import shutil from pathlib import Path import numpy as np import torch from lerobot.common.datasets.lerobot_dataset import LEROBOT_HOME, LeRobotDataset from lerobot.common.datasets.push_dataset_to_hub._download_raw import download_raw PUSHT_TASK = "Push the T-shaped blue block onto the T-shaped green target surface." PUSHT_FEATURES = { "observation.state": { "dtype": "float32", "shape": (2,), "names": { "axes": ["x", "y"], }, }, "action": { "dtype": "float32", "shape": (2,), "names": { "axes": ["x", "y"], }, }, "next.reward": { "dtype": "float32", "shape": (1,), "names": None, }, "next.success": { "dtype": "bool", "shape": (1,), "names": None, }, "observation.environment_state": { "dtype": "float32", "shape": (16,), "names": [ "keypoints", ], }, "observation.image": { "dtype": None, "shape": (3, 96, 96), "names": [ "channel", "height", "width", ], }, } def build_features(mode: str) -> dict: features = PUSHT_FEATURES if mode == "keypoints": features.pop("observation.image") else: features.pop("observation.environment_state") features["observation.image"]["dtype"] = mode return features def load_raw_dataset(zarr_path: Path): try: from lerobot.common.datasets.push_dataset_to_hub._diffusion_policy_replay_buffer import ( ReplayBuffer as DiffusionPolicyReplayBuffer, ) except ModuleNotFoundError as e: print( "`gym_pusht` is not installed. Please install it with `pip install 'lerobot[gym_pusht]'`" ) raise e zarr_data = DiffusionPolicyReplayBuffer.copy_from_path(zarr_path) return zarr_data def calculate_coverage(zarr_data): try: import pymunk from gym_pusht.envs.pusht import PushTEnv, pymunk_to_shapely except ModuleNotFoundError as e: print( "`gym_pusht` is not installed. Please install it with `pip install 'lerobot[gym_pusht]'`" ) raise e block_pos = zarr_data["state"][:, 2:4] block_angle = zarr_data["state"][:, 4] num_frames = len(block_pos) coverage = np.zeros((num_frames,)) # 8 keypoints with 2 coords each keypoints = np.zeros((num_frames, 16)) # Set x, y, theta (in radians) goal_pos_angle = np.array([256, 256, np.pi / 4]) goal_body = PushTEnv.get_goal_pose_body(goal_pos_angle) for i in range(num_frames): space = pymunk.Space() space.gravity = 0, 0 space.damping = 0 # Add walls. walls = [ PushTEnv.add_segment(space, (5, 506), (5, 5), 2), PushTEnv.add_segment(space, (5, 5), (506, 5), 2), PushTEnv.add_segment(space, (506, 5), (506, 506), 2), PushTEnv.add_segment(space, (5, 506), (506, 506), 2), ] space.add(*walls) block_body, block_shapes = PushTEnv.add_tee( space, block_pos[i].tolist(), block_angle[i].item() ) goal_geom = pymunk_to_shapely(goal_body, block_body.shapes) block_geom = pymunk_to_shapely(block_body, block_body.shapes) intersection_area = goal_geom.intersection(block_geom).area goal_area = goal_geom.area coverage[i] = intersection_area / goal_area keypoints[i] = torch.from_numpy(PushTEnv.get_keypoints(block_shapes).flatten()) return coverage, keypoints def calculate_success(coverage: float, success_threshold: float): return coverage > success_threshold def calculate_reward(coverage: float, success_threshold: float): return np.clip(coverage / success_threshold, 0, 1) def main(raw_dir: Path, repo_id: str, mode: str = "video", push_to_hub: bool = True): if mode not in ["video", "image", "keypoints"]: raise ValueError(mode) if (LEROBOT_HOME / repo_id).exists(): shutil.rmtree(LEROBOT_HOME / repo_id) if not raw_dir.exists(): download_raw(raw_dir, repo_id="lerobot-raw/pusht_raw") zarr_data = load_raw_dataset(zarr_path=raw_dir / "pusht_cchi_v7_replay.zarr") env_state = zarr_data["state"][:] agent_pos = env_state[:, :2] action = zarr_data["action"][:] image = zarr_data["img"] # (b, h, w, c) episode_data_index = { "from": np.concatenate(([0], zarr_data.meta["episode_ends"][:-1])), "to": zarr_data.meta["episode_ends"], } # Calculate success and reward based on the overlapping area # of the T-object and the T-area. coverage, keypoints = calculate_coverage(zarr_data) success = calculate_success(coverage, success_threshold=0.95) reward = calculate_reward(coverage, success_threshold=0.95) features = build_features(mode) dataset = LeRobotDataset.create( repo_id=repo_id, fps=10, robot_type="2d pointer", features=features, image_writer_threads=4, ) episodes = range(len(episode_data_index["from"])) for ep_idx in episodes: from_idx = episode_data_index["from"][ep_idx] to_idx = episode_data_index["to"][ep_idx] num_frames = to_idx - from_idx for frame_idx in range(num_frames): i = from_idx + frame_idx frame = { "action": torch.from_numpy(action[i]), # Shift reward and success by +1 until the last item of the episode "next.reward": reward[i + (frame_idx < num_frames - 1)], "next.success": success[i + (frame_idx < num_frames - 1)], } frame["observation.state"] = torch.from_numpy(agent_pos[i]) if mode == "keypoints": frame["observation.environment_state"] = torch.from_numpy(keypoints[i]) else: frame["observation.image"] = torch.from_numpy(image[i]) dataset.add_frame(frame) dataset.save_episode(task=PUSHT_TASK) dataset.consolidate() if push_to_hub: dataset.push_to_hub() if __name__ == "__main__": # To try this script, modify the repo id with your own HuggingFace user (e.g cadene/pusht) repo_id = "lerobot/pusht" modes = ["video", "image", "keypoints"] # Uncomment if you want to try with a specific mode # modes = ["video"] # modes = ["image"] # modes = ["keypoints"] raw_dir = Path("data/lerobot-raw/pusht_raw") for mode in modes: if mode in ["image", "keypoints"]: repo_id += f"_{mode}" # download and load raw dataset, create LeRobotDataset, populate it, push to hub main(raw_dir, repo_id=repo_id, mode=mode) # Uncomment if you want to load the local dataset and explore it # dataset = LeRobotDataset(repo_id=repo_id, local_files_only=True) # breakpoint()