolate = 0):
+        # Create an empty array to hold the points, starting with the vertices
+        all_points = []
+
+        # Loop through each side of the triangle and generate `n` points on each side
+        for i in range(len(vertices)):
+            start_vertex = vertices[i]
+            end_vertex = vertices[(i+1) % len(vertices)]
+
+            # Generate n+1 points between start and end vertex, including the start point but excluding the end point
+            intermediate_points = np.linspace(start_vertex, end_vertex, n+2)[:-1]
+
+            # Add the points to the all_points list
+            all_points.extend(intermediate_points)
+
+        # Convert the list of points to a numpy array
+        all_points = np.array(all_points)
+
+        for i in range(interpolate):
+            ind = i = np.random.randint(0, all_points.shape[0] - 1)
+            p1 = all_points[ind]
+            p2 = all_points[ind + 1]
+            p_interpolated = (0.5) * p1 + 0.5 * p2
+            all_points = np.insert(all_points, ind + 1, p_interpolated, axis=0)
+            
+
+        return all_points
+
+    def success_check(self):
+        if len(self.dot_pos) == 0:
+            return torch.Tensor([False]).to(bool)
+        d_points = []
+        for pos in self.dot_pos:
+            d_points.append(pos.cpu().numpy()[:,:-1])
+        
+        drawn_pts = np.vstack(d_points)
+        
+        target_points = self.generate_triangle_with_points(100, [self.v1[:-1], self.v2[:-1], self.v3[:-1]],0)
+        distance_matrix = np.sqrt(np.sum((drawn_pts[:, None, :] - target_points[None, :, :]) ** 2, axis=-1))
+        Y_closeness = np.min(distance_matrix, axis=0) < self.THRESHOLD
+
+        return torch.Tensor([np.all(Y_closeness)]).to(bool)
+
+
+
diff --git a/mani_skill/examples/motionplanning/panda_stick/run.py b/mani_skill/examples/motionplanning/panda_stick/run.py
new file mode 100644
index 000000000..5066fbc38
--- /dev/null
+++ b/mani_skill/examples/motionplanning/panda_stick/run.py
@@ -0,0 +1,92 @@
+import argparse
+import gymnasium as gym
+import numpy as np
+from tqdm import tqdm
+import os.path as osp
+from mani_skill.utils.wrappers.record import RecordEpisode
+from mani_skill.examples.motionplanning.panda_stick.solutions import solveDrawTriangle
+
+MP_SOLUTIONS = {
+    "DrawTriangle-v1" : solveDrawTriangle
+}
+def parse_args(args=None):
+    parser = argparse.ArgumentParser()
+    parser.add_argument("-e", "--env-id", type=str, default="DrawTriangle-v1", help=f"Environment to run motion planning solver on. Available options are {list(MP_SOLUTIONS.keys())}")
+    parser.add_argument("-o", "--obs-mode", type=str, default="none", help="Observation mode to use. Usually this is kept as 'none' as observations are not necesary to be stored, they can be replayed later via the mani_skill.trajectory.replay_trajectory script.")
+    parser.add_argument("-n", "--num-traj", type=int, default=10, help="Number of trajectories to generate.")
+    parser.add_argument("--only-count-success", action="store_true", help="If true, generates trajectories until num_traj of them are successful and only saves the successful trajectories/videos")
+    parser.add_argument("--reward-mode", type=str)
+    parser.add_argument("-b", "--sim-backend", type=str, default="auto", help="Which simulation backend to use. Can be 'auto', 'cpu', 'gpu'")
+    parser.add_argument("--render-mode", type=str, default="rgb_array", help="can be 'sensors' or 'rgb_array' which only affect what is saved to videos")
+    parser.add_argument("--vis", action="store_true", help="whether or not to open a GUI to visualize the solution live")
+    parser.add_argument("--save-video", action="store_true", help="whether or not to save videos locally")
+    parser.add_argument("--traj-name", type=str, help="The name of the trajectory .h5 file that will be created.")
+    parser.add_argument("--shader", default="default", type=str, help="Change shader used for rendering. Default is 'default' which is very fast. Can also be 'rt' for ray tracing and generating photo-realistic renders. Can also be 'rt-fast' for a faster but lower quality ray-traced renderer")
+    parser.add_argument("--record-dir", type=str, default="demos", help="where to save the recorded trajectories")
+    return parser.parse_args()
+
+def main(args):
+    env_id = args.env_id
+    env = gym.make(
+        env_id,
+        obs_mode=args.obs_mode,
+        control_mode="pd_joint_pos",
+        render_mode="rgb_array",
+    )
+    if env_id not in MP_SOLUTIONS:
+        raise RuntimeError(f"No already written motion planning solutions for {env_id}. Available options are {list(MP_SOLUTIONS.keys())}")
+    env = RecordEpisode(
+        env,
+        output_dir=osp.join(args.record_dir, env_id, "motionplanning"),
+        trajectory_name=args.traj_name, save_video=args.save_video,
+        source_type="motionplanning",
+        source_desc="official motion planning solution from ManiSkill contributors",
+        video_fps=30,
+        save_on_reset=False
+    )
+    solve = MP_SOLUTIONS[env_id]
+    print(f"Motion Planning Running on {env_id}")
+    pbar = tqdm(range(args.num_traj))
+    seed = 0
+    successes = []
+    solution_episode_lengths = []
+    failed_motion_plans = 0
+    passed = 0
+    while True:
+        res = solve(env, seed=seed, debug=False, vis=True if args.vis else False)
+        if res == -1:
+            success = False
+            failed_motion_plans += 1
+        else:
+            success = res[-1]["success"].item()
+            elapsed_steps = res[-1]["elapsed_steps"].item()
+            solution_episode_lengths.append(elapsed_steps)
+        successes.append(success)
+        if args.only_count_success and not success:
+            seed += 1
+            env.flush_trajectory(save=False)
+            if args.save_video:
+                env.flush_video(save=False)
+            continue
+        else:
+            env.flush_trajectory()
+            if args.save_video:
+                env.flush_video()
+            pbar.update(1)
+            pbar.set_postfix(
+                dict(
+                    success_rate=np.mean(successes),
+                    failed_motion_plan_rate=failed_motion_plans / (seed + 1),
+                    avg_episode_length=np.mean(solution_episode_lengths),
+                    max_episode_length=np.max(solution_episode_lengths),
+                    # min_episode_length=np.min(solution_episode_lengths)
+                )
+            )
+            seed += 1
+            passed += 1
+            if passed == args.num_traj:
+                break
+    env.close()
+
+if __name__ == "__main__":
+    main(parse_args())
\ No newline at end of file
diff --git a/mani_skill/examples/motionplanning/panda_stick/solutions/__init__.py b/mani_skill/examples/motionplanning/panda_stick/solutions/__init__.py
new file mode 100644
index 000000000..8186dd53f
--- /dev/null
+++ b/mani_skill/examples/motionplanning/panda_stick/solutions/__init__.py
@@ -0,0 +1 @@
+from .draw_triangle import solve as solveDrawTriangle
\ No newline at end of file
diff --git a/mani_skill/examples/motionplanning/panda_stick/solutions/draw_triangle.py b/mani_skill/examples/motionplanning/panda_stick/solutions/draw_triangle.py
new file mode 100644
index 000000000..1a3086e72
--- /dev/null
+++ b/mani_skill/examples/motionplanning/panda_stick/solutions/draw_triangle.py
@@ -0,0 +1,41 @@
+import numpy as np
+import sapien
+from PIL import Image
+
+from mani_skill.envs.tasks import PushCubeEnv
+from mani_skill.examples.motionplanning.panda_stick.motionplanner import \
+    PandaStickMotionPlanningSolver
+
+def solve(env: PushCubeEnv, seed=None, debug=False, vis=False):
+    env.reset(seed=seed)
+    planner = PandaStickMotionPlanningSolver(
+        env,
+        debug=debug,
+        vis=vis,
+        base_pose=env.unwrapped.agent.robot.pose,
+        visualize_target_grasp_pose=vis,
+        print_env_info=False,
+    )
+
+    FINGER_LENGTH = 0.025
+    env = env.unwrapped
+
+
+    rot = list(env.agent.tcp.pose.get_q()[0].cpu().numpy())
+    reach_pose = sapien.Pose(p=list(env.v1), q = rot)
+    res = planner.move_to_pose_with_screw(reach_pose)
+    # -------------------------------------------------------------------------- #
+    # Move to second vertex
+    # -------------------------------------------------------------------------- #
+
+    reach_pose = sapien.Pose(p=list(env.v2), q = rot)
+    res = planner.move_to_pose_with_screw(reach_pose)
+    
+    reach_pose = sapien.Pose(p=list(env.v3), q = rot)
+    res = planner.move_to_pose_with_screw(reach_pose)
+
+    reach_pose = sapien.Pose(p=list(env.v1), q = rot)
+    res = planner.move_to_pose_with_screw(reach_pose)
+
+    planner.close()
+    return res
\ No newline at end of file

From f1e4826255329941211d19326f6d85cbb14ef6b8 Mon Sep 17 00:00:00 2001
From: Arnav G <73305591+arnavg115@users.noreply.github.com>
Date: Thu, 10 Oct 2024 01:08:05 +0000
Subject: [PATCH 02/14] parallelized progress

---
 .../envs/tasks/drawing/draw_triangle.py       | 89 ++++++++++---------
 .../panda_stick/solutions/draw_triangle.py    |  8 +-
 2 files changed, 50 insertions(+), 47 deletions(-)

diff --git a/mani_skill/envs/tasks/drawing/draw_triangle.py b/mani_skill/envs/tasks/drawing/draw_triangle.py
index 8275c90b0..26ad7cda8 100644
--- a/mani_skill/envs/tasks/drawing/draw_triangle.py
+++ b/mani_skill/envs/tasks/drawing/draw_triangle.py
@@ -5,7 +5,10 @@
 import sapien
 import torch
 from transforms3d.euler import euler2quat
+from transforms3d.quaternions import quat2mat
 
+import mani_skill.envs.utils.randomization as randomization
+from mani_skill.utils.geometry.rotation_conversions import quaternion_to_matrix
 from mani_skill.agents.robots.panda.panda_stick import PandaStick
 from mani_skill.envs.sapien_env import BaseEnv
 from mani_skill.sensors.camera import CameraConfig
@@ -106,15 +109,13 @@ def create_goal_triangle(name="tri", base_color=None):
             # approx div by sqrt(3)
             radius = (box1_half_w) / 1.73205080757
 
-            theta = random.random() * (2/3 * pi)
+            theta = pi/2
 
             # define centers and compute verticies, might need to adjust how centers are calculated or add a theta arg for variation
             c1 = np.array([radius* math.cos(theta),radius* math.sin(theta), 0.01])
             c2 = np.array([radius * math.cos(theta + (2* pi/3)), radius * math.sin(theta + (2* pi /3)), 0.01])
             c3 = np.array([radius * math.cos((theta+ (4*pi/3))), radius * math.sin(theta + (4*pi/3)), 0.01])
-            self.v1 = (c1 + c3) - c2
-            self.v2 = (c1 + c2) - c3
-            self.v3 = (c2 + c3) - c1
+            self.original_verts = np.array([(c1 + c3) - c2 , (c1 + c2) - c3, (c2 + c3) - c1])
 
             builder = self.scene.create_actor_builder()
             first_block_pose = sapien.Pose(list(c1), euler2quat(0,0,theta - (pi/2)))
@@ -164,7 +165,7 @@ def create_goal_triangle(name="tri", base_color=None):
         self.canvas = self.canvas.build_static(name="canvas")
 
         self.dots = []
-        self.dot_pos = []
+        self.dot_pos = None
         color_choices = torch.randint(0, len(self.BRUSH_COLORS), (self.num_envs,))
         for i in range(self.MAX_DOTS):
             actors = []
@@ -193,7 +194,7 @@ def create_goal_triangle(name="tri", base_color=None):
                 )
                 actor = builder.build_kinematic(name=f"dot_{i}")
                 self.dots.append(actor)
-        self.goal_tee = create_goal_triangle(
+        self.goal_tri = create_goal_triangle(
             name="goal_tri",
             base_color=np.array([255, 255, 255, 255/4]) / 255,
         )
@@ -202,7 +203,22 @@ def _initialize_episode(self, env_idx: torch.Tensor, options: dict):
         # NOTE (stao): for simplicity this task cannot handle partial resets
         self.draw_step = 0
         with torch.device(self.device):
+            b = len(env_idx)
             self.table_scene.initialize(env_idx)
+            target_pos = torch.zeros((b, 3))
+
+            target_pos[:,:2] = torch.rand((b,2))*0.02-0.1
+            target_pos[:,-1] = 0.01
+            qs = randomization.random_quaternions(b, lock_x=True, lock_y=True)
+            mats = quaternion_to_matrix(qs)
+            self.goal_tri.set_pose(Pose.create_from_pq(p=target_pos, q=qs))
+            self.vertices = torch.from_numpy(np.tile(self.original_verts, (b, 1,1)))  # b, 3, 3
+            self.vertices = torch.matmul(self.vertices.double(), mats.double())
+            self.vertices += target_pos
+            
+
+            self.triangles = self.generate_triangle_with_points(100, self.vertices[:,:,:-1]) 
+
             for dot in self.dots:
                 # initially spawn dots in the table so they aren't seen
                 dot.set_pose(
@@ -237,7 +253,9 @@ def _after_control_step(self):
             new_dot_pos
         )
         if new_dot_pos.get_p()[:,-1] > 0:
-            self.dot_pos.append(new_dot_pos.get_p())
+            if self.dot_pos == None:
+                self.dot_pos = new_dot_pos.get_p()[:, None, :]
+            self.dot_pos = torch.cat((self.dot_pos, new_dot_pos.get_p()[:, None,: ]), dim=1)
 
         self.draw_step += 1
 
@@ -254,48 +272,33 @@ def _get_obs_extra(self, info: Dict):
             tcp_pose=self.agent.tcp.pose.raw_pose,
         )
 
-    def generate_triangle_with_points(self, n, vertices, interpolate = 0):
-        # Create an empty array to hold the points, starting with the vertices
+    def generate_triangle_with_points(self, n, vertices):
+        batch_size = vertices.shape[0]
+    
         all_points = []
 
-        # Loop through each side of the triangle and generate `n` points on each side
-        for i in range(len(vertices)):
-            start_vertex = vertices[i]
-            end_vertex = vertices[(i+1) % len(vertices)]
-
-            # Generate n+1 points between start and end vertex, including the start point but excluding the end point
-            intermediate_points = np.linspace(start_vertex, end_vertex, n+2)[:-1]
-
-            # Add the points to the all_points list
-            all_points.extend(intermediate_points)
-
-        # Convert the list of points to a numpy array
-        all_points = np.array(all_points)
-
-        for i in range(interpolate):
-            ind = i = np.random.randint(0, all_points.shape[0] - 1)
-            p1 = all_points[ind]
-            p2 = all_points[ind + 1]
-            p_interpolated = (0.5) * p1 + 0.5 * p2
-            all_points = np.insert(all_points, ind + 1, p_interpolated, axis=0)
-            
+        for i in range(vertices.shape[1]):
+            start_vertex = vertices[:, i, :] 
+            end_vertex = vertices[:, (i+1) % vertices.shape[1], :] 
+            t = torch.linspace(0, 1, n+2, device=vertices.device)[:-1]  
+            t = t.view(1, -1, 1).repeat(batch_size, 1, 2) 
+            intermediate_points = start_vertex.unsqueeze(1) * (1 - t) + end_vertex.unsqueeze(1) * t 
+            all_points.append(intermediate_points)
+        all_points = torch.cat(all_points, dim=1) 
 
         return all_points
 
     def success_check(self):
-        if len(self.dot_pos) == 0:
+        if self.dot_pos == None or len(self.dot_pos) == 0:
             return torch.Tensor([False]).to(bool)
-        d_points = []
-        for pos in self.dot_pos:
-            d_points.append(pos.cpu().numpy()[:,:-1])
-        
-        drawn_pts = np.vstack(d_points)
-        
-        target_points = self.generate_triangle_with_points(100, [self.v1[:-1], self.v2[:-1], self.v3[:-1]],0)
-        distance_matrix = np.sqrt(np.sum((drawn_pts[:, None, :] - target_points[None, :, :]) ** 2, axis=-1))
-        Y_closeness = np.min(distance_matrix, axis=0) < self.THRESHOLD
-
-        return torch.Tensor([np.all(Y_closeness)]).to(bool)
-
+        drawn_pts = self.dot_pos[:,:,:-1]
 
+        distance_matrix = torch.sqrt(
+            torch.sum(
+                (drawn_pts[:, :, None, :] - self.triangles[:, None, :, :]) ** 2,
+                axis=-1
+            )
+        )
 
+        Y_closeness = torch.min(distance_matrix, dim = 1).values < self.THRESHOLD
+        return torch.Tensor([torch.all(Y_closeness)]).to(bool)
diff --git a/mani_skill/examples/motionplanning/panda_stick/solutions/draw_triangle.py b/mani_skill/examples/motionplanning/panda_stick/solutions/draw_triangle.py
index 1a3086e72..29d7c5d9a 100644
--- a/mani_skill/examples/motionplanning/panda_stick/solutions/draw_triangle.py
+++ b/mani_skill/examples/motionplanning/panda_stick/solutions/draw_triangle.py
@@ -22,19 +22,19 @@ def solve(env: PushCubeEnv, seed=None, debug=False, vis=False):
 
 
     rot = list(env.agent.tcp.pose.get_q()[0].cpu().numpy())
-    reach_pose = sapien.Pose(p=list(env.v1), q = rot)
+    reach_pose = sapien.Pose(p=list(env.vertices[0,0].numpy()), q = rot)
     res = planner.move_to_pose_with_screw(reach_pose)
     # -------------------------------------------------------------------------- #
     # Move to second vertex
     # -------------------------------------------------------------------------- #
 
-    reach_pose = sapien.Pose(p=list(env.v2), q = rot)
+    reach_pose = sapien.Pose(p=list(env.vertices[0,1]), q = rot)
     res = planner.move_to_pose_with_screw(reach_pose)
     
-    reach_pose = sapien.Pose(p=list(env.v3), q = rot)
+    reach_pose = sapien.Pose(p=list(env.vertices[0,2]), q = rot)
     res = planner.move_to_pose_with_screw(reach_pose)
 
-    reach_pose = sapien.Pose(p=list(env.v1), q = rot)
+    reach_pose = sapien.Pose(p=list(env.vertices[0,0]), q = rot)
     res = planner.move_to_pose_with_screw(reach_pose)
 
     planner.close()

From de9b921b471c72c8edd39b8aebb419cf8edb8bda Mon Sep 17 00:00:00 2001
From: Arnav G <73305591+arnavg115@users.noreply.github.com>
Date: Thu, 10 Oct 2024 06:16:22 +0000
Subject: [PATCH 03/14] fixed triangle rotation issues

---
 .../envs/tasks/drawing/draw_triangle.py       | 19 ++++++++++---------
 mani_skill/examples/.gitignore                |  3 ++-
 2 files changed, 12 insertions(+), 10 deletions(-)

diff --git a/mani_skill/envs/tasks/drawing/draw_triangle.py b/mani_skill/envs/tasks/drawing/draw_triangle.py
index 26ad7cda8..41ad88508 100644
--- a/mani_skill/envs/tasks/drawing/draw_triangle.py
+++ b/mani_skill/envs/tasks/drawing/draw_triangle.py
@@ -4,7 +4,7 @@
 import numpy as np
 import sapien
 import torch
-from transforms3d.euler import euler2quat
+from transforms3d.euler import euler2quat, quat2euler
 from transforms3d.quaternions import quat2mat
 
 import mani_skill.envs.utils.randomization as randomization
@@ -40,7 +40,7 @@ class DrawTriangle(BaseEnv):
     """The brushes radius"""
     BRUSH_COLORS = [[0.8, 0.2, 0.2, 1]]
     """The colors of the brushes. If there is more than one color, each parallel environment will have a randomly sampled color."""
-    THRESHOLD = 0.1
+    THRESHOLD = 0.05
 
 
     SUPPORTED_REWARD_MODES = ["none"]
@@ -112,9 +112,9 @@ def create_goal_triangle(name="tri", base_color=None):
             theta = pi/2
 
             # define centers and compute verticies, might need to adjust how centers are calculated or add a theta arg for variation
-            c1 = np.array([radius* math.cos(theta),radius* math.sin(theta), 0.01])
-            c2 = np.array([radius * math.cos(theta + (2* pi/3)), radius * math.sin(theta + (2* pi /3)), 0.01])
-            c3 = np.array([radius * math.cos((theta+ (4*pi/3))), radius * math.sin(theta + (4*pi/3)), 0.01])
+            c1 = np.array([radius* math.cos(theta),radius* math.sin(theta), 0.001])
+            c2 = np.array([radius * math.cos(theta + (2* pi/3)), radius * math.sin(theta + (2* pi /3)), 0.001])
+            c3 = np.array([radius * math.cos((theta+ (4*pi/3))), radius * math.sin(theta + (4*pi/3)), 0.001])
             self.original_verts = np.array([(c1 + c3) - c2 , (c1 + c2) - c3, (c2 + c3) - c1])
 
             builder = self.scene.create_actor_builder()
@@ -196,7 +196,7 @@ def create_goal_triangle(name="tri", base_color=None):
                 self.dots.append(actor)
         self.goal_tri = create_goal_triangle(
             name="goal_tri",
-            base_color=np.array([255, 255, 255, 255/4]) / 255,
+            base_color=np.array([50, 50, 50, 200]) / 255,
         )
 
     def _initialize_episode(self, env_idx: torch.Tensor, options: dict):
@@ -210,10 +210,11 @@ def _initialize_episode(self, env_idx: torch.Tensor, options: dict):
             target_pos[:,:2] = torch.rand((b,2))*0.02-0.1
             target_pos[:,-1] = 0.01
             qs = randomization.random_quaternions(b, lock_x=True, lock_y=True)
-            mats = quaternion_to_matrix(qs)
-            self.goal_tri.set_pose(Pose.create_from_pq(p=target_pos, q=qs))
+            mats = quaternion_to_matrix(qs) 
+            self.goal_tri.set_pose(Pose.create_from_pq(p=target_pos, q= qs))
+
             self.vertices = torch.from_numpy(np.tile(self.original_verts, (b, 1,1)))  # b, 3, 3
-            self.vertices = torch.matmul(self.vertices.double(), mats.double())
+            self.vertices = (mats.double() @ self.vertices.transpose(-1,-2).double()).transpose(-1,-2) # apply rotation matrix
             self.vertices += target_pos
             
 
diff --git a/mani_skill/examples/.gitignore b/mani_skill/examples/.gitignore
index f3e6bd028..d29c740d4 100644
--- a/mani_skill/examples/.gitignore
+++ b/mani_skill/examples/.gitignore
@@ -1 +1,2 @@
-videos/
\ No newline at end of file
+videos/
+demos/
\ No newline at end of file

From 1189e323c542c711afac4a1ce873beee56f8114c Mon Sep 17 00:00:00 2001
From: Arnav G <73305591+arnavg115@users.noreply.github.com>
Date: Mon, 14 Oct 2024 19:34:36 +0000
Subject: [PATCH 04/14] clean up and format

---
 .../envs/tasks/drawing/draw_triangle.py       | 136 ++++++++++--------
 .../motionplanning/panda_stick/run.py         | 104 +++++++++++---
 .../panda_stick/solutions/__init__.py         |   2 +-
 .../panda_stick/solutions/draw_triangle.py    |  17 ++-
 4 files changed, 172 insertions(+), 87 deletions(-)

diff --git a/mani_skill/envs/tasks/drawing/draw_triangle.py b/mani_skill/envs/tasks/drawing/draw_triangle.py
index 41ad88508..5c8c39351 100644
--- a/mani_skill/envs/tasks/drawing/draw_triangle.py
+++ b/mani_skill/envs/tasks/drawing/draw_triangle.py
@@ -1,24 +1,25 @@
+import math
 import random
 from typing import Dict
 
+import mani_skill.envs.utils.randomization as randomization
 import numpy as np
 import sapien
 import torch
-from transforms3d.euler import euler2quat, quat2euler
-from transforms3d.quaternions import quat2mat
-
-import mani_skill.envs.utils.randomization as randomization
-from mani_skill.utils.geometry.rotation_conversions import quaternion_to_matrix
 from mani_skill.agents.robots.panda.panda_stick import PandaStick
 from mani_skill.envs.sapien_env import BaseEnv
 from mani_skill.sensors.camera import CameraConfig
 from mani_skill.utils import sapien_utils
+from mani_skill.utils.geometry.rotation_conversions import quaternion_to_matrix
 from mani_skill.utils.registration import register_env
-from mani_skill.utils.scene_builder.table.scene_builder import TableSceneBuilder
+from mani_skill.utils.scene_builder.table.scene_builder import \
+    TableSceneBuilder
 from mani_skill.utils.structs.actor import Actor
 from mani_skill.utils.structs.pose import Pose
 from mani_skill.utils.structs.types import SceneConfig, SimConfig
-import math
+from transforms3d.euler import euler2quat, quat2euler
+from transforms3d.quaternions import quat2mat
+
 
 @register_env("DrawTriangle-v1", max_episode_steps=1000)
 class DrawTriangle(BaseEnv):
@@ -42,10 +43,9 @@ class DrawTriangle(BaseEnv):
     """The colors of the brushes. If there is more than one color, each parallel environment will have a randomly sampled color."""
     THRESHOLD = 0.05
 
-
     SUPPORTED_REWARD_MODES = ["none"]
 
-    SUPPORTED_ROBOTS: ["panda_stick"] # type: ignore
+    SUPPORTED_ROBOTS: ["panda_stick"]  # type: ignore
     agent: PandaStick
 
     def __init__(self, *args, robot_uids="panda_stick", **kwargs):
@@ -94,31 +94,44 @@ def _default_human_render_camera_configs(self):
         )
 
     def _load_scene(self, options: dict):
-        
+
         self.table_scene = TableSceneBuilder(self, robot_init_qpos_noise=0)
         self.table_scene.build()
+
         def create_goal_triangle(name="tri", base_color=None):
-           
+
             box1_half_w = 0.3 / 2
             box1_half_h = 0.01 / 2
-            half_thickness = 0.04 / 2 
+            half_thickness = 0.04 / 2
 
-            # change, rn too lazy to import pi correctly
-            pi = 3.1415926
+            radius = (box1_half_w) / math.sqrt(3)
 
-            # approx div by sqrt(3)
-            radius = (box1_half_w) / 1.73205080757
-
-            theta = pi/2
+            theta = np.pi / 2
 
             # define centers and compute verticies, might need to adjust how centers are calculated or add a theta arg for variation
-            c1 = np.array([radius* math.cos(theta),radius* math.sin(theta), 0.001])
-            c2 = np.array([radius * math.cos(theta + (2* pi/3)), radius * math.sin(theta + (2* pi /3)), 0.001])
-            c3 = np.array([radius * math.cos((theta+ (4*pi/3))), radius * math.sin(theta + (4*pi/3)), 0.001])
-            self.original_verts = np.array([(c1 + c3) - c2 , (c1 + c2) - c3, (c2 + c3) - c1])
+            c1 = np.array([radius * math.cos(theta), radius * math.sin(theta), 0.001])
+            c2 = np.array(
+                [
+                    radius * math.cos(theta + (2 * np.pi / 3)),
+                    radius * math.sin(theta + (2 * np.pi / 3)),
+                    0.001,
+                ]
+            )
+            c3 = np.array(
+                [
+                    radius * math.cos((theta + (4 * np.pi / 3))),
+                    radius * math.sin(theta + (4 * np.pi / 3)),
+                    0.001,
+                ]
+            )
+            self.original_verts = np.array(
+                [(c1 + c3) - c2, (c1 + c2) - c3, (c2 + c3) - c1]
+            )
 
             builder = self.scene.create_actor_builder()
-            first_block_pose = sapien.Pose(list(c1), euler2quat(0,0,theta - (pi/2)))
+            first_block_pose = sapien.Pose(
+                list(c1), euler2quat(0, 0, theta - (np.pi / 2))
+            )
             first_block_size = [box1_half_w, box1_half_h, half_thickness]
             builder.add_box_visual(
                 pose=first_block_pose,
@@ -127,10 +140,12 @@ def create_goal_triangle(name="tri", base_color=None):
                     base_color=base_color,
                 ),
             )
-            
-            second_block_pose = sapien.Pose(list(c2), euler2quat(0,0,theta-(5*pi/6)))
+
+            second_block_pose = sapien.Pose(
+                list(c2), euler2quat(0, 0, theta - (5 * np.pi / 6))
+            )
             second_block_size = [box1_half_w, box1_half_h, half_thickness]
-                # builder.add_box_collision(pose=second_block_pose, half_size=second_block_size)
+            # builder.add_box_collision(pose=second_block_pose, half_size=second_block_size)
             builder.add_box_visual(
                 pose=second_block_pose,
                 half_size=second_block_size,
@@ -139,9 +154,11 @@ def create_goal_triangle(name="tri", base_color=None):
                 ),
             )
 
-            third_block_pose = sapien.Pose(list(c3),euler2quat(0,0,theta -(pi/6)))
+            third_block_pose = sapien.Pose(
+                list(c3), euler2quat(0, 0, theta - (np.pi / 6))
+            )
             third_block_size = [box1_half_w, box1_half_h, half_thickness]
-                # builder.add_box_collision(pose=second_block_pose, half_size=second_block_size)
+            # builder.add_box_collision(pose=second_block_pose, half_size=second_block_size)
             builder.add_box_visual(
                 pose=third_block_pose,
                 half_size=third_block_size,
@@ -150,8 +167,7 @@ def create_goal_triangle(name="tri", base_color=None):
                 ),
             )
             return builder.build_kinematic(name=name)
-        
-       
+
         # build a white canvas on the table
         self.canvas = self.scene.create_actor_builder()
         self.canvas.add_box_visual(
@@ -207,18 +223,25 @@ def _initialize_episode(self, env_idx: torch.Tensor, options: dict):
             self.table_scene.initialize(env_idx)
             target_pos = torch.zeros((b, 3))
 
-            target_pos[:,:2] = torch.rand((b,2))*0.02-0.1
-            target_pos[:,-1] = 0.01
+            target_pos[:, :2] = torch.rand((b, 2)) * 0.02 - 0.1
+            target_pos[:, -1] = 0.01
             qs = randomization.random_quaternions(b, lock_x=True, lock_y=True)
-            mats = quaternion_to_matrix(qs) 
-            self.goal_tri.set_pose(Pose.create_from_pq(p=target_pos, q= qs))
-
-            self.vertices = torch.from_numpy(np.tile(self.original_verts, (b, 1,1)))  # b, 3, 3
-            self.vertices = (mats.double() @ self.vertices.transpose(-1,-2).double()).transpose(-1,-2) # apply rotation matrix
+            mats = quaternion_to_matrix(qs)
+            self.goal_tri.set_pose(Pose.create_from_pq(p=target_pos, q=qs))
+
+            self.vertices = torch.from_numpy(
+                np.tile(self.original_verts, (b, 1, 1))
+            )  # b, 3, 3
+            self.vertices = (
+                mats.double() @ self.vertices.transpose(-1, -2).double()
+            ).transpose(
+                -1, -2
+            )  # apply rotation matrix
             self.vertices += target_pos
-            
 
-            self.triangles = self.generate_triangle_with_points(100, self.vertices[:,:,:-1]) 
+            self.triangles = self.generate_triangle_with_points(
+                100, self.vertices[:, :, :-1]
+            )
 
             for dot in self.dots:
                 # initially spawn dots in the table so they aren't seen
@@ -250,13 +273,13 @@ def _after_control_step(self):
         )
         # move the next unused dot to the robot's brush position. All unused dots are initialized inside the table so they aren't visible
         new_dot_pos = Pose.create_from_pq(robot_brush_pos, euler2quat(0, np.pi / 2, 0))
-        self.dots[self.draw_step].set_pose(
-            new_dot_pos
-        )
-        if new_dot_pos.get_p()[:,-1] > 0:
+        self.dots[self.draw_step].set_pose(new_dot_pos)
+        if new_dot_pos.get_p()[:, -1] > 0:
             if self.dot_pos == None:
                 self.dot_pos = new_dot_pos.get_p()[:, None, :]
-            self.dot_pos = torch.cat((self.dot_pos, new_dot_pos.get_p()[:, None,: ]), dim=1)
+            self.dot_pos = torch.cat(
+                (self.dot_pos, new_dot_pos.get_p()[:, None, :]), dim=1
+            )
 
         self.draw_step += 1
 
@@ -266,7 +289,7 @@ def _after_control_step(self):
 
     def evaluate(self):
         out = self.success_check()
-        return { "success" : out}
+        return {"success": out}
 
     def _get_obs_extra(self, info: Dict):
         return dict(
@@ -275,31 +298,32 @@ def _get_obs_extra(self, info: Dict):
 
     def generate_triangle_with_points(self, n, vertices):
         batch_size = vertices.shape[0]
-    
+
         all_points = []
 
         for i in range(vertices.shape[1]):
-            start_vertex = vertices[:, i, :] 
-            end_vertex = vertices[:, (i+1) % vertices.shape[1], :] 
-            t = torch.linspace(0, 1, n+2, device=vertices.device)[:-1]  
-            t = t.view(1, -1, 1).repeat(batch_size, 1, 2) 
-            intermediate_points = start_vertex.unsqueeze(1) * (1 - t) + end_vertex.unsqueeze(1) * t 
+            start_vertex = vertices[:, i, :]
+            end_vertex = vertices[:, (i + 1) % vertices.shape[1], :]
+            t = torch.linspace(0, 1, n + 2, device=vertices.device)[:-1]
+            t = t.view(1, -1, 1).repeat(batch_size, 1, 2)
+            intermediate_points = (
+                start_vertex.unsqueeze(1) * (1 - t) + end_vertex.unsqueeze(1) * t
+            )
             all_points.append(intermediate_points)
-        all_points = torch.cat(all_points, dim=1) 
+        all_points = torch.cat(all_points, dim=1)
 
         return all_points
 
     def success_check(self):
         if self.dot_pos == None or len(self.dot_pos) == 0:
             return torch.Tensor([False]).to(bool)
-        drawn_pts = self.dot_pos[:,:,:-1]
+        drawn_pts = self.dot_pos[:, :, :-1]
 
         distance_matrix = torch.sqrt(
             torch.sum(
-                (drawn_pts[:, :, None, :] - self.triangles[:, None, :, :]) ** 2,
-                axis=-1
+                (drawn_pts[:, :, None, :] - self.triangles[:, None, :, :]) ** 2, axis=-1
             )
         )
 
-        Y_closeness = torch.min(distance_matrix, dim = 1).values < self.THRESHOLD
+        Y_closeness = torch.min(distance_matrix, dim=1).values < self.THRESHOLD
         return torch.Tensor([torch.all(Y_closeness)]).to(bool)
diff --git a/mani_skill/examples/motionplanning/panda_stick/run.py b/mani_skill/examples/motionplanning/panda_stick/run.py
index 5066fbc38..a0373cbfc 100644
--- a/mani_skill/examples/motionplanning/panda_stick/run.py
+++ b/mani_skill/examples/motionplanning/panda_stick/run.py
@@ -1,30 +1,88 @@
 import argparse
+import os.path as osp
+
 import gymnasium as gym
 import numpy as np
-from tqdm import tqdm
-import os.path as osp
+from mani_skill.examples.motionplanning.panda_stick.solutions import \
+    solveDrawTriangle
 from mani_skill.utils.wrappers.record import RecordEpisode
-from mani_skill.examples.motionplanning.panda_stick.solutions import solveDrawTriangle
+from tqdm import tqdm
+
+MP_SOLUTIONS = {"DrawTriangle-v1": solveDrawTriangle}
+
 
-MP_SOLUTIONS = {
-    "DrawTriangle-v1" : solveDrawTriangle
-}
 def parse_args(args=None):
     parser = argparse.ArgumentParser()
-    parser.add_argument("-e", "--env-id", type=str, default="DrawTriangle-v1", help=f"Environment to run motion planning solver on. Available options are {list(MP_SOLUTIONS.keys())}")
-    parser.add_argument("-o", "--obs-mode", type=str, default="none", help="Observation mode to use. Usually this is kept as 'none' as observations are not necesary to be stored, they can be replayed later via the mani_skill.trajectory.replay_trajectory script.")
-    parser.add_argument("-n", "--num-traj", type=int, default=10, help="Number of trajectories to generate.")
-    parser.add_argument("--only-count-success", action="store_true", help="If true, generates trajectories until num_traj of them are successful and only saves the successful trajectories/videos")
+    parser.add_argument(
+        "-e",
+        "--env-id",
+        type=str,
+        default="DrawTriangle-v1",
+        help=f"Environment to run motion planning solver on. Available options are {list(MP_SOLUTIONS.keys())}",
+    )
+    parser.add_argument(
+        "-o",
+        "--obs-mode",
+        type=str,
+        default="none",
+        help="Observation mode to use. Usually this is kept as 'none' as observations are not necesary to be stored, they can be replayed later via the mani_skill.trajectory.replay_trajectory script.",
+    )
+    parser.add_argument(
+        "-n",
+        "--num-traj",
+        type=int,
+        default=10,
+        help="Number of trajectories to generate.",
+    )
+    parser.add_argument(
+        "--only-count-success",
+        action="store_true",
+        help="If true, generates trajectories until num_traj of them are successful and only saves the successful trajectories/videos",
+    )
     parser.add_argument("--reward-mode", type=str)
-    parser.add_argument("-b", "--sim-backend", type=str, default="auto", help="Which simulation backend to use. Can be 'auto', 'cpu', 'gpu'")
-    parser.add_argument("--render-mode", type=str, default="rgb_array", help="can be 'sensors' or 'rgb_array' which only affect what is saved to videos")
-    parser.add_argument("--vis", action="store_true", help="whether or not to open a GUI to visualize the solution live")
-    parser.add_argument("--save-video", action="store_true", help="whether or not to save videos locally")
-    parser.add_argument("--traj-name", type=str, help="The name of the trajectory .h5 file that will be created.")
-    parser.add_argument("--shader", default="default", type=str, help="Change shader used for rendering. Default is 'default' which is very fast. Can also be 'rt' for ray tracing and generating photo-realistic renders. Can also be 'rt-fast' for a faster but lower quality ray-traced renderer")
-    parser.add_argument("--record-dir", type=str, default="demos", help="where to save the recorded trajectories")
+    parser.add_argument(
+        "-b",
+        "--sim-backend",
+        type=str,
+        default="auto",
+        help="Which simulation backend to use. Can be 'auto', 'cpu', 'gpu'",
+    )
+    parser.add_argument(
+        "--render-mode",
+        type=str,
+        default="rgb_array",
+        help="can be 'sensors' or 'rgb_array' which only affect what is saved to videos",
+    )
+    parser.add_argument(
+        "--vis",
+        action="store_true",
+        help="whether or not to open a GUI to visualize the solution live",
+    )
+    parser.add_argument(
+        "--save-video",
+        action="store_true",
+        help="whether or not to save videos locally",
+    )
+    parser.add_argument(
+        "--traj-name",
+        type=str,
+        help="The name of the trajectory .h5 file that will be created.",
+    )
+    parser.add_argument(
+        "--shader",
+        default="default",
+        type=str,
+        help="Change shader used for rendering. Default is 'default' which is very fast. Can also be 'rt' for ray tracing and generating photo-realistic renders. Can also be 'rt-fast' for a faster but lower quality ray-traced renderer",
+    )
+    parser.add_argument(
+        "--record-dir",
+        type=str,
+        default="demos",
+        help="where to save the recorded trajectories",
+    )
     return parser.parse_args()
 
+
 def main(args):
     env_id = args.env_id
     env = gym.make(
@@ -34,15 +92,18 @@ def main(args):
         render_mode="rgb_array",
     )
     if env_id not in MP_SOLUTIONS:
-        raise RuntimeError(f"No already written motion planning solutions for {env_id}. Available options are {list(MP_SOLUTIONS.keys())}")
+        raise RuntimeError(
+            f"No already written motion planning solutions for {env_id}. Available options are {list(MP_SOLUTIONS.keys())}"
+        )
     env = RecordEpisode(
         env,
         output_dir=osp.join(args.record_dir, env_id, "motionplanning"),
-        trajectory_name=args.traj_name, save_video=args.save_video,
+        trajectory_name=args.traj_name,
+        save_video=args.save_video,
         source_type="motionplanning",
         source_desc="official motion planning solution from ManiSkill contributors",
         video_fps=30,
-        save_on_reset=False
+        save_on_reset=False,
     )
     solve = MP_SOLUTIONS[env_id]
     print(f"Motion Planning Running on {env_id}")
@@ -88,5 +149,6 @@ def main(args):
                 break
     env.close()
 
+
 if __name__ == "__main__":
-    main(parse_args())
\ No newline at end of file
+    main(parse_args())
diff --git a/mani_skill/examples/motionplanning/panda_stick/solutions/__init__.py b/mani_skill/examples/motionplanning/panda_stick/solutions/__init__.py
index 8186dd53f..8ad0262c7 100644
--- a/mani_skill/examples/motionplanning/panda_stick/solutions/__init__.py
+++ b/mani_skill/examples/motionplanning/panda_stick/solutions/__init__.py
@@ -1 +1 @@
-from .draw_triangle import solve as solveDrawTriangle
\ No newline at end of file
+from .draw_triangle import solve as solveDrawTriangle
diff --git a/mani_skill/examples/motionplanning/panda_stick/solutions/draw_triangle.py b/mani_skill/examples/motionplanning/panda_stick/solutions/draw_triangle.py
index 29d7c5d9a..fbabb3f83 100644
--- a/mani_skill/examples/motionplanning/panda_stick/solutions/draw_triangle.py
+++ b/mani_skill/examples/motionplanning/panda_stick/solutions/draw_triangle.py
@@ -1,10 +1,10 @@
 import numpy as np
 import sapien
-from PIL import Image
-
 from mani_skill.envs.tasks import PushCubeEnv
 from mani_skill.examples.motionplanning.panda_stick.motionplanner import \
     PandaStickMotionPlanningSolver
+from PIL import Image
+
 
 def solve(env: PushCubeEnv, seed=None, debug=False, vis=False):
     env.reset(seed=seed)
@@ -20,22 +20,21 @@ def solve(env: PushCubeEnv, seed=None, debug=False, vis=False):
     FINGER_LENGTH = 0.025
     env = env.unwrapped
 
-
     rot = list(env.agent.tcp.pose.get_q()[0].cpu().numpy())
-    reach_pose = sapien.Pose(p=list(env.vertices[0,0].numpy()), q = rot)
+    reach_pose = sapien.Pose(p=list(env.vertices[0, 0].numpy()), q=rot)
     res = planner.move_to_pose_with_screw(reach_pose)
     # -------------------------------------------------------------------------- #
     # Move to second vertex
     # -------------------------------------------------------------------------- #
 
-    reach_pose = sapien.Pose(p=list(env.vertices[0,1]), q = rot)
+    reach_pose = sapien.Pose(p=list(env.vertices[0, 1]), q=rot)
     res = planner.move_to_pose_with_screw(reach_pose)
-    
-    reach_pose = sapien.Pose(p=list(env.vertices[0,2]), q = rot)
+
+    reach_pose = sapien.Pose(p=list(env.vertices[0, 2]), q=rot)
     res = planner.move_to_pose_with_screw(reach_pose)
 
-    reach_pose = sapien.Pose(p=list(env.vertices[0,0]), q = rot)
+    reach_pose = sapien.Pose(p=list(env.vertices[0, 0]), q=rot)
     res = planner.move_to_pose_with_screw(reach_pose)
 
     planner.close()
-    return res
\ No newline at end of file
+    return res

From 645a74c7818fd608683b75337a491b3a72fa1a42 Mon Sep 17 00:00:00 2001
From: Arnav G <73305591+arnavg115@users.noreply.github.com>
Date: Tue, 15 Oct 2024 09:08:16 +0000
Subject: [PATCH 05/14] rgbd diffusion policy progress

---
 .../diffusion_policy/make_env.py              |   3 +-
 .../diffusion_policy/plain_conv.py            |  57 +++
 .../diffusion_policy/utils.py                 |  97 ++++
 .../baselines/diffusion_policy/train_rgbd.py  | 451 ++++++++++++++++++
 4 files changed, 607 insertions(+), 1 deletion(-)
 create mode 100644 examples/baselines/diffusion_policy/diffusion_policy/plain_conv.py
 create mode 100644 examples/baselines/diffusion_policy/train_rgbd.py

diff --git a/examples/baselines/diffusion_policy/diffusion_policy/make_env.py b/examples/baselines/diffusion_policy/diffusion_policy/make_env.py
index 13b6d0add..d26064b37 100644
--- a/examples/baselines/diffusion_policy/diffusion_policy/make_env.py
+++ b/examples/baselines/diffusion_policy/diffusion_policy/make_env.py
@@ -28,7 +28,8 @@ def thunk():
                 env = CPUGymWrapper(env, ignore_terminations=True, record_metrics=True)
                 if video_dir:
                     env = RecordEpisode(env, output_dir=video_dir, save_trajectory=False, info_on_video=True, source_type="diffusion_policy", source_desc="diffusion_policy evaluation rollout")
-                env = gym.wrappers.FrameStack(env, other_kwargs['obs_horizon'])
+                if env_kwargs["obs_mode"] == "state":
+                    env = gym.wrappers.FrameStack(env, other_kwargs['obs_horizon'])
                 env.action_space.seed(seed)
                 env.observation_space.seed(seed)
                 return env
diff --git a/examples/baselines/diffusion_policy/diffusion_policy/plain_conv.py b/examples/baselines/diffusion_policy/diffusion_policy/plain_conv.py
new file mode 100644
index 000000000..74e0da439
--- /dev/null
+++ b/examples/baselines/diffusion_policy/diffusion_policy/plain_conv.py
@@ -0,0 +1,57 @@
+import torch.nn as nn
+
+def make_mlp(in_channels, mlp_channels, act_builder=nn.ReLU, last_act=True):
+    c_in = in_channels
+    module_list = []
+    for idx, c_out in enumerate(mlp_channels):
+        module_list.append(nn.Linear(c_in, c_out))
+        if last_act or idx < len(mlp_channels) - 1:
+            module_list.append(act_builder())
+        c_in = c_out
+    return nn.Sequential(*module_list)
+
+class PlainConv(nn.Module):
+    def __init__(self,
+                 in_channels=3,
+                 out_dim=256,
+                 pool_feature_map=False,
+                 last_act=True, # True for ConvBody, False for CNN
+                 ):
+        super().__init__()
+        # assume input image size is 64x64
+
+        self.out_dim = out_dim
+        self.cnn = nn.Sequential(
+            nn.Conv2d(in_channels, 16, 3, padding=1, bias=True), nn.ReLU(inplace=True),
+            nn.MaxPool2d(2, 2),  # [32, 32]
+            nn.Conv2d(16, 32, 3, padding=1, bias=True), nn.ReLU(inplace=True),
+            nn.MaxPool2d(2, 2),  # [16, 16]
+            nn.Conv2d(32, 64, 3, padding=1, bias=True), nn.ReLU(inplace=True),
+            nn.MaxPool2d(2, 2),  # [8, 8]
+            nn.Conv2d(64, 128, 3, padding=1, bias=True), nn.ReLU(inplace=True),
+            nn.MaxPool2d(2, 2),  # [4, 4]
+            nn.Conv2d(128, 128, 1, padding=0, bias=True), nn.ReLU(inplace=True),
+        )
+
+        if pool_feature_map:
+            self.pool = nn.AdaptiveMaxPool2d((1, 1))
+            self.fc = make_mlp(128, [out_dim], last_act=last_act)
+        else:
+            self.pool = None
+            self.fc = make_mlp(128 * 4 * 4, [out_dim], last_act=last_act)
+
+        self.reset_parameters()
+
+    def reset_parameters(self):
+        for name, module in self.named_modules():
+            if isinstance(module, (nn.Linear, nn.Conv1d, nn.Conv2d)):
+                if module.bias is not None:
+                    nn.init.zeros_(module.bias)
+
+    def forward(self, image):
+        x = self.cnn(image)
+        if self.pool is not None:
+            x = self.pool(x)
+        x = x.flatten(1)
+        x = self.fc(x)
+        return x
\ No newline at end of file
diff --git a/examples/baselines/diffusion_policy/diffusion_policy/utils.py b/examples/baselines/diffusion_policy/diffusion_policy/utils.py
index af3876fca..ee21344f4 100644
--- a/examples/baselines/diffusion_policy/diffusion_policy/utils.py
+++ b/examples/baselines/diffusion_policy/diffusion_policy/utils.py
@@ -1,7 +1,10 @@
 from torch.utils.data.sampler import Sampler
 import numpy as np
 import torch
+import torch.nn as nn
 from h5py import File, Group, Dataset
+from gymnasium import spaces
+import torch.nn.functional as F
 
 class IterationBasedBatchSampler(Sampler):
     """Wraps a BatchSampler.
@@ -117,3 +120,97 @@ def load_demo_dataset(path, keys=['observations', 'actions'], num_traj=None, con
         else:
             print('Load', target_key, len(dataset[target_key]), type(dataset[target_key][0]))
     return dataset
+def convert_obs(obs, concat_fn, transpose_fn, state_obs_extractor):
+        img_dict = obs['sensor_data']
+        new_img_dict = {
+            key: transpose_fn(
+                concat_fn([v[key] for v in img_dict.values()])
+            ) # (C, H, W) or (B, C, H, W)
+            for key in ['rgb', 'depth']
+        }
+        # if isinstance(new_img_dict['depth'], torch.Tensor): # MS2 vec env uses float16, but gym AsyncVecEnv uses float32
+        #     new_img_dict['depth'] = new_img_dict['depth'].to(torch.float16)
+
+        # Unified version
+        states_to_stack = state_obs_extractor(obs)
+        for j in range(len(states_to_stack)):
+            if states_to_stack[j].dtype == np.float64:
+                states_to_stack[j] = states_to_stack[j].astype(np.float32)
+        try:
+            state = np.hstack(states_to_stack)
+        except: # dirty fix for concat trajectory of states
+            state = np.column_stack(states_to_stack)
+        if state.dtype == np.float64:
+            for x in states_to_stack:
+                print(x.shape, x.dtype)
+            import pdb; pdb.set_trace()
+
+        out_dict = {
+            'state': state,
+            'rgb': new_img_dict['rgb'], 
+            'depth': new_img_dict['depth'],
+        }
+        return out_dict
+
+def build_obs_space(env, depth_dtype, state_obs_extractor):
+        # NOTE: We have to use float32 for gym AsyncVecEnv since it does not support float16, but we can use float16 for MS2 vec env
+        obs_space = env.observation_space
+
+        # Unified version
+        state_dim = sum([v.shape[0] for v in state_obs_extractor(obs_space)])
+
+        single_img_space = next(iter(env.observation_space['image'].values()))
+        h, w, _ = single_img_space['rgb'].shape
+        n_images = len(env.observation_space['image'])
+
+        return spaces.Dict({
+            'state': spaces.Box(-float("inf"), float("inf"), shape=(state_dim,), dtype=np.float32),
+            'rgb': spaces.Box(0, 255, shape=(n_images*3, h,w), dtype=np.uint8),
+            'depth': spaces.Box(-float("inf"), float("inf"), shape=(n_images,h,w), dtype=depth_dtype),
+        })
+
+def build_state_obs_extractor(env_id):
+    env_name = env_id.split('-')[0]
+    if env_name in ['TurnFaucet', 'StackCube']:
+        return lambda obs: list(obs['extra'].values())
+    elif env_name == 'PushChair' or env_name == "PickCube":
+        return lambda obs: list(obs['agent'].values()) + list(obs['extra'].values())
+    else:
+        raise NotImplementedError(f'Please tune state obs by yourself')
+
+
+class RandomShiftsAug(nn.Module):
+    """
+    from https://github.com/facebookresearch/drqv2/blob/main/drqv2.py
+    """
+    def __init__(self, pad):
+        super().__init__()
+        self.pad = pad
+
+    def forward(self, x):
+        n, c, h, w = x.size()
+        assert h == w
+        padding = tuple([self.pad] * 4)
+        x = F.pad(x, padding, 'replicate')
+        eps = 1.0 / (h + 2 * self.pad)
+        arange = torch.linspace(-1.0 + eps,
+                                1.0 - eps,
+                                h + 2 * self.pad,
+                                device=x.device,
+                                dtype=x.dtype)[:h]
+        arange = arange.unsqueeze(0).repeat(h, 1).unsqueeze(2)
+        base_grid = torch.cat([arange, arange.transpose(1, 0)], dim=2)
+        base_grid = base_grid.unsqueeze(0).repeat(n, 1, 1, 1)
+
+        shift = torch.randint(0,
+                              2 * self.pad + 1,
+                              size=(n, 1, 1, 2),
+                              device=x.device,
+                              dtype=x.dtype)
+        shift *= 2.0 / (h + 2 * self.pad)
+
+        grid = base_grid + shift
+        return F.grid_sample(x,
+                             grid,
+                             padding_mode='zeros',
+                             align_corners=False)
\ No newline at end of file
diff --git a/examples/baselines/diffusion_policy/train_rgbd.py b/examples/baselines/diffusion_policy/train_rgbd.py
new file mode 100644
index 000000000..e7c992aa4
--- /dev/null
+++ b/examples/baselines/diffusion_policy/train_rgbd.py
@@ -0,0 +1,451 @@
+ALGO_NAME = 'BC_Diffusion_rgbd_UNet'
+
+import argparse
+from functools import partial
+import os
+import random
+from distutils.util import strtobool
+import time
+import gymnasium as gym
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.optim as optim
+import torch.nn.functional as F
+from torch.utils.tensorboard import SummaryWriter
+from diffusion_policy.evaluate import evaluate
+from diffusion_policy.plain_conv import PlainConv
+from mani_skill.utils import gym_utils
+from mani_skill.utils.wrappers.flatten import FlattenRGBDObservationWrapper
+from mani_skill.utils.registration import REGISTERED_ENVS
+
+from collections import defaultdict, deque
+
+from torch.utils.data.dataset import Dataset
+from torch.utils.data.sampler import RandomSampler, BatchSampler
+from torch.utils.data.dataloader import DataLoader
+from diffusion_policy.utils import IterationBasedBatchSampler, build_state_obs_extractor, convert_obs, worker_init_fn
+from diffusion_policy.make_env import make_eval_envs
+from diffusers.schedulers.scheduling_ddpm import DDPMScheduler
+from diffusers.training_utils import EMAModel
+from diffusers.optimization import get_scheduler
+from diffusion_policy.conditional_unet1d import ConditionalUnet1D
+from dataclasses import dataclass, field
+from typing import Optional, List
+import tyro
+
+from gymnasium.wrappers.frame_stack import FrameStack, LazyFrames
+from gymnasium import spaces
+from gymnasium.spaces import Box
+
+
+@dataclass
+class Args:
+    exp_name: Optional[str] = None
+    """the name of this experiment"""
+    seed: int = 1
+    """seed of the experiment"""
+    torch_deterministic: bool = True
+    """if toggled, `torch.backends.cudnn.deterministic=False`"""
+    cuda: bool = True
+    """if toggled, cuda will be enabled by default"""
+    track: bool = False
+    """if toggled, this experiment will be tracked with Weights and Biases"""
+    wandb_project_name: str = "ManiSkill"
+    """the wandb's project name"""
+    wandb_entity: Optional[str] = None
+    """the entity (team) of wandb's project"""
+    capture_video: bool = True
+    """whether to capture videos of the agent performances (check out `videos` folder)"""
+
+    env_id: str = "PegInsertionSide-v0"
+    """the id of the environment"""
+    demo_path: str = 'data/ms2_official_demos/rigid_body/PegInsertionSide-v0/trajectory.state.pd_ee_delta_pose.h5'
+    """the path of demo dataset (pkl or h5)"""
+    num_demos: Optional[int] = None
+    """number of trajectories to load from the demo dataset"""
+    total_iters: int = 1_000_000
+    """total timesteps of the experiment"""
+    batch_size: int = 1024
+    """the batch size of sample from the replay memory"""
+
+    # Diffusion Policy specific arguments
+    lr: float = 1e-4
+    """the learning rate of the diffusion policy"""
+    obs_horizon: int = 2 # Seems not very important in ManiSkill, 1, 2, 4 work well
+    act_horizon: int = 8 # Seems not very important in ManiSkill, 4, 8, 15 work well
+    pred_horizon: int = 16 # 16->8 leads to worse performance, maybe it is like generate a half image; 16->32, improvement is very marginal
+    diffusion_step_embed_dim: int = 64 # not very important
+    unet_dims: List[int] = field(default_factory=lambda: [64, 128, 256]) # default setting is about ~4.5M params
+    n_groups: int = 8 # jigu says it is better to let each group have at least 8 channels; it seems 4 and 8 are simila
+
+    # Environment/experiment specific arguments
+    max_episode_steps: Optional[int] = None
+    """Change the environments' max_episode_steps to this value. Sometimes necessary if the demonstrations being imitated are too short. Typically the default
+    max episode steps of environments in ManiSkill are tuned lower so reinforcement learning agents can learn faster."""
+    log_freq: int = 1000
+    """the frequency of logging the training metrics"""
+    eval_freq: int = 5000
+    """the frequency of evaluating the agent on the evaluation environments"""
+    save_freq: Optional[int] = None
+    """the frequency of saving the model checkpoints. By default this is None and will only save checkpoints based on the best evaluation metrics."""
+    num_eval_episodes: int = 100
+    """the number of episodes to evaluate the agent on"""
+    num_eval_envs: int = 10
+    """the number of parallel environments to evaluate the agent on"""
+    sim_backend: str = "cpu"
+    """the simulation backend to use for evaluation environments. can be "cpu" or "gpu"""
+    num_dataload_workers: int = 0
+    """the number of workers to use for loading the training data in the torch dataloader"""
+    control_mode: str = 'pd_joint_delta_pos'
+    """the control mode to use for the evaluation environments. Must match the control mode of the demonstration dataset."""
+
+    # additional tags/configs for logging purposes to wandb and shared comparisons with other algorithms
+    demo_type: Optional[str] = None
+
+def reorder_keys(d, ref_dict):
+    out = dict()
+    for k, v in ref_dict.items():
+        if isinstance(v, dict) or isinstance(v, spaces.Dict):
+            out[k] = reorder_keys(d[k], ref_dict[k])
+        else:
+            out[k] = d[k]
+    return out
+
+
+
+
+class SmallDemoDataset_DiffusionPolicy(Dataset): # Load everything into memory
+    def __init__(self, data_path, obs_process_fn, obs_space, num_traj):
+        if data_path[-4:] == '.pkl':
+            raise NotImplementedError()
+        else:
+            from diffusion_policy.utils import load_demo_dataset
+            trajectories = load_demo_dataset(data_path, num_traj=num_traj, concat=False)
+            # trajectories['observations'] is a list of dict, each dict is a traj, with keys in obs_space, values with length L+1
+            # trajectories['actions'] is a list of np.ndarray (L, act_dim)
+
+        print('Raw trajectory loaded, start to pre-process the observations...')
+
+        # Pre-process the observations, make them align with the obs returned by the obs_wrapper
+        obs_traj_dict_list = []
+        for obs_traj_dict in trajectories['observations']:
+            _obs_traj_dict = reorder_keys(obs_traj_dict, obs_space) # key order in demo is different from key order in env obs
+            _obs_traj_dict = obs_process_fn(_obs_traj_dict)
+            _obs_traj_dict['depth'] = torch.Tensor(_obs_traj_dict['depth'].astype(np.float32) / 1024).to(torch.float16)
+            _obs_traj_dict['rgb'] = torch.from_numpy(_obs_traj_dict['rgb']) # still uint8
+            _obs_traj_dict['state'] = torch.from_numpy(_obs_traj_dict['state'])
+            obs_traj_dict_list.append(_obs_traj_dict)
+        trajectories['observations'] = obs_traj_dict_list
+        self.obs_keys = list(_obs_traj_dict.keys())
+        # Pre-process the actions
+        for i in range(len(trajectories['actions'])):
+            trajectories['actions'][i] = torch.Tensor(trajectories['actions'][i])
+        print('Obs/action pre-processing is done, start to pre-compute the slice indices...')
+
+        # Pre-compute all possible (traj_idx, start, end) tuples, this is very specific to Diffusion Policy
+        if 'delta_pos' in args.control_mode or args.control_mode == 'base_pd_joint_vel_arm_pd_joint_vel':
+            self.pad_action_arm = torch.zeros((trajectories['actions'][0].shape[1]-1,))
+            # to make the arm stay still, we pad the action with 0 in 'delta_pos' control mode
+            # gripper action needs to be copied from the last action
+        else:
+            raise NotImplementedError(f'Control Mode {args.control_mode} not supported')
+        self.obs_horizon, self.pred_horizon = obs_horizon, pred_horizon = args.obs_horizon, args.pred_horizon
+        self.slices = []
+        num_traj = len(trajectories['actions'])
+        total_transitions = 0
+        for traj_idx in range(num_traj):
+            L = trajectories['actions'][traj_idx].shape[0]
+            assert trajectories['observations'][traj_idx]['state'].shape[0] == L + 1
+            total_transitions += L
+
+            # |o|o|                             observations: 2
+            # | |a|a|a|a|a|a|a|a|               actions executed: 8
+            # |p|p|p|p|p|p|p|p|p|p|p|p|p|p|p|p| actions predicted: 16
+            pad_before = obs_horizon - 1
+            # Pad before the trajectory, so the first action of an episode is in "actions executed"
+            # obs_horizon - 1 is the number of "not used actions"
+            pad_after = pred_horizon - obs_horizon
+            # Pad after the trajectory, so all the observations are utilized in training
+            # Note that in the original code, pad_after = act_horizon - 1, but I think this is not the best choice
+            self.slices += [
+                (traj_idx, start, start + pred_horizon) for start in range(-pad_before, L - pred_horizon + pad_after)
+            ]  # slice indices follow convention [start, end)
+        
+        print(f"Total transitions: {total_transitions}, Total obs sequences: {len(self.slices)}")
+
+        self.trajectories = trajectories
+
+    def __getitem__(self, index):
+        traj_idx, start, end = self.slices[index]
+        L, act_dim = self.trajectories['actions'][traj_idx].shape
+
+        obs_traj = self.trajectories['observations'][traj_idx]
+        obs_seq = {}
+        for k, v in obs_traj.items():
+            obs_seq[k] = v[max(0, start):start+self.obs_horizon] # start+self.obs_horizon is at least 1
+            if start < 0: # pad before the trajectory
+                pad_obs_seq = torch.stack([obs_seq[k][0]]*abs(start), dim=0)
+                obs_seq[k] = torch.cat((pad_obs_seq, obs_seq[k]), dim=0)
+            # don't need to pad obs after the trajectory, see the above char drawing
+
+        act_seq = self.trajectories['actions'][traj_idx][max(0, start):end]
+        if start < 0: # pad before the trajectory
+            act_seq = torch.cat([act_seq[0].repeat(-start, 1), act_seq], dim=0)
+        if end > L: # pad after the trajectory
+            gripper_action = act_seq[-1, -1] # assume gripper is with pos controller
+            pad_action = torch.cat((self.pad_action_arm, gripper_action[None]), dim=0)
+            act_seq = torch.cat([act_seq, pad_action.repeat(end-L, 1)], dim=0)
+            # making the robot (arm and gripper) stay still
+        assert obs_seq['state'].shape[0] == self.obs_horizon and act_seq.shape[0] == self.pred_horizon
+        return {
+            'observations': obs_seq,
+            'actions': act_seq,
+        }
+
+    def __len__(self):
+        return len(self.slices)
+class DictFrameStack(FrameStack):
+    def __init__(
+        self,
+        env: gym.Env,
+        num_stack: int,
+        lz4_compress: bool = False,
+    ):
+        """Observation wrapper that stacks the observations in a rolling manner.
+
+        Args:
+            env (Env): The environment to apply the wrapper
+            num_stack (int): The number of frames to stack
+            lz4_compress (bool): Use lz4 to compress the frames internally
+        """
+        gym.utils.RecordConstructorArgs.__init__(
+            self, num_stack=num_stack, lz4_compress=lz4_compress
+        )
+        gym.ObservationWrapper.__init__(self, env)
+
+        self.num_stack = num_stack
+        self.lz4_compress = lz4_compress
+
+        self.frames = deque(maxlen=num_stack)
+
+        new_observation_space = gym.spaces.Dict()
+        for k, v in self.observation_space.items():
+            low = np.repeat(v.low[np.newaxis, ...], num_stack, axis=1)
+            high = np.repeat(v.high[np.newaxis, ...], num_stack, axis=1)
+            new_observation_space[k] = Box(
+                low=low, high=high, dtype=v.dtype
+            )
+        self.observation_space = new_observation_space
+
+    def observation(self, observation):
+        """Converts the wrappers current frames to lazy frames.
+
+        Args:
+            observation: Ignored
+
+        Returns:
+            :class:`LazyFrames` object for the wrapper's frame buffer,  :attr:`self.frames`
+        """
+        assert len(self.frames) == self.num_stack, (len(self.frames), self.num_stack)
+        return {
+            k: LazyFrames([x[k] for x in self.frames], self.lz4_compress)
+            for k in self.observation_space.keys()
+        }
+
+class Agent(nn.Module):
+    def __init__(self, env, args):
+        super().__init__()
+        self.obs_horizon = args.obs_horizon
+        self.act_horizon = args.act_horizon
+        self.pred_horizon = args.pred_horizon
+        assert len(env.single_observation_space['state'].shape) == 2 # (obs_horizon, obs_dim)
+        assert len(env.single_action_space.shape) == 1 # (act_dim, )
+        assert (env.single_action_space.high == 1).all() and (env.single_action_space.low == -1).all()
+        # denoising results will be clipped to [-1,1], so the action should be in [-1,1] as well
+        self.act_dim = env.single_action_space.shape[0]
+        obs_state_dim = env.single_observation_space['state'].shape[1]
+        _, C, H, W = envs.single_observation_space['rgb'].shape
+
+        visual_feature_dim = 256
+        self.visual_encoder = PlainConv(in_channels=int(C/3*4), out_dim=visual_feature_dim)
+        self.noise_pred_net = ConditionalUnet1D(
+            input_dim=self.act_dim, # act_horizon is not used (U-Net doesn't care)
+            global_cond_dim=self.obs_horizon * (visual_feature_dim + obs_state_dim),
+            diffusion_step_embed_dim=args.diffusion_step_embed_dim,
+            down_dims=args.unet_dims,
+            n_groups=args.n_groups,
+        )
+        self.num_diffusion_iters = 100
+        self.noise_scheduler = DDPMScheduler(
+            num_train_timesteps=self.num_diffusion_iters,
+            beta_schedule='squaredcos_cap_v2', # has big impact on performance, try not to change
+            clip_sample=True, # clip output to [-1,1] to improve stability
+            prediction_type='epsilon' # predict noise (instead of denoised action)
+        )
+
+        if args.random_shift > 0:
+            from diffusion_policy.utils import RandomShiftsAug
+            self.aug = RandomShiftsAug(args.random_shift)
+
+    def encode_obs(self, obs_seq, eval_mode):
+        rgb = obs_seq['rgb'].float() / 255.0 # (B, obs_horizon, 3*k, H, W)
+        depth = obs_seq['depth'].float() # (B, obs_horizon, 1*k, H, W)
+        img_seq = torch.cat([rgb, depth], dim=2) # (B, obs_horizon, C, H, W), C=4*k
+        img_seq = img_seq.flatten(end_dim=1) # (B*obs_horizon, C, H, W)
+        if hasattr(self, 'aug') and not eval_mode:
+            img_seq = self.aug(img_seq) # (B*obs_horizon, C, H, W)
+        visual_feature = self.visual_encoder(img_seq) # (B*obs_horizon, D)
+        visual_feature = visual_feature.reshape(rgb.shape[0], self.obs_horizon, visual_feature.shape[1]) # (B, obs_horizon, D)
+        feature = torch.cat((visual_feature, obs_seq['state']), dim=-1) # (B, obs_horizon, D+obs_state_dim)
+        return feature.flatten(start_dim=1) # (B, obs_horizon * (D+obs_state_dim))
+    
+    def compute_loss(self, obs_seq, action_seq):
+        B = obs_seq['state'].shape[0]
+
+        # observation as FiLM conditioning
+        obs_cond = self.encode_obs(obs_seq, eval_mode=False) # (B, obs_horizon * obs_dim)
+
+        # sample noise to add to actions
+        noise = torch.randn((B, self.pred_horizon, self.act_dim), device=device)
+
+        # sample a diffusion iteration for each data point
+        timesteps = torch.randint(
+            0, self.noise_scheduler.config.num_train_timesteps,
+            (B,), device=device
+        ).long()
+
+        # add noise to the clean images(actions) according to the noise magnitude at each diffusion iteration
+        # (this is the forward diffusion process)
+        noisy_action_seq = self.noise_scheduler.add_noise(
+            action_seq, noise, timesteps)
+        
+        # predict the noise residual
+        noise_pred = self.noise_pred_net(
+            noisy_action_seq, timesteps, global_cond=obs_cond)
+
+        return F.mse_loss(noise_pred, noise)
+    
+    def get_eval_action(self, obs_seq):
+        # init scheduler
+        # self.noise_scheduler.set_timesteps(self.num_diffusion_iters)
+        # set_timesteps will change noise_scheduler.timesteps is only used in noise_scheduler.step()
+        # noise_scheduler.step() is only called during inference
+        # if we use DDPM, and inference_diffusion_steps == train_diffusion_steps, then we can skip this
+
+        # obs_seq['state']: (B, obs_horizon, obs_state_dim)
+        B = obs_seq['state'].shape[0]
+        with torch.no_grad():
+            obs_cond = self.encode_obs(obs_seq, eval_mode=True) # (B, obs_horizon * obs_dim)
+
+            # initialize action from Guassian noise
+            noisy_action_seq = torch.randn((B, self.pred_horizon, self.act_dim), device=obs_seq['state'].device)
+
+            for k in self.noise_scheduler.timesteps:
+                # predict noise
+                noise_pred = self.noise_pred_net(
+                    sample=noisy_action_seq,
+                    timestep=k,
+                    global_cond=obs_cond,
+                )
+
+                # inverse diffusion step (remove noise)
+                noisy_action_seq = self.noise_scheduler.step(
+                    model_output=noise_pred,
+                    timestep=k,
+                    sample=noisy_action_seq,
+                ).prev_sample
+
+        # only take act_horizon number of actions
+        start = self.obs_horizon - 1
+        end = start + self.act_horizon
+        return noisy_action_seq[:, start:end] # (B, act_horizon, act_dim)
+
+if __name__ == "__main__":
+    args = tyro.cli(Args)
+
+    if args.exp_name is None:
+        args.exp_name = os.path.basename(__file__)[: -len(".py")]
+        run_name = f"{args.env_id}__{args.exp_name}__{args.seed}__{int(time.time())}"
+    else:
+        run_name = args.exp_name
+    
+    if args.demo_path.endswith('.h5'):
+        import json
+        json_file = args.demo_path[:-2] + 'json'
+        with open(json_file, 'r') as f:
+            demo_info = json.load(f)
+            if 'control_mode' in demo_info['env_info']['env_kwargs']:
+                control_mode = demo_info['env_info']['env_kwargs']['control_mode']
+            elif 'control_mode' in demo_info['episodes'][0]:
+                control_mode = demo_info['episodes'][0]['control_mode']
+            else:
+                raise Exception('Control mode not found in json')
+            assert control_mode == args.control_mode, f"Control mode mismatched. Dataset has control mode {control_mode}, but args has control mode {args.control_mode}"
+    assert args.obs_horizon + args.act_horizon - 1 <= args.pred_horizon
+    assert args.obs_horizon >= 1 and args.act_horizon >= 1 and args.pred_horizon >= 1
+
+    random.seed(args.seed)
+    np.random.seed(args.seed)
+    torch.manual_seed(args.seed)
+    torch.backends.cudnn.deterministic = args.torch_deterministic
+
+    device = torch.device("cuda" if torch.cuda.is_available() and args.cuda else "cpu")
+
+    env_kwargs = dict(control_mode=args.control_mode, reward_mode="sparse", obs_mode="rgbd", render_mode="rgb_array")
+    if args.max_episode_steps is not None:
+        env_kwargs["max_episode_steps"] = args.max_episode_steps
+    other_kwargs = dict(obs_horizon=args.obs_horizon)
+    envs = make_eval_envs(args.env_id, args.num_eval_envs, args.sim_backend, env_kwargs, other_kwargs, video_dir=f'runs/{run_name}/videos' if args.capture_video else None, wrappers=[FlattenRGBDObservationWrapper, partial(DictFrameStack, num_stack = args.obs_horizon)])
+    if args.track:
+        import wandb
+        config = vars(args)
+        config["eval_env_cfg"] = dict(**env_kwargs, num_envs=args.num_eval_envs, env_id=args.env_id, env_horizon=gym_utils.find_max_episode_steps_value(envs))
+        wandb.init(
+            project=args.wandb_project_name,
+            entity=args.wandb_entity,
+            sync_tensorboard=True,
+            config=config,
+            name=run_name,
+            save_code=True,
+            group="DiffusionPolicy",
+            tags=["diffusion_policy"]
+        )
+    writer = SummaryWriter(f"runs/{run_name}")
+    writer.add_text(
+        "hyperparameters",
+        "|param|value|\n|-|-|\n%s" % ("\n".join([f"|{key}|{value}|" for key, value in vars(args).items()])),
+    )
+
+    obs_process_fn = partial(
+        convert_obs, 
+        concat_fn=partial(np.concatenate, axis=-1), 
+        transpose_fn=partial(np.transpose, axes=(0, 3, 1, 2)), # (B, H, W, C) -> (B, C, H, W)
+        state_obs_extractor=build_state_obs_extractor(args.env_id),
+    )
+    tmp_env = gym.make(args.env_id, obs_mode='rgbd'); orignal_obs_space = tmp_env.observation_space; tmp_env.close()
+    dataset = SmallDemoDataset_DiffusionPolicy(args.demo_path, obs_process_fn, orignal_obs_space, args.num_demos)
+    sampler = RandomSampler(dataset, replacement=False)
+    batch_sampler = BatchSampler(sampler, batch_size=args.batch_size, drop_last=True)
+    batch_sampler = IterationBasedBatchSampler(batch_sampler, args.total_iters)
+    train_dataloader = DataLoader(
+        dataset,
+        batch_sampler=batch_sampler,
+        num_workers=args.num_dataload_workers,
+        worker_init_fn=lambda worker_id: worker_init_fn(worker_id, base_seed=args.seed),
+        pin_memory=True,
+        persistent_workers=(args.num_dataload_workers > 0),
+    )
+    sampler = RandomSampler(dataset, replacement=False)
+    batch_sampler = BatchSampler(sampler, batch_size=args.batch_size, drop_last=True)
+    batch_sampler = IterationBasedBatchSampler(batch_sampler, args.total_iters)
+    train_dataloader = DataLoader(
+        dataset,
+        batch_sampler=batch_sampler,
+        num_workers=args.num_dataload_workers,
+        worker_init_fn=lambda worker_id: worker_init_fn(worker_id, base_seed=args.seed),
+        pin_memory=True,
+        persistent_workers=(args.num_dataload_workers > 0),
+    )
+
+    agent = Agent(envs, args).to(device)
\ No newline at end of file

From 2bd9cfcf4a944161ee342b37f7661213443f5b2d Mon Sep 17 00:00:00 2001
From: Arnav G <73305591+arnavg115@users.noreply.github.com>
Date: Thu, 17 Oct 2024 01:53:49 +0000
Subject: [PATCH 06/14] diff policy rgbd cpu fixes

---
 .../diffusion_policy/make_env.py              |  56 +++++++
 .../diffusion_policy/plain_conv.py            |   2 +-
 .../baselines/diffusion_policy/train_rgbd.py  | 152 +++++++++++-------
 mani_skill/utils/wrappers/flatten.py          |  10 +-
 4 files changed, 158 insertions(+), 62 deletions(-)

diff --git a/examples/baselines/diffusion_policy/diffusion_policy/make_env.py b/examples/baselines/diffusion_policy/diffusion_policy/make_env.py
index d26064b37..e9e5bf33a 100644
--- a/examples/baselines/diffusion_policy/diffusion_policy/make_env.py
+++ b/examples/baselines/diffusion_policy/diffusion_policy/make_env.py
@@ -1,9 +1,63 @@
+from collections import deque
 from typing import Optional
 import gymnasium as gym
 import mani_skill.envs
 from mani_skill.utils import gym_utils
 from mani_skill.vector.wrappers.gymnasium import ManiSkillVectorEnv
 from mani_skill.utils.wrappers import RecordEpisode, FrameStack, CPUGymWrapper
+import numpy as np
+from gymnasium.spaces import Box
+from gymnasium.wrappers.frame_stack import FrameStack as GymFrameStack
+from gymnasium.wrappers.frame_stack import LazyFrames
+
+
+class DictFrameStack(GymFrameStack):
+    def __init__(
+        self,
+        env: gym.Env,
+        num_stack: int,
+        lz4_compress: bool = False,
+    ):
+        """Observation wrapper that stacks the observations in a rolling manner.
+
+        Args:
+            env (Env): The environment to apply the wrapper
+            num_stack (int): The number of frames to stack
+            lz4_compress (bool): Use lz4 to compress the frames internally
+        """
+        gym.utils.RecordConstructorArgs.__init__(
+            self, num_stack=num_stack, lz4_compress=lz4_compress
+        )
+        gym.ObservationWrapper.__init__(self, env)
+
+        self.num_stack = num_stack
+        self.lz4_compress = lz4_compress
+
+        self.frames = deque(maxlen=num_stack)
+
+        new_observation_space = gym.spaces.Dict()
+        for k, v in self.observation_space.items():
+            low = np.repeat(v.low[np.newaxis, ...], num_stack, axis=0)
+            high = np.repeat(v.high[np.newaxis, ...], num_stack, axis=0)
+            new_observation_space[k] = Box(
+                low=low, high=high, dtype=v.dtype
+            )
+        self.observation_space = new_observation_space
+
+    def observation(self, observation):
+        """Converts the wrappers current frames to lazy frames.
+
+        Args:
+            observation: Ignored
+
+        Returns:
+            :class:`LazyFrames` object for the wrapper's frame buffer,  :attr:`self.frames`
+        """
+        assert len(self.frames) == self.num_stack, (len(self.frames), self.num_stack)
+        return {
+            k: LazyFrames([x[k] for x in self.frames], self.lz4_compress)
+            for k in self.observation_space.keys()
+        }
 
 
 def make_eval_envs(env_id, num_envs: int, sim_backend: str, env_kwargs: dict, other_kwargs: dict, video_dir: Optional[str] = None, wrappers: list[gym.Wrapper] = []):
@@ -30,6 +84,8 @@ def thunk():
                     env = RecordEpisode(env, output_dir=video_dir, save_trajectory=False, info_on_video=True, source_type="diffusion_policy", source_desc="diffusion_policy evaluation rollout")
                 if env_kwargs["obs_mode"] == "state":
                     env = gym.wrappers.FrameStack(env, other_kwargs['obs_horizon'])
+                elif env_kwargs["obs_mode"] == "rgbd":
+                    env = DictFrameStack(env, 2)
                 env.action_space.seed(seed)
                 env.observation_space.seed(seed)
                 return env
diff --git a/examples/baselines/diffusion_policy/diffusion_policy/plain_conv.py b/examples/baselines/diffusion_policy/diffusion_policy/plain_conv.py
index 74e0da439..50c09e59d 100644
--- a/examples/baselines/diffusion_policy/diffusion_policy/plain_conv.py
+++ b/examples/baselines/diffusion_policy/diffusion_policy/plain_conv.py
@@ -38,7 +38,7 @@ def __init__(self,
             self.fc = make_mlp(128, [out_dim], last_act=last_act)
         else:
             self.pool = None
-            self.fc = make_mlp(128 * 4 * 4, [out_dim], last_act=last_act)
+            self.fc = make_mlp(128 * 4 * 4 * 4, [out_dim], last_act=last_act)
 
         self.reset_parameters()
 
diff --git a/examples/baselines/diffusion_policy/train_rgbd.py b/examples/baselines/diffusion_policy/train_rgbd.py
index e7c992aa4..2d7f9719b 100644
--- a/examples/baselines/diffusion_policy/train_rgbd.py
+++ b/examples/baselines/diffusion_policy/train_rgbd.py
@@ -1,6 +1,7 @@
 ALGO_NAME = 'BC_Diffusion_rgbd_UNet'
 
 import argparse
+from collections import defaultdict
 from functools import partial
 import os
 import random
@@ -19,7 +20,6 @@
 from mani_skill.utils.wrappers.flatten import FlattenRGBDObservationWrapper
 from mani_skill.utils.registration import REGISTERED_ENVS
 
-from collections import defaultdict, deque
 
 from torch.utils.data.dataset import Dataset
 from torch.utils.data.sampler import RandomSampler, BatchSampler
@@ -33,10 +33,7 @@
 from dataclasses import dataclass, field
 from typing import Optional, List
 import tyro
-
-from gymnasium.wrappers.frame_stack import FrameStack, LazyFrames
 from gymnasium import spaces
-from gymnasium.spaces import Box
 
 
 @dataclass
@@ -66,7 +63,7 @@ class Args:
     """number of trajectories to load from the demo dataset"""
     total_iters: int = 1_000_000
     """total timesteps of the experiment"""
-    batch_size: int = 1024
+    batch_size: int = 256
     """the batch size of sample from the replay memory"""
 
     # Diffusion Policy specific arguments
@@ -205,53 +202,7 @@ def __getitem__(self, index):
 
     def __len__(self):
         return len(self.slices)
-class DictFrameStack(FrameStack):
-    def __init__(
-        self,
-        env: gym.Env,
-        num_stack: int,
-        lz4_compress: bool = False,
-    ):
-        """Observation wrapper that stacks the observations in a rolling manner.
-
-        Args:
-            env (Env): The environment to apply the wrapper
-            num_stack (int): The number of frames to stack
-            lz4_compress (bool): Use lz4 to compress the frames internally
-        """
-        gym.utils.RecordConstructorArgs.__init__(
-            self, num_stack=num_stack, lz4_compress=lz4_compress
-        )
-        gym.ObservationWrapper.__init__(self, env)
-
-        self.num_stack = num_stack
-        self.lz4_compress = lz4_compress
-
-        self.frames = deque(maxlen=num_stack)
-
-        new_observation_space = gym.spaces.Dict()
-        for k, v in self.observation_space.items():
-            low = np.repeat(v.low[np.newaxis, ...], num_stack, axis=1)
-            high = np.repeat(v.high[np.newaxis, ...], num_stack, axis=1)
-            new_observation_space[k] = Box(
-                low=low, high=high, dtype=v.dtype
-            )
-        self.observation_space = new_observation_space
 
-    def observation(self, observation):
-        """Converts the wrappers current frames to lazy frames.
-
-        Args:
-            observation: Ignored
-
-        Returns:
-            :class:`LazyFrames` object for the wrapper's frame buffer,  :attr:`self.frames`
-        """
-        assert len(self.frames) == self.num_stack, (len(self.frames), self.num_stack)
-        return {
-            k: LazyFrames([x[k] for x in self.frames], self.lz4_compress)
-            for k in self.observation_space.keys()
-        }
 
 class Agent(nn.Module):
     def __init__(self, env, args):
@@ -265,10 +216,10 @@ def __init__(self, env, args):
         # denoising results will be clipped to [-1,1], so the action should be in [-1,1] as well
         self.act_dim = env.single_action_space.shape[0]
         obs_state_dim = env.single_observation_space['state'].shape[1]
-        _, C, H, W = envs.single_observation_space['rgb'].shape
+        _, H, W, C = envs.single_observation_space['rgb'].shape
 
         visual_feature_dim = 256
-        self.visual_encoder = PlainConv(in_channels=int(C/3*4), out_dim=visual_feature_dim)
+        self.visual_encoder = PlainConv(in_channels=int(C/3*4), out_dim=visual_feature_dim, pool_feature_map=True)
         self.noise_pred_net = ConditionalUnet1D(
             input_dim=self.act_dim, # act_horizon is not used (U-Net doesn't care)
             global_cond_dim=self.obs_horizon * (visual_feature_dim + obs_state_dim),
@@ -284,9 +235,6 @@ def __init__(self, env, args):
             prediction_type='epsilon' # predict noise (instead of denoised action)
         )
 
-        if args.random_shift > 0:
-            from diffusion_policy.utils import RandomShiftsAug
-            self.aug = RandomShiftsAug(args.random_shift)
 
     def encode_obs(self, obs_seq, eval_mode):
         rgb = obs_seq['rgb'].float() / 255.0 # (B, obs_horizon, 3*k, H, W)
@@ -326,7 +274,7 @@ def compute_loss(self, obs_seq, action_seq):
 
         return F.mse_loss(noise_pred, noise)
     
-    def get_eval_action(self, obs_seq):
+    def get_action(self, obs_seq):
         # init scheduler
         # self.noise_scheduler.set_timesteps(self.num_diffusion_iters)
         # set_timesteps will change noise_scheduler.timesteps is only used in noise_scheduler.step()
@@ -336,6 +284,9 @@ def get_eval_action(self, obs_seq):
         # obs_seq['state']: (B, obs_horizon, obs_state_dim)
         B = obs_seq['state'].shape[0]
         with torch.no_grad():
+            obs_seq['rgb'] = obs_seq['rgb'].permute(0,1,4,2,3)
+            obs_seq['depth'] = obs_seq['depth'].permute(0,1,4,2,3)
+
             obs_cond = self.encode_obs(obs_seq, eval_mode=True) # (B, obs_horizon * obs_dim)
 
             # initialize action from Guassian noise
@@ -396,7 +347,7 @@ def get_eval_action(self, obs_seq):
     if args.max_episode_steps is not None:
         env_kwargs["max_episode_steps"] = args.max_episode_steps
     other_kwargs = dict(obs_horizon=args.obs_horizon)
-    envs = make_eval_envs(args.env_id, args.num_eval_envs, args.sim_backend, env_kwargs, other_kwargs, video_dir=f'runs/{run_name}/videos' if args.capture_video else None, wrappers=[FlattenRGBDObservationWrapper, partial(DictFrameStack, num_stack = args.obs_horizon)])
+    envs = make_eval_envs(args.env_id, args.num_eval_envs, args.sim_backend, env_kwargs, other_kwargs, video_dir=f'runs/{run_name}/videos' if args.capture_video else None, wrappers=[partial(FlattenRGBDObservationWrapper, sep_depth=True)])
     if args.track:
         import wandb
         config = vars(args)
@@ -448,4 +399,87 @@ def get_eval_action(self, obs_seq):
         persistent_workers=(args.num_dataload_workers > 0),
     )
 
-    agent = Agent(envs, args).to(device)
\ No newline at end of file
+    agent = Agent(envs, args).to(device)
+
+    optimizer = optim.AdamW(params=agent.parameters(), 
+        lr=args.lr, betas=(0.95, 0.999), weight_decay=1e-6)
+
+    # Cosine LR schedule with linear warmup
+    lr_scheduler = get_scheduler(
+        name='cosine',
+        optimizer=optimizer,
+        num_warmup_steps=500,
+        num_training_steps=args.total_iters,
+    )
+
+    # Exponential Moving Average
+    # accelerates training and improves stability
+    # holds a copy of the model weights
+    ema = EMAModel(parameters=agent.parameters(), power=0.75)
+    ema_agent = Agent(envs, args).to(device)
+
+    # ---------------------------------------------------------------------------- #
+    # Training begins.
+    # ---------------------------------------------------------------------------- #
+    agent.train()
+    
+    best_eval_metrics = defaultdict(float)
+    timings = defaultdict(float)
+
+    for iteration, data_batch in enumerate(train_dataloader):
+        # # copy data from cpu to gpu
+        # data_batch = {k: v.cuda(non_blocking=True) for k, v in data_batch.items()}
+
+        # forward and compute loss
+        obs_batch_dict = data_batch['observations']
+        obs_batch_dict = {k: v.cuda(non_blocking=True) for k, v in obs_batch_dict.items()}
+        act_batch = data_batch['actions'].cuda(non_blocking=True)
+
+        # forward and compute loss
+        total_loss = agent.compute_loss(
+            obs_seq=obs_batch_dict, # obs_batch_dict['state'] is (B, L, obs_dim)
+            action_seq=act_batch, # (B, L, act_dim)
+        )
+
+        # backward
+        optimizer.zero_grad()
+        total_loss.backward()
+        optimizer.step()
+        lr_scheduler.step() # step lr scheduler every batch, this is different from standard pytorch behavior
+        last_tick = time.time()
+
+        ema.step(agent.parameters())
+
+        if iteration % args.log_freq == 0:
+            print(f"Iteration {iteration}, loss: {total_loss.item()}")
+            writer.add_scalar("charts/learning_rate", optimizer.param_groups[0]["lr"], iteration)
+            writer.add_scalar("losses/total_loss", total_loss.item(), iteration)
+            for k, v in timings.items():
+                writer.add_scalar(f"time/{k}", v, iteration)
+        # Evaluation
+        if iteration % args.eval_freq == 0:
+            last_tick = time.time()
+
+            ema.copy_to(ema_agent.parameters())
+            # def sample_fn(obs):
+
+            eval_metrics = evaluate(args.num_eval_episodes, ema_agent, envs, device, args.sim_backend)
+            timings["eval"] += time.time() - last_tick
+
+            print(f"Evaluated {len(eval_metrics['success_at_end'])} episodes")
+            for k in eval_metrics.keys():
+                eval_metrics[k] = np.mean(eval_metrics[k])
+                writer.add_scalar(f"eval/{k}", eval_metrics[k], iteration)
+                print(f"{k}: {eval_metrics[k]:.4f}")
+
+            save_on_best_metrics = ["success_once", "success_at_end"]
+            for k in save_on_best_metrics:
+                if k in eval_metrics and eval_metrics[k] > best_eval_metrics[k]:
+                    best_eval_metrics[k] = eval_metrics[k]
+                    save_ckpt(run_name, f"best_eval_{k}")
+                    print(f'New best {k}_rate: {eval_metrics[k]:.4f}. Saving checkpoint.')
+        # Checkpoint
+        if args.save_freq is not None and iteration % args.save_freq == 0:
+            save_ckpt(run_name, str(iteration))
+    envs.close()
+    writer.close()
\ No newline at end of file
diff --git a/mani_skill/utils/wrappers/flatten.py b/mani_skill/utils/wrappers/flatten.py
index 64be9d0c4..126dcbe82 100644
--- a/mani_skill/utils/wrappers/flatten.py
+++ b/mani_skill/utils/wrappers/flatten.py
@@ -23,11 +23,12 @@ class FlattenRGBDObservationWrapper(gym.ObservationWrapper):
     Note that the returned observations will have a "rgbd" or "rgb" or "depth" key depending on the rgb/depth bool flags.
     """
 
-    def __init__(self, env, rgb=True, depth=True, state=True) -> None:
+    def __init__(self, env, rgb=True, depth=True, state=True, sep_depth=False) -> None:
         self.base_env: BaseEnv = env.unwrapped
         super().__init__(env)
         self.include_rgb = rgb
         self.include_depth = depth
+        self.sep_depth = sep_depth
         self.include_state = state
         new_obs = self.observation(self.base_env._init_raw_obs)
         self.base_env.update_obs_space(new_obs)
@@ -50,7 +51,12 @@ def observation(self, observation: Dict):
         if self.include_rgb and not self.include_depth:
             ret["rgb"] = images
         elif self.include_rgb and self.include_depth:
-            ret["rgbd"] = images
+            # todo: might not be the best way or the right place to perform this
+            if self.sep_depth:
+                ret["rgb"] = images[...,:-1]
+                ret["depth"] = images[...,-1:]
+            else:
+                ret["rgbd"] = images
         elif self.include_depth and not self.include_rgb:
             ret["depth"] = images
         return ret

From 41bc78a323f56ed4c660c28bfb98430d06b53ad6 Mon Sep 17 00:00:00 2001
From: Arnav G <73305591+arnavg115@users.noreply.github.com>
Date: Mon, 21 Oct 2024 01:25:01 +0000
Subject: [PATCH 07/14] minor diff policy fixes and finished draw triangle
 parallelization

---
 .../diffusion_policy/diffusion_policy/make_env.py        | 2 +-
 examples/baselines/diffusion_policy/train_rgbd.py        | 1 +
 mani_skill/envs/tasks/drawing/draw_triangle.py           | 9 ++++-----
 3 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/examples/baselines/diffusion_policy/diffusion_policy/make_env.py b/examples/baselines/diffusion_policy/diffusion_policy/make_env.py
index e9e5bf33a..7cfb657f5 100644
--- a/examples/baselines/diffusion_policy/diffusion_policy/make_env.py
+++ b/examples/baselines/diffusion_policy/diffusion_policy/make_env.py
@@ -85,7 +85,7 @@ def thunk():
                 if env_kwargs["obs_mode"] == "state":
                     env = gym.wrappers.FrameStack(env, other_kwargs['obs_horizon'])
                 elif env_kwargs["obs_mode"] == "rgbd":
-                    env = DictFrameStack(env, 2)
+                    env = DictFrameStack(env, other_kwargs['obs_horizon'])
                 env.action_space.seed(seed)
                 env.observation_space.seed(seed)
                 return env
diff --git a/examples/baselines/diffusion_policy/train_rgbd.py b/examples/baselines/diffusion_policy/train_rgbd.py
index 2d7f9719b..f2706c609 100644
--- a/examples/baselines/diffusion_policy/train_rgbd.py
+++ b/examples/baselines/diffusion_policy/train_rgbd.py
@@ -478,6 +478,7 @@ def get_action(self, obs_seq):
                     best_eval_metrics[k] = eval_metrics[k]
                     save_ckpt(run_name, f"best_eval_{k}")
                     print(f'New best {k}_rate: {eval_metrics[k]:.4f}. Saving checkpoint.')
+            
         # Checkpoint
         if args.save_freq is not None and iteration % args.save_freq == 0:
             save_ckpt(run_name, str(iteration))
diff --git a/mani_skill/envs/tasks/drawing/draw_triangle.py b/mani_skill/envs/tasks/drawing/draw_triangle.py
index 5c8c39351..74e6a63bd 100644
--- a/mani_skill/envs/tasks/drawing/draw_triangle.py
+++ b/mani_skill/envs/tasks/drawing/draw_triangle.py
@@ -43,7 +43,7 @@ class DrawTriangle(BaseEnv):
     """The colors of the brushes. If there is more than one color, each parallel environment will have a randomly sampled color."""
     THRESHOLD = 0.05
 
-    SUPPORTED_REWARD_MODES = ["none"]
+    SUPPORTED_REWARD_MODES = ["sparse"]
 
     SUPPORTED_ROBOTS: ["panda_stick"]  # type: ignore
     agent: PandaStick
@@ -216,7 +216,6 @@ def create_goal_triangle(name="tri", base_color=None):
         )
 
     def _initialize_episode(self, env_idx: torch.Tensor, options: dict):
-        # NOTE (stao): for simplicity this task cannot handle partial resets
         self.draw_step = 0
         with torch.device(self.device):
             b = len(env_idx)
@@ -226,18 +225,18 @@ def _initialize_episode(self, env_idx: torch.Tensor, options: dict):
             target_pos[:, :2] = torch.rand((b, 2)) * 0.02 - 0.1
             target_pos[:, -1] = 0.01
             qs = randomization.random_quaternions(b, lock_x=True, lock_y=True)
-            mats = quaternion_to_matrix(qs)
+            mats = quaternion_to_matrix(qs).to(self.device)
             self.goal_tri.set_pose(Pose.create_from_pq(p=target_pos, q=qs))
 
             self.vertices = torch.from_numpy(
                 np.tile(self.original_verts, (b, 1, 1))
-            )  # b, 3, 3
+            ).to(self.device) # b, 3, 3
             self.vertices = (
                 mats.double() @ self.vertices.transpose(-1, -2).double()
             ).transpose(
                 -1, -2
             )  # apply rotation matrix
-            self.vertices += target_pos
+            self.vertices += target_pos.unsqueeze(1)
 
             self.triangles = self.generate_triangle_with_points(
                 100, self.vertices[:, :, :-1]

From e322dadd33fc6d1e4455fe6be778ef40cf1c85ff Mon Sep 17 00:00:00 2001
From: Arnav G <73305591+arnavg115@users.noreply.github.com>
Date: Mon, 21 Oct 2024 19:55:42 +0000
Subject: [PATCH 08/14] Added depth arg to diff pol rgbd + formatting

---
 .../diffusion_policy/make_env.py              |  79 +++-
 .../diffusion_policy/plain_conv.py            |  32 +-
 .../diffusion_policy/utils.py                 | 221 ++++++-----
 .../baselines/diffusion_policy/train_rgbd.py  | 372 +++++++++++-------
 .../envs/tasks/drawing/draw_triangle.py       |   4 +-
 .../panda_stick/motionplanner.py              |   4 +-
 6 files changed, 443 insertions(+), 269 deletions(-)

diff --git a/examples/baselines/diffusion_policy/diffusion_policy/make_env.py b/examples/baselines/diffusion_policy/diffusion_policy/make_env.py
index 7cfb657f5..0a210d79f 100644
--- a/examples/baselines/diffusion_policy/diffusion_policy/make_env.py
+++ b/examples/baselines/diffusion_policy/diffusion_policy/make_env.py
@@ -1,14 +1,15 @@
 from collections import deque
 from typing import Optional
+
 import gymnasium as gym
 import mani_skill.envs
-from mani_skill.utils import gym_utils
-from mani_skill.vector.wrappers.gymnasium import ManiSkillVectorEnv
-from mani_skill.utils.wrappers import RecordEpisode, FrameStack, CPUGymWrapper
 import numpy as np
 from gymnasium.spaces import Box
 from gymnasium.wrappers.frame_stack import FrameStack as GymFrameStack
 from gymnasium.wrappers.frame_stack import LazyFrames
+from mani_skill.utils import gym_utils
+from mani_skill.utils.wrappers import CPUGymWrapper, FrameStack, RecordEpisode
+from mani_skill.vector.wrappers.gymnasium import ManiSkillVectorEnv
 
 
 class DictFrameStack(GymFrameStack):
@@ -39,9 +40,7 @@ def __init__(
         for k, v in self.observation_space.items():
             low = np.repeat(v.low[np.newaxis, ...], num_stack, axis=0)
             high = np.repeat(v.high[np.newaxis, ...], num_stack, axis=0)
-            new_observation_space[k] = Box(
-                low=low, high=high, dtype=v.dtype
-            )
+            new_observation_space[k] = Box(low=low, high=high, dtype=v.dtype)
         self.observation_space = new_observation_space
 
     def observation(self, observation):
@@ -60,7 +59,15 @@ def observation(self, observation):
         }
 
 
-def make_eval_envs(env_id, num_envs: int, sim_backend: str, env_kwargs: dict, other_kwargs: dict, video_dir: Optional[str] = None, wrappers: list[gym.Wrapper] = []):
+def make_eval_envs(
+    env_id,
+    num_envs: int,
+    sim_backend: str,
+    env_kwargs: dict,
+    other_kwargs: dict,
+    video_dir: Optional[str] = None,
+    wrappers: list[gym.Wrapper] = [],
+):
     """Create vectorized environment for evaluation and/or recording videos.
     For CPU vectorized environments only the first parallel environment is used to record videos.
     For GPU vectorized environments all parallel environments are used to record videos.
@@ -74,32 +81,72 @@ def make_eval_envs(env_id, num_envs: int, sim_backend: str, env_kwargs: dict, ot
         wrappers: the list of wrappers to apply to the environment.
     """
     if sim_backend == "cpu":
-        def cpu_make_env(env_id, seed, video_dir=None, env_kwargs = dict(), other_kwargs = dict()):
+
+        def cpu_make_env(
+            env_id, seed, video_dir=None, env_kwargs=dict(), other_kwargs=dict()
+        ):
             def thunk():
                 env = gym.make(env_id, reconfiguration_freq=1, **env_kwargs)
                 for wrapper in wrappers:
                     env = wrapper(env)
                 env = CPUGymWrapper(env, ignore_terminations=True, record_metrics=True)
                 if video_dir:
-                    env = RecordEpisode(env, output_dir=video_dir, save_trajectory=False, info_on_video=True, source_type="diffusion_policy", source_desc="diffusion_policy evaluation rollout")
+                    env = RecordEpisode(
+                        env,
+                        output_dir=video_dir,
+                        save_trajectory=False,
+                        info_on_video=True,
+                        source_type="diffusion_policy",
+                        source_desc="diffusion_policy evaluation rollout",
+                    )
                 if env_kwargs["obs_mode"] == "state":
-                    env = gym.wrappers.FrameStack(env, other_kwargs['obs_horizon'])
+                    env = gym.wrappers.FrameStack(env, other_kwargs["obs_horizon"])
                 elif env_kwargs["obs_mode"] == "rgbd":
-                    env = DictFrameStack(env, other_kwargs['obs_horizon'])
+                    env = DictFrameStack(env, other_kwargs["obs_horizon"])
                 env.action_space.seed(seed)
                 env.observation_space.seed(seed)
                 return env
 
             return thunk
-        vector_cls = gym.vector.SyncVectorEnv if num_envs == 1 else lambda x : gym.vector.AsyncVectorEnv(x, context="forkserver")
-        env = vector_cls([cpu_make_env(env_id, seed, video_dir if seed == 0 else None, env_kwargs, other_kwargs) for seed in range(num_envs)])
+
+        vector_cls = (
+            gym.vector.SyncVectorEnv
+            if num_envs == 1
+            else lambda x: gym.vector.AsyncVectorEnv(x, context="forkserver")
+        )
+        env = vector_cls(
+            [
+                cpu_make_env(
+                    env_id,
+                    seed,
+                    video_dir if seed == 0 else None,
+                    env_kwargs,
+                    other_kwargs,
+                )
+                for seed in range(num_envs)
+            ]
+        )
     else:
-        env = gym.make(env_id, num_envs=num_envs, sim_backend=sim_backend, reconfiguration_freq=1, **env_kwargs)
+        env = gym.make(
+            env_id,
+            num_envs=num_envs,
+            sim_backend=sim_backend,
+            reconfiguration_freq=1,
+            **env_kwargs
+        )
         max_episode_steps = gym_utils.find_max_episode_steps_value(env)
         for wrapper in wrappers:
             env = wrapper(env)
-        env = FrameStack(env, num_stack=other_kwargs['obs_horizon'])
+        env = FrameStack(env, num_stack=other_kwargs["obs_horizon"])
         if video_dir:
-            env = RecordEpisode(env, output_dir=video_dir, save_trajectory=False, save_video=True, source_type="diffusion_policy", source_desc="diffusion_policy evaluation rollout", max_steps_per_video=max_episode_steps)
+            env = RecordEpisode(
+                env,
+                output_dir=video_dir,
+                save_trajectory=False,
+                save_video=True,
+                source_type="diffusion_policy",
+                source_desc="diffusion_policy evaluation rollout",
+                max_steps_per_video=max_episode_steps,
+            )
         env = ManiSkillVectorEnv(env, ignore_terminations=True, record_metrics=True)
     return env
diff --git a/examples/baselines/diffusion_policy/diffusion_policy/plain_conv.py b/examples/baselines/diffusion_policy/diffusion_policy/plain_conv.py
index 50c09e59d..7244469b8 100644
--- a/examples/baselines/diffusion_policy/diffusion_policy/plain_conv.py
+++ b/examples/baselines/diffusion_policy/diffusion_policy/plain_conv.py
@@ -1,5 +1,6 @@
 import torch.nn as nn
 
+
 def make_mlp(in_channels, mlp_channels, act_builder=nn.ReLU, last_act=True):
     c_in = in_channels
     module_list = []
@@ -10,27 +11,34 @@ def make_mlp(in_channels, mlp_channels, act_builder=nn.ReLU, last_act=True):
         c_in = c_out
     return nn.Sequential(*module_list)
 
+
 class PlainConv(nn.Module):
-    def __init__(self,
-                 in_channels=3,
-                 out_dim=256,
-                 pool_feature_map=False,
-                 last_act=True, # True for ConvBody, False for CNN
-                 ):
+    def __init__(
+        self,
+        in_channels=3,
+        out_dim=256,
+        pool_feature_map=False,
+        last_act=True,  # True for ConvBody, False for CNN
+    ):
         super().__init__()
         # assume input image size is 64x64
 
         self.out_dim = out_dim
         self.cnn = nn.Sequential(
-            nn.Conv2d(in_channels, 16, 3, padding=1, bias=True), nn.ReLU(inplace=True),
+            nn.Conv2d(in_channels, 16, 3, padding=1, bias=True),
+            nn.ReLU(inplace=True),
             nn.MaxPool2d(2, 2),  # [32, 32]
-            nn.Conv2d(16, 32, 3, padding=1, bias=True), nn.ReLU(inplace=True),
+            nn.Conv2d(16, 32, 3, padding=1, bias=True),
+            nn.ReLU(inplace=True),
             nn.MaxPool2d(2, 2),  # [16, 16]
-            nn.Conv2d(32, 64, 3, padding=1, bias=True), nn.ReLU(inplace=True),
+            nn.Conv2d(32, 64, 3, padding=1, bias=True),
+            nn.ReLU(inplace=True),
             nn.MaxPool2d(2, 2),  # [8, 8]
-            nn.Conv2d(64, 128, 3, padding=1, bias=True), nn.ReLU(inplace=True),
+            nn.Conv2d(64, 128, 3, padding=1, bias=True),
+            nn.ReLU(inplace=True),
             nn.MaxPool2d(2, 2),  # [4, 4]
-            nn.Conv2d(128, 128, 1, padding=0, bias=True), nn.ReLU(inplace=True),
+            nn.Conv2d(128, 128, 1, padding=0, bias=True),
+            nn.ReLU(inplace=True),
         )
 
         if pool_feature_map:
@@ -54,4 +62,4 @@ def forward(self, image):
             x = self.pool(x)
         x = x.flatten(1)
         x = self.fc(x)
-        return x
\ No newline at end of file
+        return x
diff --git a/examples/baselines/diffusion_policy/diffusion_policy/utils.py b/examples/baselines/diffusion_policy/diffusion_policy/utils.py
index ee21344f4..bc270e19c 100644
--- a/examples/baselines/diffusion_policy/diffusion_policy/utils.py
+++ b/examples/baselines/diffusion_policy/diffusion_policy/utils.py
@@ -1,10 +1,11 @@
-from torch.utils.data.sampler import Sampler
 import numpy as np
 import torch
 import torch.nn as nn
-from h5py import File, Group, Dataset
-from gymnasium import spaces
 import torch.nn.functional as F
+from gymnasium import spaces
+from h5py import Dataset, File, Group
+from torch.utils.data.sampler import Sampler
+
 
 class IterationBasedBatchSampler(Sampler):
     """Wraps a BatchSampler.
@@ -48,15 +49,18 @@ def worker_init_fn(worker_id, base_seed=None):
     # print(worker_id, base_seed)
     np.random.seed(base_seed + worker_id)
 
+
 TARGET_KEY_TO_SOURCE_KEY = {
-    'states': 'env_states',
-    'observations': 'obs',
-    'success': 'success',
-    'next_observations': 'obs',
+    "states": "env_states",
+    "observations": "obs",
+    "success": "success",
+    "next_observations": "obs",
     # 'dones': 'dones',
     # 'rewards': 'rewards',
-    'actions': 'actions',
+    "actions": "actions",
 }
+
+
 def load_content_from_h5_file(file):
     if isinstance(file, (File, Group)):
         return {key: load_content_from_h5_file(file[key]) for key in list(file.keys())}
@@ -65,34 +69,40 @@ def load_content_from_h5_file(file):
     else:
         raise NotImplementedError(f"Unspported h5 file type: {type(file)}")
 
-def load_hdf5(path, ):
-    print('Loading HDF5 file', path)
-    file = File(path, 'r')
+
+def load_hdf5(
+    path,
+):
+    print("Loading HDF5 file", path)
+    file = File(path, "r")
     ret = load_content_from_h5_file(file)
     file.close()
-    print('Loaded')
+    print("Loaded")
     return ret
 
+
 def load_traj_hdf5(path, num_traj=None):
-    print('Loading HDF5 file', path)
-    file = File(path, 'r')
+    print("Loading HDF5 file", path)
+    file = File(path, "r")
     keys = list(file.keys())
     if num_traj is not None:
         assert num_traj <= len(keys), f"num_traj: {num_traj} > len(keys): {len(keys)}"
-        keys = sorted(keys, key=lambda x: int(x.split('_')[-1]))
+        keys = sorted(keys, key=lambda x: int(x.split("_")[-1]))
         keys = keys[:num_traj]
-    ret = {
-        key: load_content_from_h5_file(file[key]) for key in keys
-    }
+    ret = {key: load_content_from_h5_file(file[key]) for key in keys}
     file.close()
-    print('Loaded')
+    print("Loaded")
     return ret
-def load_demo_dataset(path, keys=['observations', 'actions'], num_traj=None, concat=True):
+
+
+def load_demo_dataset(
+    path, keys=["observations", "actions"], num_traj=None, concat=True
+):
     # assert num_traj is None
     raw_data = load_traj_hdf5(path, num_traj)
     # raw_data has keys like: ['traj_0', 'traj_1', ...]
     # raw_data['traj_0'] has keys like: ['actions', 'dones', 'env_states', 'infos', ...]
-    _traj = raw_data['traj_0']
+    _traj = raw_data["traj_0"]
     for key in keys:
         source_key = TARGET_KEY_TO_SOURCE_KEY[key]
         assert source_key in _traj, f"key: {source_key} not in traj_0: {_traj.keys()}"
@@ -101,88 +111,108 @@ def load_demo_dataset(path, keys=['observations', 'actions'], num_traj=None, con
         # if 'next' in target_key:
         #     raise NotImplementedError('Please carefully deal with the length of trajectory')
         source_key = TARGET_KEY_TO_SOURCE_KEY[target_key]
-        dataset[target_key] = [ raw_data[idx][source_key] for idx in raw_data ]
+        dataset[target_key] = [raw_data[idx][source_key] for idx in raw_data]
         if isinstance(dataset[target_key][0], np.ndarray) and concat:
-            if target_key in ['observations', 'states'] and \
-                    len(dataset[target_key][0]) > len(raw_data['traj_0']['actions']):
-                dataset[target_key] = np.concatenate([
-                    t[:-1] for t in dataset[target_key]
-                ], axis=0)
-            elif target_key in ['next_observations', 'next_states'] and \
-                    len(dataset[target_key][0]) > len(raw_data['traj_0']['actions']):
-                dataset[target_key] = np.concatenate([
-                    t[1:] for t in dataset[target_key]
-                ], axis=0)
+            if target_key in ["observations", "states"] and len(
+                dataset[target_key][0]
+            ) > len(raw_data["traj_0"]["actions"]):
+                dataset[target_key] = np.concatenate(
+                    [t[:-1] for t in dataset[target_key]], axis=0
+                )
+            elif target_key in ["next_observations", "next_states"] and len(
+                dataset[target_key][0]
+            ) > len(raw_data["traj_0"]["actions"]):
+                dataset[target_key] = np.concatenate(
+                    [t[1:] for t in dataset[target_key]], axis=0
+                )
             else:
                 dataset[target_key] = np.concatenate(dataset[target_key], axis=0)
 
-            print('Load', target_key, dataset[target_key].shape)
+            print("Load", target_key, dataset[target_key].shape)
         else:
-            print('Load', target_key, len(dataset[target_key]), type(dataset[target_key][0]))
+            print(
+                "Load",
+                target_key,
+                len(dataset[target_key]),
+                type(dataset[target_key][0]),
+            )
     return dataset
-def convert_obs(obs, concat_fn, transpose_fn, state_obs_extractor):
-        img_dict = obs['sensor_data']
-        new_img_dict = {
-            key: transpose_fn(
-                concat_fn([v[key] for v in img_dict.values()])
-            ) # (C, H, W) or (B, C, H, W)
-            for key in ['rgb', 'depth']
-        }
-        # if isinstance(new_img_dict['depth'], torch.Tensor): # MS2 vec env uses float16, but gym AsyncVecEnv uses float32
-        #     new_img_dict['depth'] = new_img_dict['depth'].to(torch.float16)
-
-        # Unified version
-        states_to_stack = state_obs_extractor(obs)
-        for j in range(len(states_to_stack)):
-            if states_to_stack[j].dtype == np.float64:
-                states_to_stack[j] = states_to_stack[j].astype(np.float32)
-        try:
-            state = np.hstack(states_to_stack)
-        except: # dirty fix for concat trajectory of states
-            state = np.column_stack(states_to_stack)
-        if state.dtype == np.float64:
-            for x in states_to_stack:
-                print(x.shape, x.dtype)
-            import pdb; pdb.set_trace()
-
-        out_dict = {
-            'state': state,
-            'rgb': new_img_dict['rgb'], 
-            'depth': new_img_dict['depth'],
-        }
-        return out_dict
 
-def build_obs_space(env, depth_dtype, state_obs_extractor):
-        # NOTE: We have to use float32 for gym AsyncVecEnv since it does not support float16, but we can use float16 for MS2 vec env
-        obs_space = env.observation_space
 
-        # Unified version
-        state_dim = sum([v.shape[0] for v in state_obs_extractor(obs_space)])
+def convert_obs(obs, concat_fn, transpose_fn, state_obs_extractor):
+    img_dict = obs["sensor_data"]
+    new_img_dict = {
+        key: transpose_fn(
+            concat_fn([v[key] for v in img_dict.values()])
+        )  # (C, H, W) or (B, C, H, W)
+        for key in ["rgb", "depth"]
+    }
+    # if isinstance(new_img_dict['depth'], torch.Tensor): # MS2 vec env uses float16, but gym AsyncVecEnv uses float32
+    #     new_img_dict['depth'] = new_img_dict['depth'].to(torch.float16)
+
+    # Unified version
+    states_to_stack = state_obs_extractor(obs)
+    for j in range(len(states_to_stack)):
+        if states_to_stack[j].dtype == np.float64:
+            states_to_stack[j] = states_to_stack[j].astype(np.float32)
+    try:
+        state = np.hstack(states_to_stack)
+    except:  # dirty fix for concat trajectory of states
+        state = np.column_stack(states_to_stack)
+    if state.dtype == np.float64:
+        for x in states_to_stack:
+            print(x.shape, x.dtype)
+        import pdb
+
+        pdb.set_trace()
+
+    out_dict = {
+        "state": state,
+        "rgb": new_img_dict["rgb"],
+        "depth": new_img_dict["depth"],
+    }
+    return out_dict
+
 
-        single_img_space = next(iter(env.observation_space['image'].values()))
-        h, w, _ = single_img_space['rgb'].shape
-        n_images = len(env.observation_space['image'])
+def build_obs_space(env, depth_dtype, state_obs_extractor):
+    # NOTE: We have to use float32 for gym AsyncVecEnv since it does not support float16, but we can use float16 for MS2 vec env
+    obs_space = env.observation_space
+
+    # Unified version
+    state_dim = sum([v.shape[0] for v in state_obs_extractor(obs_space)])
+
+    single_img_space = next(iter(env.observation_space["image"].values()))
+    h, w, _ = single_img_space["rgb"].shape
+    n_images = len(env.observation_space["image"])
+
+    return spaces.Dict(
+        {
+            "state": spaces.Box(
+                -float("inf"), float("inf"), shape=(state_dim,), dtype=np.float32
+            ),
+            "rgb": spaces.Box(0, 255, shape=(n_images * 3, h, w), dtype=np.uint8),
+            "depth": spaces.Box(
+                -float("inf"), float("inf"), shape=(n_images, h, w), dtype=depth_dtype
+            ),
+        }
+    )
 
-        return spaces.Dict({
-            'state': spaces.Box(-float("inf"), float("inf"), shape=(state_dim,), dtype=np.float32),
-            'rgb': spaces.Box(0, 255, shape=(n_images*3, h,w), dtype=np.uint8),
-            'depth': spaces.Box(-float("inf"), float("inf"), shape=(n_images,h,w), dtype=depth_dtype),
-        })
 
 def build_state_obs_extractor(env_id):
-    env_name = env_id.split('-')[0]
-    if env_name in ['TurnFaucet', 'StackCube']:
-        return lambda obs: list(obs['extra'].values())
-    elif env_name == 'PushChair' or env_name == "PickCube":
-        return lambda obs: list(obs['agent'].values()) + list(obs['extra'].values())
+    env_name = env_id.split("-")[0]
+    if env_name in ["TurnFaucet", "StackCube"]:
+        return lambda obs: list(obs["extra"].values())
+    elif env_name == "PushChair" or env_name == "PickCube":
+        return lambda obs: list(obs["agent"].values()) + list(obs["extra"].values())
     else:
-        raise NotImplementedError(f'Please tune state obs by yourself')
+        raise NotImplementedError(f"Please tune state obs by yourself")
 
 
 class RandomShiftsAug(nn.Module):
     """
     from https://github.com/facebookresearch/drqv2/blob/main/drqv2.py
     """
+
     def __init__(self, pad):
         super().__init__()
         self.pad = pad
@@ -191,26 +221,19 @@ def forward(self, x):
         n, c, h, w = x.size()
         assert h == w
         padding = tuple([self.pad] * 4)
-        x = F.pad(x, padding, 'replicate')
+        x = F.pad(x, padding, "replicate")
         eps = 1.0 / (h + 2 * self.pad)
-        arange = torch.linspace(-1.0 + eps,
-                                1.0 - eps,
-                                h + 2 * self.pad,
-                                device=x.device,
-                                dtype=x.dtype)[:h]
+        arange = torch.linspace(
+            -1.0 + eps, 1.0 - eps, h + 2 * self.pad, device=x.device, dtype=x.dtype
+        )[:h]
         arange = arange.unsqueeze(0).repeat(h, 1).unsqueeze(2)
         base_grid = torch.cat([arange, arange.transpose(1, 0)], dim=2)
         base_grid = base_grid.unsqueeze(0).repeat(n, 1, 1, 1)
 
-        shift = torch.randint(0,
-                              2 * self.pad + 1,
-                              size=(n, 1, 1, 2),
-                              device=x.device,
-                              dtype=x.dtype)
+        shift = torch.randint(
+            0, 2 * self.pad + 1, size=(n, 1, 1, 2), device=x.device, dtype=x.dtype
+        )
         shift *= 2.0 / (h + 2 * self.pad)
 
         grid = base_grid + shift
-        return F.grid_sample(x,
-                             grid,
-                             padding_mode='zeros',
-                             align_corners=False)
\ No newline at end of file
+        return F.grid_sample(x, grid, padding_mode="zeros", align_corners=False)
diff --git a/examples/baselines/diffusion_policy/train_rgbd.py b/examples/baselines/diffusion_policy/train_rgbd.py
index f2706c609..ef6e317f6 100644
--- a/examples/baselines/diffusion_policy/train_rgbd.py
+++ b/examples/baselines/diffusion_policy/train_rgbd.py
@@ -1,39 +1,41 @@
-ALGO_NAME = 'BC_Diffusion_rgbd_UNet'
+ALGO_NAME = "BC_Diffusion_rgbd_UNet"
 
 import argparse
-from collections import defaultdict
-from functools import partial
 import os
 import random
-from distutils.util import strtobool
 import time
+from collections import defaultdict
+from dataclasses import dataclass, field
+from distutils.util import strtobool
+from functools import partial
+from typing import List, Optional
+
 import gymnasium as gym
 import numpy as np
 import torch
 import torch.nn as nn
-import torch.optim as optim
 import torch.nn.functional as F
-from torch.utils.tensorboard import SummaryWriter
-from diffusion_policy.evaluate import evaluate
-from diffusion_policy.plain_conv import PlainConv
+import torch.optim as optim
+import tyro
+from diffusers.optimization import get_scheduler
+from diffusers.schedulers.scheduling_ddpm import DDPMScheduler
+from diffusers.training_utils import EMAModel
+from gymnasium import spaces
 from mani_skill.utils import gym_utils
-from mani_skill.utils.wrappers.flatten import FlattenRGBDObservationWrapper
 from mani_skill.utils.registration import REGISTERED_ENVS
-
-
-from torch.utils.data.dataset import Dataset
-from torch.utils.data.sampler import RandomSampler, BatchSampler
+from mani_skill.utils.wrappers.flatten import FlattenRGBDObservationWrapper
 from torch.utils.data.dataloader import DataLoader
-from diffusion_policy.utils import IterationBasedBatchSampler, build_state_obs_extractor, convert_obs, worker_init_fn
-from diffusion_policy.make_env import make_eval_envs
-from diffusers.schedulers.scheduling_ddpm import DDPMScheduler
-from diffusers.training_utils import EMAModel
-from diffusers.optimization import get_scheduler
+from torch.utils.data.dataset import Dataset
+from torch.utils.data.sampler import BatchSampler, RandomSampler
+from torch.utils.tensorboard import SummaryWriter
+
 from diffusion_policy.conditional_unet1d import ConditionalUnet1D
-from dataclasses import dataclass, field
-from typing import Optional, List
-import tyro
-from gymnasium import spaces
+from diffusion_policy.evaluate import evaluate
+from diffusion_policy.make_env import make_eval_envs
+from diffusion_policy.plain_conv import PlainConv
+from diffusion_policy.utils import (IterationBasedBatchSampler,
+                                    build_state_obs_extractor, convert_obs,
+                                    worker_init_fn)
 
 
 @dataclass
@@ -57,7 +59,9 @@ class Args:
 
     env_id: str = "PegInsertionSide-v0"
     """the id of the environment"""
-    demo_path: str = 'data/ms2_official_demos/rigid_body/PegInsertionSide-v0/trajectory.state.pd_ee_delta_pose.h5'
+    demo_path: str = (
+        "data/ms2_official_demos/rigid_body/PegInsertionSide-v0/trajectory.state.pd_ee_delta_pose.h5"
+    )
     """the path of demo dataset (pkl or h5)"""
     num_demos: Optional[int] = None
     """number of trajectories to load from the demo dataset"""
@@ -69,12 +73,20 @@ class Args:
     # Diffusion Policy specific arguments
     lr: float = 1e-4
     """the learning rate of the diffusion policy"""
-    obs_horizon: int = 2 # Seems not very important in ManiSkill, 1, 2, 4 work well
-    act_horizon: int = 8 # Seems not very important in ManiSkill, 4, 8, 15 work well
-    pred_horizon: int = 16 # 16->8 leads to worse performance, maybe it is like generate a half image; 16->32, improvement is very marginal
-    diffusion_step_embed_dim: int = 64 # not very important
-    unet_dims: List[int] = field(default_factory=lambda: [64, 128, 256]) # default setting is about ~4.5M params
-    n_groups: int = 8 # jigu says it is better to let each group have at least 8 channels; it seems 4 and 8 are simila
+    obs_horizon: int = 2  # Seems not very important in ManiSkill, 1, 2, 4 work well
+    act_horizon: int = 8  # Seems not very important in ManiSkill, 4, 8, 15 work well
+    pred_horizon: int = (
+        16  # 16->8 leads to worse performance, maybe it is like generate a half image; 16->32, improvement is very marginal
+    )
+    diffusion_step_embed_dim: int = 64  # not very important
+    unet_dims: List[int] = field(
+        default_factory=lambda: [64, 128, 256]
+    )  # default setting is about ~4.5M params
+    n_groups: int = (
+        8  # jigu says it is better to let each group have at least 8 channels; it seems 4 and 8 are simila
+    )
+    depth: bool = True
+    """use depth to train"""
 
     # Environment/experiment specific arguments
     max_episode_steps: Optional[int] = None
@@ -94,12 +106,13 @@ class Args:
     """the simulation backend to use for evaluation environments. can be "cpu" or "gpu"""
     num_dataload_workers: int = 0
     """the number of workers to use for loading the training data in the torch dataloader"""
-    control_mode: str = 'pd_joint_delta_pos'
+    control_mode: str = "pd_joint_delta_pos"
     """the control mode to use for the evaluation environments. Must match the control mode of the demonstration dataset."""
 
     # additional tags/configs for logging purposes to wandb and shared comparisons with other algorithms
     demo_type: Optional[str] = None
 
+
 def reorder_keys(d, ref_dict):
     out = dict()
     for k, v in ref_dict.items():
@@ -110,50 +123,65 @@ def reorder_keys(d, ref_dict):
     return out
 
 
-
-
-class SmallDemoDataset_DiffusionPolicy(Dataset): # Load everything into memory
+class SmallDemoDataset_DiffusionPolicy(Dataset):  # Load everything into memory
     def __init__(self, data_path, obs_process_fn, obs_space, num_traj):
-        if data_path[-4:] == '.pkl':
+        if data_path[-4:] == ".pkl":
             raise NotImplementedError()
         else:
             from diffusion_policy.utils import load_demo_dataset
+
             trajectories = load_demo_dataset(data_path, num_traj=num_traj, concat=False)
             # trajectories['observations'] is a list of dict, each dict is a traj, with keys in obs_space, values with length L+1
             # trajectories['actions'] is a list of np.ndarray (L, act_dim)
 
-        print('Raw trajectory loaded, start to pre-process the observations...')
+        print("Raw trajectory loaded, start to pre-process the observations...")
 
         # Pre-process the observations, make them align with the obs returned by the obs_wrapper
         obs_traj_dict_list = []
-        for obs_traj_dict in trajectories['observations']:
-            _obs_traj_dict = reorder_keys(obs_traj_dict, obs_space) # key order in demo is different from key order in env obs
+        for obs_traj_dict in trajectories["observations"]:
+            _obs_traj_dict = reorder_keys(
+                obs_traj_dict, obs_space
+            )  # key order in demo is different from key order in env obs
             _obs_traj_dict = obs_process_fn(_obs_traj_dict)
-            _obs_traj_dict['depth'] = torch.Tensor(_obs_traj_dict['depth'].astype(np.float32) / 1024).to(torch.float16)
-            _obs_traj_dict['rgb'] = torch.from_numpy(_obs_traj_dict['rgb']) # still uint8
-            _obs_traj_dict['state'] = torch.from_numpy(_obs_traj_dict['state'])
+            _obs_traj_dict["depth"] = torch.Tensor(
+                _obs_traj_dict["depth"].astype(np.float32) / 1024
+            ).to(torch.float16)
+            _obs_traj_dict["rgb"] = torch.from_numpy(
+                _obs_traj_dict["rgb"]
+            )  # still uint8
+            _obs_traj_dict["state"] = torch.from_numpy(_obs_traj_dict["state"])
             obs_traj_dict_list.append(_obs_traj_dict)
-        trajectories['observations'] = obs_traj_dict_list
+        trajectories["observations"] = obs_traj_dict_list
         self.obs_keys = list(_obs_traj_dict.keys())
         # Pre-process the actions
-        for i in range(len(trajectories['actions'])):
-            trajectories['actions'][i] = torch.Tensor(trajectories['actions'][i])
-        print('Obs/action pre-processing is done, start to pre-compute the slice indices...')
+        for i in range(len(trajectories["actions"])):
+            trajectories["actions"][i] = torch.Tensor(trajectories["actions"][i])
+        print(
+            "Obs/action pre-processing is done, start to pre-compute the slice indices..."
+        )
 
         # Pre-compute all possible (traj_idx, start, end) tuples, this is very specific to Diffusion Policy
-        if 'delta_pos' in args.control_mode or args.control_mode == 'base_pd_joint_vel_arm_pd_joint_vel':
-            self.pad_action_arm = torch.zeros((trajectories['actions'][0].shape[1]-1,))
+        if (
+            "delta_pos" in args.control_mode
+            or args.control_mode == "base_pd_joint_vel_arm_pd_joint_vel"
+        ):
+            self.pad_action_arm = torch.zeros(
+                (trajectories["actions"][0].shape[1] - 1,)
+            )
             # to make the arm stay still, we pad the action with 0 in 'delta_pos' control mode
             # gripper action needs to be copied from the last action
         else:
-            raise NotImplementedError(f'Control Mode {args.control_mode} not supported')
-        self.obs_horizon, self.pred_horizon = obs_horizon, pred_horizon = args.obs_horizon, args.pred_horizon
+            raise NotImplementedError(f"Control Mode {args.control_mode} not supported")
+        self.obs_horizon, self.pred_horizon = obs_horizon, pred_horizon = (
+            args.obs_horizon,
+            args.pred_horizon,
+        )
         self.slices = []
-        num_traj = len(trajectories['actions'])
+        num_traj = len(trajectories["actions"])
         total_transitions = 0
         for traj_idx in range(num_traj):
-            L = trajectories['actions'][traj_idx].shape[0]
-            assert trajectories['observations'][traj_idx]['state'].shape[0] == L + 1
+            L = trajectories["actions"][traj_idx].shape[0]
+            assert trajectories["observations"][traj_idx]["state"].shape[0] == L + 1
             total_transitions += L
 
             # |o|o|                             observations: 2
@@ -166,38 +194,46 @@ def __init__(self, data_path, obs_process_fn, obs_space, num_traj):
             # Pad after the trajectory, so all the observations are utilized in training
             # Note that in the original code, pad_after = act_horizon - 1, but I think this is not the best choice
             self.slices += [
-                (traj_idx, start, start + pred_horizon) for start in range(-pad_before, L - pred_horizon + pad_after)
+                (traj_idx, start, start + pred_horizon)
+                for start in range(-pad_before, L - pred_horizon + pad_after)
             ]  # slice indices follow convention [start, end)
-        
-        print(f"Total transitions: {total_transitions}, Total obs sequences: {len(self.slices)}")
+
+        print(
+            f"Total transitions: {total_transitions}, Total obs sequences: {len(self.slices)}"
+        )
 
         self.trajectories = trajectories
 
     def __getitem__(self, index):
         traj_idx, start, end = self.slices[index]
-        L, act_dim = self.trajectories['actions'][traj_idx].shape
+        L, act_dim = self.trajectories["actions"][traj_idx].shape
 
-        obs_traj = self.trajectories['observations'][traj_idx]
+        obs_traj = self.trajectories["observations"][traj_idx]
         obs_seq = {}
         for k, v in obs_traj.items():
-            obs_seq[k] = v[max(0, start):start+self.obs_horizon] # start+self.obs_horizon is at least 1
-            if start < 0: # pad before the trajectory
-                pad_obs_seq = torch.stack([obs_seq[k][0]]*abs(start), dim=0)
+            obs_seq[k] = v[
+                max(0, start) : start + self.obs_horizon
+            ]  # start+self.obs_horizon is at least 1
+            if start < 0:  # pad before the trajectory
+                pad_obs_seq = torch.stack([obs_seq[k][0]] * abs(start), dim=0)
                 obs_seq[k] = torch.cat((pad_obs_seq, obs_seq[k]), dim=0)
             # don't need to pad obs after the trajectory, see the above char drawing
 
-        act_seq = self.trajectories['actions'][traj_idx][max(0, start):end]
-        if start < 0: # pad before the trajectory
+        act_seq = self.trajectories["actions"][traj_idx][max(0, start) : end]
+        if start < 0:  # pad before the trajectory
             act_seq = torch.cat([act_seq[0].repeat(-start, 1), act_seq], dim=0)
-        if end > L: # pad after the trajectory
-            gripper_action = act_seq[-1, -1] # assume gripper is with pos controller
+        if end > L:  # pad after the trajectory
+            gripper_action = act_seq[-1, -1]  # assume gripper is with pos controller
             pad_action = torch.cat((self.pad_action_arm, gripper_action[None]), dim=0)
-            act_seq = torch.cat([act_seq, pad_action.repeat(end-L, 1)], dim=0)
+            act_seq = torch.cat([act_seq, pad_action.repeat(end - L, 1)], dim=0)
             # making the robot (arm and gripper) stay still
-        assert obs_seq['state'].shape[0] == self.obs_horizon and act_seq.shape[0] == self.pred_horizon
+        assert (
+            obs_seq["state"].shape[0] == self.obs_horizon
+            and act_seq.shape[0] == self.pred_horizon
+        )
         return {
-            'observations': obs_seq,
-            'actions': act_seq,
+            "observations": obs_seq,
+            "actions": act_seq,
         }
 
     def __len__(self):
@@ -210,18 +246,25 @@ def __init__(self, env, args):
         self.obs_horizon = args.obs_horizon
         self.act_horizon = args.act_horizon
         self.pred_horizon = args.pred_horizon
-        assert len(env.single_observation_space['state'].shape) == 2 # (obs_horizon, obs_dim)
-        assert len(env.single_action_space.shape) == 1 # (act_dim, )
-        assert (env.single_action_space.high == 1).all() and (env.single_action_space.low == -1).all()
+        assert (
+            len(env.single_observation_space["state"].shape) == 2
+        )  # (obs_horizon, obs_dim)
+        assert len(env.single_action_space.shape) == 1  # (act_dim, )
+        assert (env.single_action_space.high == 1).all() and (
+            env.single_action_space.low == -1
+        ).all()
         # denoising results will be clipped to [-1,1], so the action should be in [-1,1] as well
         self.act_dim = env.single_action_space.shape[0]
-        obs_state_dim = env.single_observation_space['state'].shape[1]
-        _, H, W, C = envs.single_observation_space['rgb'].shape
+        obs_state_dim = env.single_observation_space["state"].shape[1]
+        _, H, W, C = envs.single_observation_space["rgb"].shape
 
         visual_feature_dim = 256
-        self.visual_encoder = PlainConv(in_channels=int(C/3*4), out_dim=visual_feature_dim, pool_feature_map=True)
+        in_c = int(C / 3 * 4) if args.depth else C
+        self.visual_encoder = PlainConv(
+            in_channels=in_c, out_dim=visual_feature_dim, pool_feature_map=True
+        )
         self.noise_pred_net = ConditionalUnet1D(
-            input_dim=self.act_dim, # act_horizon is not used (U-Net doesn't care)
+            input_dim=self.act_dim,  # act_horizon is not used (U-Net doesn't care)
             global_cond_dim=self.obs_horizon * (visual_feature_dim + obs_state_dim),
             diffusion_step_embed_dim=args.diffusion_step_embed_dim,
             down_dims=args.unet_dims,
@@ -230,50 +273,57 @@ def __init__(self, env, args):
         self.num_diffusion_iters = 100
         self.noise_scheduler = DDPMScheduler(
             num_train_timesteps=self.num_diffusion_iters,
-            beta_schedule='squaredcos_cap_v2', # has big impact on performance, try not to change
-            clip_sample=True, # clip output to [-1,1] to improve stability
-            prediction_type='epsilon' # predict noise (instead of denoised action)
+            beta_schedule="squaredcos_cap_v2",  # has big impact on performance, try not to change
+            clip_sample=True,  # clip output to [-1,1] to improve stability
+            prediction_type="epsilon",  # predict noise (instead of denoised action)
         )
 
-
     def encode_obs(self, obs_seq, eval_mode):
-        rgb = obs_seq['rgb'].float() / 255.0 # (B, obs_horizon, 3*k, H, W)
-        depth = obs_seq['depth'].float() # (B, obs_horizon, 1*k, H, W)
-        img_seq = torch.cat([rgb, depth], dim=2) # (B, obs_horizon, C, H, W), C=4*k
-        img_seq = img_seq.flatten(end_dim=1) # (B*obs_horizon, C, H, W)
-        if hasattr(self, 'aug') and not eval_mode:
-            img_seq = self.aug(img_seq) # (B*obs_horizon, C, H, W)
-        visual_feature = self.visual_encoder(img_seq) # (B*obs_horizon, D)
-        visual_feature = visual_feature.reshape(rgb.shape[0], self.obs_horizon, visual_feature.shape[1]) # (B, obs_horizon, D)
-        feature = torch.cat((visual_feature, obs_seq['state']), dim=-1) # (B, obs_horizon, D+obs_state_dim)
-        return feature.flatten(start_dim=1) # (B, obs_horizon * (D+obs_state_dim))
-    
+        rgb = obs_seq["rgb"].float() / 255.0  # (B, obs_horizon, 3*k, H, W)
+        if args.depth:
+            depth = obs_seq["depth"].float()  # (B, obs_horizon, 1*k, H, W)
+            img_seq = torch.cat([rgb, depth], dim=2)  # (B, obs_horizon, C, H, W), C=4*k
+        else:
+            img_seq = rgb
+        img_seq = img_seq.flatten(end_dim=1)  # (B*obs_horizon, C, H, W)
+        if hasattr(self, "aug") and not eval_mode:
+            img_seq = self.aug(img_seq)  # (B*obs_horizon, C, H, W)
+        visual_feature = self.visual_encoder(img_seq)  # (B*obs_horizon, D)
+        visual_feature = visual_feature.reshape(
+            rgb.shape[0], self.obs_horizon, visual_feature.shape[1]
+        )  # (B, obs_horizon, D)
+        feature = torch.cat(
+            (visual_feature, obs_seq["state"]), dim=-1
+        )  # (B, obs_horizon, D+obs_state_dim)
+        return feature.flatten(start_dim=1)  # (B, obs_horizon * (D+obs_state_dim))
+
     def compute_loss(self, obs_seq, action_seq):
-        B = obs_seq['state'].shape[0]
+        B = obs_seq["state"].shape[0]
 
         # observation as FiLM conditioning
-        obs_cond = self.encode_obs(obs_seq, eval_mode=False) # (B, obs_horizon * obs_dim)
+        obs_cond = self.encode_obs(
+            obs_seq, eval_mode=False
+        )  # (B, obs_horizon * obs_dim)
 
         # sample noise to add to actions
         noise = torch.randn((B, self.pred_horizon, self.act_dim), device=device)
 
         # sample a diffusion iteration for each data point
         timesteps = torch.randint(
-            0, self.noise_scheduler.config.num_train_timesteps,
-            (B,), device=device
+            0, self.noise_scheduler.config.num_train_timesteps, (B,), device=device
         ).long()
 
         # add noise to the clean images(actions) according to the noise magnitude at each diffusion iteration
         # (this is the forward diffusion process)
-        noisy_action_seq = self.noise_scheduler.add_noise(
-            action_seq, noise, timesteps)
-        
+        noisy_action_seq = self.noise_scheduler.add_noise(action_seq, noise, timesteps)
+
         # predict the noise residual
         noise_pred = self.noise_pred_net(
-            noisy_action_seq, timesteps, global_cond=obs_cond)
+            noisy_action_seq, timesteps, global_cond=obs_cond
+        )
 
         return F.mse_loss(noise_pred, noise)
-    
+
     def get_action(self, obs_seq):
         # init scheduler
         # self.noise_scheduler.set_timesteps(self.num_diffusion_iters)
@@ -282,15 +332,20 @@ def get_action(self, obs_seq):
         # if we use DDPM, and inference_diffusion_steps == train_diffusion_steps, then we can skip this
 
         # obs_seq['state']: (B, obs_horizon, obs_state_dim)
-        B = obs_seq['state'].shape[0]
+        B = obs_seq["state"].shape[0]
         with torch.no_grad():
-            obs_seq['rgb'] = obs_seq['rgb'].permute(0,1,4,2,3)
-            obs_seq['depth'] = obs_seq['depth'].permute(0,1,4,2,3)
+            obs_seq["rgb"] = obs_seq["rgb"].permute(0, 1, 4, 2, 3)
+            if args.depth:
+                obs_seq["depth"] = obs_seq["depth"].permute(0, 1, 4, 2, 3) / 1024
 
-            obs_cond = self.encode_obs(obs_seq, eval_mode=True) # (B, obs_horizon * obs_dim)
+            obs_cond = self.encode_obs(
+                obs_seq, eval_mode=True
+            )  # (B, obs_horizon * obs_dim)
 
             # initialize action from Guassian noise
-            noisy_action_seq = torch.randn((B, self.pred_horizon, self.act_dim), device=obs_seq['state'].device)
+            noisy_action_seq = torch.randn(
+                (B, self.pred_horizon, self.act_dim), device=obs_seq["state"].device
+            )
 
             for k in self.noise_scheduler.timesteps:
                 # predict noise
@@ -310,7 +365,8 @@ def get_action(self, obs_seq):
         # only take act_horizon number of actions
         start = self.obs_horizon - 1
         end = start + self.act_horizon
-        return noisy_action_seq[:, start:end] # (B, act_horizon, act_dim)
+        return noisy_action_seq[:, start:end]  # (B, act_horizon, act_dim)
+
 
 if __name__ == "__main__":
     args = tyro.cli(Args)
@@ -320,19 +376,22 @@ def get_action(self, obs_seq):
         run_name = f"{args.env_id}__{args.exp_name}__{args.seed}__{int(time.time())}"
     else:
         run_name = args.exp_name
-    
-    if args.demo_path.endswith('.h5'):
+
+    if args.demo_path.endswith(".h5"):
         import json
-        json_file = args.demo_path[:-2] + 'json'
-        with open(json_file, 'r') as f:
+
+        json_file = args.demo_path[:-2] + "json"
+        with open(json_file, "r") as f:
             demo_info = json.load(f)
-            if 'control_mode' in demo_info['env_info']['env_kwargs']:
-                control_mode = demo_info['env_info']['env_kwargs']['control_mode']
-            elif 'control_mode' in demo_info['episodes'][0]:
-                control_mode = demo_info['episodes'][0]['control_mode']
+            if "control_mode" in demo_info["env_info"]["env_kwargs"]:
+                control_mode = demo_info["env_info"]["env_kwargs"]["control_mode"]
+            elif "control_mode" in demo_info["episodes"][0]:
+                control_mode = demo_info["episodes"][0]["control_mode"]
             else:
-                raise Exception('Control mode not found in json')
-            assert control_mode == args.control_mode, f"Control mode mismatched. Dataset has control mode {control_mode}, but args has control mode {args.control_mode}"
+                raise Exception("Control mode not found in json")
+            assert (
+                control_mode == args.control_mode
+            ), f"Control mode mismatched. Dataset has control mode {control_mode}, but args has control mode {args.control_mode}"
     assert args.obs_horizon + args.act_horizon - 1 <= args.pred_horizon
     assert args.obs_horizon >= 1 and args.act_horizon >= 1 and args.pred_horizon >= 1
 
@@ -343,15 +402,34 @@ def get_action(self, obs_seq):
 
     device = torch.device("cuda" if torch.cuda.is_available() and args.cuda else "cpu")
 
-    env_kwargs = dict(control_mode=args.control_mode, reward_mode="sparse", obs_mode="rgbd", render_mode="rgb_array")
+    env_kwargs = dict(
+        control_mode=args.control_mode,
+        reward_mode="sparse",
+        obs_mode="rgbd",
+        render_mode="rgb_array",
+    )
     if args.max_episode_steps is not None:
         env_kwargs["max_episode_steps"] = args.max_episode_steps
     other_kwargs = dict(obs_horizon=args.obs_horizon)
-    envs = make_eval_envs(args.env_id, args.num_eval_envs, args.sim_backend, env_kwargs, other_kwargs, video_dir=f'runs/{run_name}/videos' if args.capture_video else None, wrappers=[partial(FlattenRGBDObservationWrapper, sep_depth=True)])
+    envs = make_eval_envs(
+        args.env_id,
+        args.num_eval_envs,
+        args.sim_backend,
+        env_kwargs,
+        other_kwargs,
+        video_dir=f"runs/{run_name}/videos" if args.capture_video else None,
+        wrappers=[partial(FlattenRGBDObservationWrapper, sep_depth=True)],
+    )
     if args.track:
         import wandb
+
         config = vars(args)
-        config["eval_env_cfg"] = dict(**env_kwargs, num_envs=args.num_eval_envs, env_id=args.env_id, env_horizon=gym_utils.find_max_episode_steps_value(envs))
+        config["eval_env_cfg"] = dict(
+            **env_kwargs,
+            num_envs=args.num_eval_envs,
+            env_id=args.env_id,
+            env_horizon=gym_utils.find_max_episode_steps_value(envs),
+        )
         wandb.init(
             project=args.wandb_project_name,
             entity=args.wandb_entity,
@@ -360,22 +438,29 @@ def get_action(self, obs_seq):
             name=run_name,
             save_code=True,
             group="DiffusionPolicy",
-            tags=["diffusion_policy"]
+            tags=["diffusion_policy"],
         )
     writer = SummaryWriter(f"runs/{run_name}")
     writer.add_text(
         "hyperparameters",
-        "|param|value|\n|-|-|\n%s" % ("\n".join([f"|{key}|{value}|" for key, value in vars(args).items()])),
+        "|param|value|\n|-|-|\n%s"
+        % ("\n".join([f"|{key}|{value}|" for key, value in vars(args).items()])),
     )
 
     obs_process_fn = partial(
-        convert_obs, 
-        concat_fn=partial(np.concatenate, axis=-1), 
-        transpose_fn=partial(np.transpose, axes=(0, 3, 1, 2)), # (B, H, W, C) -> (B, C, H, W)
+        convert_obs,
+        concat_fn=partial(np.concatenate, axis=-1),
+        transpose_fn=partial(
+            np.transpose, axes=(0, 3, 1, 2)
+        ),  # (B, H, W, C) -> (B, C, H, W)
         state_obs_extractor=build_state_obs_extractor(args.env_id),
     )
-    tmp_env = gym.make(args.env_id, obs_mode='rgbd'); orignal_obs_space = tmp_env.observation_space; tmp_env.close()
-    dataset = SmallDemoDataset_DiffusionPolicy(args.demo_path, obs_process_fn, orignal_obs_space, args.num_demos)
+    tmp_env = gym.make(args.env_id, obs_mode="rgbd")
+    orignal_obs_space = tmp_env.observation_space
+    tmp_env.close()
+    dataset = SmallDemoDataset_DiffusionPolicy(
+        args.demo_path, obs_process_fn, orignal_obs_space, args.num_demos
+    )
     sampler = RandomSampler(dataset, replacement=False)
     batch_sampler = BatchSampler(sampler, batch_size=args.batch_size, drop_last=True)
     batch_sampler = IterationBasedBatchSampler(batch_sampler, args.total_iters)
@@ -401,12 +486,13 @@ def get_action(self, obs_seq):
 
     agent = Agent(envs, args).to(device)
 
-    optimizer = optim.AdamW(params=agent.parameters(), 
-        lr=args.lr, betas=(0.95, 0.999), weight_decay=1e-6)
+    optimizer = optim.AdamW(
+        params=agent.parameters(), lr=args.lr, betas=(0.95, 0.999), weight_decay=1e-6
+    )
 
     # Cosine LR schedule with linear warmup
     lr_scheduler = get_scheduler(
-        name='cosine',
+        name="cosine",
         optimizer=optimizer,
         num_warmup_steps=500,
         num_training_steps=args.total_iters,
@@ -422,7 +508,7 @@ def get_action(self, obs_seq):
     # Training begins.
     # ---------------------------------------------------------------------------- #
     agent.train()
-    
+
     best_eval_metrics = defaultdict(float)
     timings = defaultdict(float)
 
@@ -431,28 +517,32 @@ def get_action(self, obs_seq):
         # data_batch = {k: v.cuda(non_blocking=True) for k, v in data_batch.items()}
 
         # forward and compute loss
-        obs_batch_dict = data_batch['observations']
-        obs_batch_dict = {k: v.cuda(non_blocking=True) for k, v in obs_batch_dict.items()}
-        act_batch = data_batch['actions'].cuda(non_blocking=True)
+        obs_batch_dict = data_batch["observations"]
+        obs_batch_dict = {
+            k: v.cuda(non_blocking=True) for k, v in obs_batch_dict.items()
+        }
+        act_batch = data_batch["actions"].cuda(non_blocking=True)
 
         # forward and compute loss
         total_loss = agent.compute_loss(
-            obs_seq=obs_batch_dict, # obs_batch_dict['state'] is (B, L, obs_dim)
-            action_seq=act_batch, # (B, L, act_dim)
+            obs_seq=obs_batch_dict,  # obs_batch_dict['state'] is (B, L, obs_dim)
+            action_seq=act_batch,  # (B, L, act_dim)
         )
 
         # backward
         optimizer.zero_grad()
         total_loss.backward()
         optimizer.step()
-        lr_scheduler.step() # step lr scheduler every batch, this is different from standard pytorch behavior
+        lr_scheduler.step()  # step lr scheduler every batch, this is different from standard pytorch behavior
         last_tick = time.time()
 
         ema.step(agent.parameters())
 
         if iteration % args.log_freq == 0:
             print(f"Iteration {iteration}, loss: {total_loss.item()}")
-            writer.add_scalar("charts/learning_rate", optimizer.param_groups[0]["lr"], iteration)
+            writer.add_scalar(
+                "charts/learning_rate", optimizer.param_groups[0]["lr"], iteration
+            )
             writer.add_scalar("losses/total_loss", total_loss.item(), iteration)
             for k, v in timings.items():
                 writer.add_scalar(f"time/{k}", v, iteration)
@@ -463,7 +553,9 @@ def get_action(self, obs_seq):
             ema.copy_to(ema_agent.parameters())
             # def sample_fn(obs):
 
-            eval_metrics = evaluate(args.num_eval_episodes, ema_agent, envs, device, args.sim_backend)
+            eval_metrics = evaluate(
+                args.num_eval_episodes, ema_agent, envs, device, args.sim_backend
+            )
             timings["eval"] += time.time() - last_tick
 
             print(f"Evaluated {len(eval_metrics['success_at_end'])} episodes")
@@ -477,10 +569,12 @@ def get_action(self, obs_seq):
                 if k in eval_metrics and eval_metrics[k] > best_eval_metrics[k]:
                     best_eval_metrics[k] = eval_metrics[k]
                     save_ckpt(run_name, f"best_eval_{k}")
-                    print(f'New best {k}_rate: {eval_metrics[k]:.4f}. Saving checkpoint.')
-            
+                    print(
+                        f"New best {k}_rate: {eval_metrics[k]:.4f}. Saving checkpoint."
+                    )
+
         # Checkpoint
         if args.save_freq is not None and iteration % args.save_freq == 0:
             save_ckpt(run_name, str(iteration))
     envs.close()
-    writer.close()
\ No newline at end of file
+    writer.close()
diff --git a/mani_skill/envs/tasks/drawing/draw_triangle.py b/mani_skill/envs/tasks/drawing/draw_triangle.py
index 74e6a63bd..38b3fc365 100644
--- a/mani_skill/envs/tasks/drawing/draw_triangle.py
+++ b/mani_skill/envs/tasks/drawing/draw_triangle.py
@@ -230,7 +230,9 @@ def _initialize_episode(self, env_idx: torch.Tensor, options: dict):
 
             self.vertices = torch.from_numpy(
                 np.tile(self.original_verts, (b, 1, 1))
-            ).to(self.device) # b, 3, 3
+            ).to(
+                self.device
+            )  # b, 3, 3
             self.vertices = (
                 mats.double() @ self.vertices.transpose(-1, -2).double()
             ).transpose(
diff --git a/mani_skill/examples/motionplanning/panda_stick/motionplanner.py b/mani_skill/examples/motionplanning/panda_stick/motionplanner.py
index 097ad1748..5a423884b 100644
--- a/mani_skill/examples/motionplanning/panda_stick/motionplanner.py
+++ b/mani_skill/examples/motionplanning/panda_stick/motionplanner.py
@@ -2,12 +2,12 @@
 import numpy as np
 import sapien
 import trimesh
-
 from mani_skill.agents.base_agent import BaseAgent
 from mani_skill.envs.sapien_env import BaseEnv
 from mani_skill.envs.scene import ManiSkillScene
 from mani_skill.utils.structs.pose import to_sapien_pose
 
+
 class PandaStickMotionPlanningSolver:
     def __init__(
         self,
@@ -113,7 +113,7 @@ def move_to_pose_with_screw(
     ):
         pose = to_sapien_pose(pose)
         # try screw two times before giving up
-        pose = sapien.Pose(p=pose.p , q=pose.q)
+        pose = sapien.Pose(p=pose.p, q=pose.q)
         result = self.planner.plan_screw(
             np.concatenate([pose.p, pose.q]),
             self.robot.get_qpos().cpu().numpy()[0],

From 46c3b227650327df68b8b4034986efa576bb0148 Mon Sep 17 00:00:00 2001
From: Arnav G <73305591+arnavg115@users.noreply.github.com>
Date: Mon, 21 Oct 2024 19:58:27 +0000
Subject: [PATCH 09/14] Removed unused code

---
 .../diffusion_policy/utils.py                 | 30 -------------------
 1 file changed, 30 deletions(-)

diff --git a/examples/baselines/diffusion_policy/diffusion_policy/utils.py b/examples/baselines/diffusion_policy/diffusion_policy/utils.py
index bc270e19c..e3398ccee 100644
--- a/examples/baselines/diffusion_policy/diffusion_policy/utils.py
+++ b/examples/baselines/diffusion_policy/diffusion_policy/utils.py
@@ -207,33 +207,3 @@ def build_state_obs_extractor(env_id):
     else:
         raise NotImplementedError(f"Please tune state obs by yourself")
 
-
-class RandomShiftsAug(nn.Module):
-    """
-    from https://github.com/facebookresearch/drqv2/blob/main/drqv2.py
-    """
-
-    def __init__(self, pad):
-        super().__init__()
-        self.pad = pad
-
-    def forward(self, x):
-        n, c, h, w = x.size()
-        assert h == w
-        padding = tuple([self.pad] * 4)
-        x = F.pad(x, padding, "replicate")
-        eps = 1.0 / (h + 2 * self.pad)
-        arange = torch.linspace(
-            -1.0 + eps, 1.0 - eps, h + 2 * self.pad, device=x.device, dtype=x.dtype
-        )[:h]
-        arange = arange.unsqueeze(0).repeat(h, 1).unsqueeze(2)
-        base_grid = torch.cat([arange, arange.transpose(1, 0)], dim=2)
-        base_grid = base_grid.unsqueeze(0).repeat(n, 1, 1, 1)
-
-        shift = torch.randint(
-            0, 2 * self.pad + 1, size=(n, 1, 1, 2), device=x.device, dtype=x.dtype
-        )
-        shift *= 2.0 / (h + 2 * self.pad)
-
-        grid = base_grid + shift
-        return F.grid_sample(x, grid, padding_mode="zeros", align_corners=False)

From 4f598bd9947bb76a10e81cac018ede4f930d3b6f Mon Sep 17 00:00:00 2001
From: Arnav G <73305591+arnavg115@users.noreply.github.com>
Date: Tue, 22 Oct 2024 20:19:25 +0000
Subject: [PATCH 10/14] Made requested fixes, made bugfix to frame stack
 wrapper

---
 .../diffusion_policy/make_env.py                | 17 +++++++----------
 mani_skill/envs/tasks/drawing/draw_triangle.py  | 12 ++++++------
 .../panda_stick/solutions/draw_triangle.py      | 17 +++++++++++++++--
 mani_skill/utils/wrappers/flatten.py            |  1 -
 mani_skill/utils/wrappers/frame_stack.py        | 11 +++++++++++
 5 files changed, 39 insertions(+), 19 deletions(-)

diff --git a/examples/baselines/diffusion_policy/diffusion_policy/make_env.py b/examples/baselines/diffusion_policy/diffusion_policy/make_env.py
index 0a210d79f..d18f5270c 100644
--- a/examples/baselines/diffusion_policy/diffusion_policy/make_env.py
+++ b/examples/baselines/diffusion_policy/diffusion_policy/make_env.py
@@ -12,7 +12,7 @@
 from mani_skill.vector.wrappers.gymnasium import ManiSkillVectorEnv
 
 
-class DictFrameStack(GymFrameStack):
+class DictFrameStack(FrameStack):
     def __init__(
         self,
         env: gym.Env,
@@ -26,15 +26,11 @@ def __init__(
             num_stack (int): The number of frames to stack
             lz4_compress (bool): Use lz4 to compress the frames internally
         """
-        gym.utils.RecordConstructorArgs.__init__(
-            self, num_stack=num_stack, lz4_compress=lz4_compress
-        )
-        gym.ObservationWrapper.__init__(self, env)
-
-        self.num_stack = num_stack
-        self.lz4_compress = lz4_compress
-
-        self.frames = deque(maxlen=num_stack)
+        # gym.utils.RecordConstructorArgs.__init__(
+        #     self, num_stack=num_stack, lz4_compress=lz4_compress
+        # )
+        # gym.ObservationWrapper.__init__(self, env)
+        super().__init__(env, num_stack, lz4_compress)
 
         new_observation_space = gym.spaces.Dict()
         for k, v in self.observation_space.items():
@@ -43,6 +39,7 @@ def __init__(
             new_observation_space[k] = Box(low=low, high=high, dtype=v.dtype)
         self.observation_space = new_observation_space
 
+
     def observation(self, observation):
         """Converts the wrappers current frames to lazy frames.
 
diff --git a/mani_skill/envs/tasks/drawing/draw_triangle.py b/mani_skill/envs/tasks/drawing/draw_triangle.py
index 38b3fc365..c7b35b753 100644
--- a/mani_skill/envs/tasks/drawing/draw_triangle.py
+++ b/mani_skill/envs/tasks/drawing/draw_triangle.py
@@ -28,7 +28,7 @@ class DrawTriangle(BaseEnv):
     to create their own drawing type tasks.
     """
 
-    MAX_DOTS = 1010
+    MAX_DOTS = 300
     """
     The total "ink" available to use and draw with before you need to call env.reset. NOTE that on GPU simulation it is not recommended to have a very high value for this as it can slow down rendering
     when too many objects are being rendered in many scenes.
@@ -102,26 +102,26 @@ def create_goal_triangle(name="tri", base_color=None):
 
             box1_half_w = 0.3 / 2
             box1_half_h = 0.01 / 2
-            half_thickness = 0.04 / 2
+            half_thickness = 0.001 / 2
 
             radius = (box1_half_w) / math.sqrt(3)
 
             theta = np.pi / 2
 
             # define centers and compute verticies, might need to adjust how centers are calculated or add a theta arg for variation
-            c1 = np.array([radius * math.cos(theta), radius * math.sin(theta), 0.001])
+            c1 = np.array([radius * math.cos(theta), radius * math.sin(theta), 0.01])
             c2 = np.array(
                 [
                     radius * math.cos(theta + (2 * np.pi / 3)),
                     radius * math.sin(theta + (2 * np.pi / 3)),
-                    0.001,
+                    0.01,
                 ]
             )
             c3 = np.array(
                 [
                     radius * math.cos((theta + (4 * np.pi / 3))),
                     radius * math.sin(theta + (4 * np.pi / 3)),
-                    0.001,
+                    0.01,
                 ]
             )
             self.original_verts = np.array(
@@ -212,7 +212,7 @@ def create_goal_triangle(name="tri", base_color=None):
                 self.dots.append(actor)
         self.goal_tri = create_goal_triangle(
             name="goal_tri",
-            base_color=np.array([50, 50, 50, 200]) / 255,
+            base_color=np.array([50, 50, 50, 1]) / 255,
         )
 
     def _initialize_episode(self, env_idx: torch.Tensor, options: dict):
diff --git a/mani_skill/examples/motionplanning/panda_stick/solutions/draw_triangle.py b/mani_skill/examples/motionplanning/panda_stick/solutions/draw_triangle.py
index fbabb3f83..18cbdc594 100644
--- a/mani_skill/examples/motionplanning/panda_stick/solutions/draw_triangle.py
+++ b/mani_skill/examples/motionplanning/panda_stick/solutions/draw_triangle.py
@@ -1,9 +1,7 @@
-import numpy as np
 import sapien
 from mani_skill.envs.tasks import PushCubeEnv
 from mani_skill.examples.motionplanning.panda_stick.motionplanner import \
     PandaStickMotionPlanningSolver
-from PIL import Image
 
 
 def solve(env: PushCubeEnv, seed=None, debug=False, vis=False):
@@ -15,24 +13,39 @@ def solve(env: PushCubeEnv, seed=None, debug=False, vis=False):
         base_pose=env.unwrapped.agent.robot.pose,
         visualize_target_grasp_pose=vis,
         print_env_info=False,
+        joint_vel_limits = 0.3
     )
 
     FINGER_LENGTH = 0.025
     env = env.unwrapped
 
     rot = list(env.agent.tcp.pose.get_q()[0].cpu().numpy())
+
+    # -------------------------------------------------------------------------- #
+    # Move to first vertex
+    # -------------------------------------------------------------------------- #
+
     reach_pose = sapien.Pose(p=list(env.vertices[0, 0].numpy()), q=rot)
     res = planner.move_to_pose_with_screw(reach_pose)
+
     # -------------------------------------------------------------------------- #
     # Move to second vertex
     # -------------------------------------------------------------------------- #
 
     reach_pose = sapien.Pose(p=list(env.vertices[0, 1]), q=rot)
     res = planner.move_to_pose_with_screw(reach_pose)
+    
+    # -------------------------------------------------------------------------- #
+    # Move to third vertex
+    # -------------------------------------------------------------------------- #
 
     reach_pose = sapien.Pose(p=list(env.vertices[0, 2]), q=rot)
     res = planner.move_to_pose_with_screw(reach_pose)
 
+    # -------------------------------------------------------------------------- #
+    # Move back to first vertex
+    # -------------------------------------------------------------------------- #
+
     reach_pose = sapien.Pose(p=list(env.vertices[0, 0]), q=rot)
     res = planner.move_to_pose_with_screw(reach_pose)
 
diff --git a/mani_skill/utils/wrappers/flatten.py b/mani_skill/utils/wrappers/flatten.py
index 126dcbe82..989b95125 100644
--- a/mani_skill/utils/wrappers/flatten.py
+++ b/mani_skill/utils/wrappers/flatten.py
@@ -51,7 +51,6 @@ def observation(self, observation: Dict):
         if self.include_rgb and not self.include_depth:
             ret["rgb"] = images
         elif self.include_rgb and self.include_depth:
-            # todo: might not be the best way or the right place to perform this
             if self.sep_depth:
                 ret["rgb"] = images[...,:-1]
                 ret["depth"] = images[...,-1:]
diff --git a/mani_skill/utils/wrappers/frame_stack.py b/mani_skill/utils/wrappers/frame_stack.py
index 3a3e5c4d2..d8242abca 100644
--- a/mani_skill/utils/wrappers/frame_stack.py
+++ b/mani_skill/utils/wrappers/frame_stack.py
@@ -76,6 +76,15 @@ def __getitem__(self, int_or_slice: Union[int, slice]):
             [self._check_decompress(f) for f in self._frames[int_or_slice]], axis=0
         )
 
+    def _check_decompress(self, frame):
+        if self.lz4_compress:
+            from lz4.block import decompress
+
+            return np.frombuffer(decompress(frame), dtype=self.dtype).reshape(
+                self.frame_shape
+            )
+        return frame
+
     def __eq__(self, other):
         """Checks that the current frames are equal to the other object."""
         return self.__array__() == other
@@ -98,6 +107,7 @@ def __init__(
         self,
         env: gym.Env,
         num_stack: int,
+        lz4_compress: bool = False
     ):
         """Observation wrapper that stacks the observations in a rolling manner.
 
@@ -110,6 +120,7 @@ def __init__(
 
         self.num_stack = num_stack
         self.frames = deque(maxlen=num_stack)
+        self.lz4_compress = lz4_compress
         [self.frames.append(self.base_env._init_raw_obs) for _ in range(self.num_stack)]
         new_obs = self.observation(self.base_env._init_raw_obs)
         self.base_env.update_obs_space(new_obs)

From 7a52287e73eb008540355d4a246dae254b775e4c Mon Sep 17 00:00:00 2001
From: Arnav G <73305591+arnavg115@users.noreply.github.com>
Date: Tue, 22 Oct 2024 22:32:19 +0000
Subject: [PATCH 11/14] Edited make_env and frame_stack

---
 .../baselines/diffusion_policy/diffusion_policy/make_env.py    | 3 +--
 mani_skill/utils/wrappers/frame_stack.py                       | 3 +--
 2 files changed, 2 insertions(+), 4 deletions(-)

diff --git a/examples/baselines/diffusion_policy/diffusion_policy/make_env.py b/examples/baselines/diffusion_policy/diffusion_policy/make_env.py
index d18f5270c..f8543185a 100644
--- a/examples/baselines/diffusion_policy/diffusion_policy/make_env.py
+++ b/examples/baselines/diffusion_policy/diffusion_policy/make_env.py
@@ -5,12 +5,11 @@
 import mani_skill.envs
 import numpy as np
 from gymnasium.spaces import Box
-from gymnasium.wrappers.frame_stack import FrameStack as GymFrameStack
 from gymnasium.wrappers.frame_stack import LazyFrames
 from mani_skill.utils import gym_utils
 from mani_skill.utils.wrappers import CPUGymWrapper, FrameStack, RecordEpisode
 from mani_skill.vector.wrappers.gymnasium import ManiSkillVectorEnv
-
+# from mani_skill.utils.wrappers.frame_stack import LazyFrames
 
 class DictFrameStack(FrameStack):
     def __init__(
diff --git a/mani_skill/utils/wrappers/frame_stack.py b/mani_skill/utils/wrappers/frame_stack.py
index d8242abca..fb0aaf9ce 100644
--- a/mani_skill/utils/wrappers/frame_stack.py
+++ b/mani_skill/utils/wrappers/frame_stack.py
@@ -79,8 +79,7 @@ def __getitem__(self, int_or_slice: Union[int, slice]):
     def _check_decompress(self, frame):
         if self.lz4_compress:
             from lz4.block import decompress
-
-            return np.frombuffer(decompress(frame), dtype=self.dtype).reshape(
+            return torch.frombuffer(decompress(frame), dtype=self.dtype).reshape(
                 self.frame_shape
             )
         return frame

From 7adcd5dee72e2e0df653e49318b2490bc25adb71 Mon Sep 17 00:00:00 2001
From: Arnav G <73305591+arnavg115@users.noreply.github.com>
Date: Thu, 24 Oct 2024 19:17:01 +0000
Subject: [PATCH 12/14] Added state obs to draw triangle

---
 examples/baselines/diffusion_policy/examples.sh | 14 +++++++++++++-
 mani_skill/envs/tasks/drawing/draw_triangle.py  | 14 ++++++++++++--
 2 files changed, 25 insertions(+), 3 deletions(-)

diff --git a/examples/baselines/diffusion_policy/examples.sh b/examples/baselines/diffusion_policy/examples.sh
index d59255b05..5739b2851 100644
--- a/examples/baselines/diffusion_policy/examples.sh
+++ b/examples/baselines/diffusion_policy/examples.sh
@@ -44,4 +44,16 @@ python -m mani_skill.trajectory.replay_trajectory \
 python train.py --env-id PegInsertionSide-v1 \
   --demo-path ~/.maniskill/demos/PegInsertionSide-v1/motionplanning/trajectory.state.pd_ee_delta_pose.cpu.h5 \
   --control-mode "pd_ee_delta_pose" --sim-backend "cpu" --num-demos 100 --max_episode_steps 300 \
-  --total_iters 300000
\ No newline at end of file
+  --total_iters 300000
+
+# DrawTriangle-v1
+python -m mani_skill.trajectory.replay_trajectory \
+  --traj-path ~/.maniskill/demos/DrawTriangle-v1/motionplanning/trajectory.h5 \
+  --use-first-env-state -c pd_ee_delta_pose -o state \
+  --save-traj --num-procs 10 -b cpu
+
+# RGBD based observations
+python train_rgbd.py --env-id DrawTriangle-v1 \
+  --demo-path ~/.maniskill/demos/DrawTriangle-v1/motionplanning/trajectory.rgbd.pd_ee_delta_pose.cpu.h5
+  --control-mode "pd_ee_delta_pose" --sim-backend "cpu" --num-demos 100 --max_episode_steps 300 \
+  --total-iters 300000
\ No newline at end of file
diff --git a/mani_skill/envs/tasks/drawing/draw_triangle.py b/mani_skill/envs/tasks/drawing/draw_triangle.py
index c7b35b753..9547e561e 100644
--- a/mani_skill/envs/tasks/drawing/draw_triangle.py
+++ b/mani_skill/envs/tasks/drawing/draw_triangle.py
@@ -212,7 +212,7 @@ def create_goal_triangle(name="tri", base_color=None):
                 self.dots.append(actor)
         self.goal_tri = create_goal_triangle(
             name="goal_tri",
-            base_color=np.array([50, 50, 50, 1]) / 255,
+            base_color=np.array([10, 10, 10,255]) / 255,
         )
 
     def _initialize_episode(self, env_idx: torch.Tensor, options: dict):
@@ -293,10 +293,20 @@ def evaluate(self):
         return {"success": out}
 
     def _get_obs_extra(self, info: Dict):
-        return dict(
+        obs = dict(
             tcp_pose=self.agent.tcp.pose.raw_pose,
         )
 
+        if "state" in self.obs_mode:
+            obs.update(
+                goal_pose = self.goal_tri.pose.raw_pose,
+                tcp_to_verts_pos = self.vertices - self.agent.tcp.pose.p.unsqueeze(1),
+                goal_pos=self.goal_tri.pose.p,
+                vertices = self.vertices
+            )
+
+        return obs
+
     def generate_triangle_with_points(self, n, vertices):
         batch_size = vertices.shape[0]
 

From a209f59a7c61fa900fc7db4ac47be0b7aa8f2b23 Mon Sep 17 00:00:00 2001
From: "Arnav G." <73305591+arnavg115@users.noreply.github.com>
Date: Thu, 24 Oct 2024 12:21:12 -0700
Subject: [PATCH 13/14] Update draw_triangle max steps

---
 mani_skill/envs/tasks/drawing/draw_triangle.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/mani_skill/envs/tasks/drawing/draw_triangle.py b/mani_skill/envs/tasks/drawing/draw_triangle.py
index 9547e561e..4353dddf6 100644
--- a/mani_skill/envs/tasks/drawing/draw_triangle.py
+++ b/mani_skill/envs/tasks/drawing/draw_triangle.py
@@ -21,7 +21,7 @@
 from transforms3d.quaternions import quat2mat
 
 
-@register_env("DrawTriangle-v1", max_episode_steps=1000)
+@register_env("DrawTriangle-v1", max_episode_steps=300)
 class DrawTriangle(BaseEnv):
     """
     This is a simple environment demonstrating drawing simulation on a table with a robot arm. There are no success/rewards defined, users can use this code as a starting point

From b5528d6d20db217762db0ab513caf4652869f603 Mon Sep 17 00:00:00 2001
From: Arnav G <73305591+arnavg115@users.noreply.github.com>
Date: Sat, 26 Oct 2024 02:15:03 +0000
Subject: [PATCH 14/14] Added DrawTriangle Docs

---
 docs/source/tasks/drawing/index.md            |  23 ++++++++++++++++++
 .../environment_demos/DrawTriangle-v1_rt.mp4  | Bin 0 -> 410912 bytes
 .../envs/tasks/drawing/draw_triangle.py       |   5 ----
 3 files changed, 23 insertions(+), 5 deletions(-)
 create mode 100644 figures/environment_demos/DrawTriangle-v1_rt.mp4

diff --git a/docs/source/tasks/drawing/index.md b/docs/source/tasks/drawing/index.md
index daf03fe02..ad304f349 100644
--- a/docs/source/tasks/drawing/index.md
+++ b/docs/source/tasks/drawing/index.md
@@ -26,3 +26,26 @@ None
 <video preload="auto" controls="True" width="100%">
 <source src="https://github.com/haosulab/ManiSkill/raw/main/figures/environment_demos/TableTopFreeDraw-v1_rt.mp4" type="video/mp4">
 </video>
+
+## DrawTriangle-v1
+
+:::{dropdown} Task Card
+:icon: note
+:color: primary
+
+**Task Description:**
+Instantiates a table with a white canvas on it and a goal triangle with an outline. A robot with a stick is to draw the triangle with a red line.
+
+**Supported Robots: PandaStick**
+
+**Randomizations:**
+- the goal triangle's position on the xy-plane is randomized
+- the goal triangle's z-rotation is randomized in range [0, 2 $\pi$]
+
+**Success Conditions:**
+- the drawn points by the robot are within a euclidean distance of 0.05m with points on the goal triangle
+:::
+
+<video preload="auto" controls="True" width="100%">
+<source src="https://github.com/haosulab/ManiSkill/raw/figures/environment_demos/DrawTriangle-v1_rt.mp4" type="video/mp4">
+</video>
\ No newline at end of file
diff --git a/figures/environment_demos/DrawTriangle-v1_rt.mp4 b/figures/environment_demos/DrawTriangle-v1_rt.mp4
new file mode 100644
index 0000000000000000000000000000000000000000..40d1fa784e44e6f481b7fdb5fea3083fdaa13940
GIT binary patch
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HcmV?d00001

diff --git a/mani_skill/envs/tasks/drawing/draw_triangle.py b/mani_skill/envs/tasks/drawing/draw_triangle.py
index 4353dddf6..9bd1c10f0 100644
--- a/mani_skill/envs/tasks/drawing/draw_triangle.py
+++ b/mani_skill/envs/tasks/drawing/draw_triangle.py
@@ -23,11 +23,6 @@
 
 @register_env("DrawTriangle-v1", max_episode_steps=300)
 class DrawTriangle(BaseEnv):
-    """
-    This is a simple environment demonstrating drawing simulation on a table with a robot arm. There are no success/rewards defined, users can use this code as a starting point
-    to create their own drawing type tasks.
-    """
-
     MAX_DOTS = 300
     """
     The total "ink" available to use and draw with before you need to call env.reset. NOTE that on GPU simulation it is not recommended to have a very high value for this as it can slow down rendering