diff --git a/lerobot/common/datasets/lerobot_dataset.py b/lerobot/common/datasets/lerobot_dataset.py index eb76f78d6..b90ae7ab5 100644 --- a/lerobot/common/datasets/lerobot_dataset.py +++ b/lerobot/common/datasets/lerobot_dataset.py @@ -126,8 +126,8 @@ def tolerance_s(self) -> float: are not close enough from the requested frames. It is only used when `delta_timestamps` is provided or when loading video frames from mp4 files. """ - # 1e-4 to account for possible numerical error - return 1 / self.fps - 1e-4 + # Add 1e-4 seconds to expected step size to account for possible numerical error + return 1 / self.fps + 1e-4 def __len__(self): return self.num_samples @@ -357,8 +357,8 @@ def tolerance_s(self) -> float: are not close enough from the requested frames. It is only used when `delta_timestamps` is provided or when loading video frames from mp4 files. """ - # 1e-4 to account for possible numerical error - return 1 / self.fps - 1e-4 + # Add 1e-4 seconds to expected step size to account for possible numerical error + return 1 / self.fps + 1e-4 def __len__(self): return self.num_samples diff --git a/lerobot/configs/env/koch_drake_leader_arm_teleop.yaml b/lerobot/configs/env/koch_drake_leader_arm_teleop.yaml new file mode 100644 index 000000000..393d9a2b4 --- /dev/null +++ b/lerobot/configs/env/koch_drake_leader_arm_teleop.yaml @@ -0,0 +1,4 @@ +observation_mode: "both" +action_mode: "joint" +render_mode: "human" +cube_file_path: "{ASSETS_PATH}/red_cube.sdf" \ No newline at end of file diff --git a/lerobot/configs/env/koch_drake_leader_arm_teleop_replay_1.yaml b/lerobot/configs/env/koch_drake_leader_arm_teleop_replay_1.yaml new file mode 100644 index 000000000..24c7f9274 --- /dev/null +++ b/lerobot/configs/env/koch_drake_leader_arm_teleop_replay_1.yaml @@ -0,0 +1,4 @@ +observation_mode: "both" +action_mode: "joint" +render_mode: "human" +cube_file_path: "{ASSETS_PATH}/green_cube.sdf" \ No newline at end of file diff --git a/lerobot/configs/env/koch_drake_leader_arm_teleop_replay_2.yaml b/lerobot/configs/env/koch_drake_leader_arm_teleop_replay_2.yaml new file mode 100644 index 000000000..1e549576c --- /dev/null +++ b/lerobot/configs/env/koch_drake_leader_arm_teleop_replay_2.yaml @@ -0,0 +1,4 @@ +observation_mode: "both" +action_mode: "joint" +render_mode: "human" +cube_file_path: "{ASSETS_PATH}/blue_cube.sdf" \ No newline at end of file diff --git a/lerobot/scripts/record_and_replay_teleop_dataset.py b/lerobot/scripts/record_and_replay_teleop_dataset.py new file mode 100644 index 000000000..7753dba1f --- /dev/null +++ b/lerobot/scripts/record_and_replay_teleop_dataset.py @@ -0,0 +1,766 @@ +"""This script demonstrates how to record a LeRobot dataset of training data using a very simple gym environment. + +Open your browser to: http://localhost:7000/ for visualization. + +run +python3 lerobot/scripts/record_and_replay_teleop_dataset.py + +Control with arrows, to end recording hit END. +Look in 'data_traces/{1,2,3}/' for videos + +The following videos should result: +1) In "data_traces/1//videos" recordings of the teleop session with a red block. +2) In "data_traces/2//videos" recordings of a replay of session 1 (from disk) with a blue block. +3) In "data_traces/3//videos" recordings of a replay of session 1 (from program memory) with a green block. + +""" + +import array +from turtle import position +from typing import NamedTuple +from pynput.keyboard import Key +from pynput import keyboard +import argparse +import copy +import os +import sys +import pathlib +import time +import importlib +import types +import logging + +import gymnasium as gym +import numpy as np +import lerobot.common +import lerobot.common.utils +import lerobot.common.utils.utils +import torch +import lerobot + +from datasets import Dataset, Features, Sequence, Value +from lerobot.common.datasets.compute_stats import compute_stats +from lerobot.common.datasets.lerobot_dataset import CODEBASE_VERSION, DATA_DIR, LeRobotDataset +from lerobot.common.datasets.push_dataset_to_hub.utils import concatenate_episodes, save_images_concurrently +from lerobot.common.datasets.utils import ( + hf_transform_to_torch, +) + +from lerobot.common.datasets.video_utils import VideoFrame, encode_video_frames +from lerobot.scripts.push_dataset_to_hub import push_meta_data_to_hub, push_videos_to_hub, save_meta_data +from tqdm import tqdm + +# Predeclarations +args = None +output_helper = None +episode_state = None + +########################################################################################## +# Reading and writing datasets + + +class ReplayHelper: + def __init__(self, *, lerobot_dataset: pathlib.PosixPath | LeRobotDataset): + if isinstance(lerobot_dataset, pathlib.PosixPath): + self.dataset = LeRobotDataset( + repo_id=args.repo_id, + root=lerobot_dataset + ) + elif isinstance(lerobot_dataset, LeRobotDataset): + self.dataset = lerobot_dataset + else: + raise ValueError( + "Input value `lerobot_dataset` has an unexpected type: {}".format(type(lerobot_dataset))) + + print("dataset={}".format(self.dataset)) + self.frame_index = 0 + + def increment_frame_index(self): + self.frame_index += 1 + + def get_action_at_frame(self): + return self.dataset[self.frame_index]["action"] + + def get_state_observation_at_next_frame(self): + if self.is_at_last_frame_in_dataset: + return None + if self.is_at_last_frame_in_episode: + return None + return self.dataset[self.frame_index+1]["observation.state"] + + @property + def is_at_last_frame_in_episode(self): + return self.dataset[self.frame_index]["episode_index"] != self.dataset[self.frame_index + 1]["episode_index"] + + @property + def is_at_last_frame_in_dataset(self): + return self.dataset.num_samples-1 == self.frame_index + + +class OutputHelper: + def __init__(self, *, repo_id): + self.repo_id = repo_id + self.dataset_counter = -1 + + def maybe_set_up_data_keys(self, observation): + if len(self.image_keys) + len(self.state_keys) == 0: + self.set_up_data_keys(observation) + + def set_up_data_keys(self, observation): + """ + Preconditions: + - len(self.state_keys) == 0 + - len(self.image_keys) == 0 + """ + assert len(self.state_keys) == 0 + assert len(self.image_keys) == 0 + for key, value in observation.items(): + if len(value.shape) == 1: + self.state_keys.append(key) + elif len(value.shape) == 3: + self.image_keys.append(key) + else: + raise ValueError( + "{} has unrecognised shape {}!".format(key, value.shape)) + + def reset_dataset_temp_data(self): + self.ep_dicts = [] + self.episode_data_index = {"from": [], "to": []} + self.episode_fps = [] + self.id_from = 0 + self.id_to = 0 + self.image_keys = [] + self.state_keys = [] + + def vectorize_state_observations(self, observations): + states = [] + for state_name in self.state_keys: + states.append(np.array(observations[state_name])) + return states + + def tensorize_state_observations(self, observations): + states = self.vectorize_state_observations(observations) + state = torch.tensor(np.concatenate(states, axis=1)) + return state + + def save_episode_data(self, *, num_steps: int, episode_index: int, observations, actions, timestamps): + """ + Preconditions: + - self.dataset_counter >= 0 + """ + assert self.dataset_counter >= 0 + ep_dict = {} + # store images in png and create the video + for img_key in self.image_keys: + images = observations[img_key] + save_images_concurrently( + images, + self.images_data_path / + f"{img_key}_episode_{episode_index:06d}", + args.num_workers, + ) + fname = f"{img_key}_episode_{episode_index:06d}.mp4" + + # store the reference to the video frame + ep_dict[f"observation.{img_key}"] = [ + {"path": f"videos/{fname}", "timestamp": timestamp} for timestamp in timestamps] + + action = torch.tensor(np.array(actions)) + next_done = torch.zeros(num_steps, dtype=torch.bool) + next_done[-1] = True + + ep_dict["observation.state"] = self.tensorize_state_observations( + observations) + ep_dict["action"] = action + ep_dict["episode_index"] = torch.tensor( + [episode_index] * num_steps, dtype=torch.int64) + ep_dict["frame_index"] = torch.arange(0, num_steps, 1) + ep_dict["timestamp"] = torch.tensor(timestamps) + ep_dict["next.done"] = next_done + + print(f"num_steps={num_steps}") + print(f"timestamps[-1]={timestamps[-1]}") + self.episode_fps.append(num_steps / timestamps[-1]) + self.ep_dicts.append(ep_dict) + print("Episode {} done, fps: {:.2f}".format( + episode_index, self.episode_fps[-1])) + + self.episode_data_index["from"].append(self.id_from) + self.episode_data_index["to"].append(self.id_from + num_steps) + + self.id_to = self.id_from + num_steps + self.id_from = self.id_to + + def save_data_and_maybe_push_to_hub(self) -> LeRobotDataset: + self.encode_video_frames(episode_fps=self.episode_fps) + + hf_dataset = self.construct_hf_dataset(ep_dicts=self.ep_dicts) + lerobot_dataset = self.construct_lerobot_dataset_and_save_to_disk( + hf_dataset=hf_dataset, episode_data_index=self.episode_data_index, episode_fps=self.episode_fps) + + self.maybe_push_to_hub(hf_dataset=hf_dataset, + repo_id=args.repo_id, + revision=args.revision) + return lerobot_dataset + + def increment_dataset_counter(self): + self.dataset_counter += 1 + self.set_up_output_path() + self.reset_dataset_temp_data() + + # During data collection, all data written to disk is stored here. + @property + def output_data_path(self) -> str: + # Use DATA_DIR, if available + if lerobot.common.datasets.lerobot_dataset.DATA_DIR is None: + lerobot.common.datasets.lerobot_dataset.DATA_DIR = pathlib.Path( + "data_traces") + print( + "Warning: env variable DATA_DIR was not set, defaulting to './{}'." + .format(lerobot.common.datasets.lerobot_dataset.DATA_DIR)) + + dataset_index = self.dataset_counter + + return lerobot.common.datasets.lerobot_dataset.DATA_DIR / str(self.dataset_counter) + + # Where to save the HF Dataset + + @property + def hf_dataset_path(self) -> str: + return self.output_data_path / args.repo_id / "train" + + # During data collection, frames are stored here as png images. + @property + def images_data_path(self) -> str: + return self.output_data_path / args.repo_id / "images" + + # After data collection, png images of each episode are encoded into an mp4 file stored here. + @property + def videos_data_path(self) -> str: + return self.output_data_path / args.repo_id / "videos" + + # After data collection, meta data is stored here. + @property + def meta_data_path(self) -> str: + return self.output_data_path / args.repo_id / "meta_data" + + def set_up_output_path(self): + # Create image and video directories + if not os.path.exists(self.images_data_path): + os.makedirs(self.images_data_path, exist_ok=True) + if not os.path.exists(self.videos_data_path): + os.makedirs(self.videos_data_path, exist_ok=True) + + def encode_video_frames(self, *, episode_fps): + print("encode video frames") + for ep_idx in range(len(episode_fps)): + for img_key in self.image_keys: + encode_video_frames( + imgs_dir=self.images_data_path / + f"{img_key}_episode_{ep_idx:06d}", + video_path=self.videos_data_path / + f"{img_key}_episode_{ep_idx:06d}.mp4", + fps=episode_fps[ep_idx], + ) + + def construct_hf_dataset(self, *, ep_dicts): + print("concatenate episodes") + # Since our fps varies we are sometimes off tolerance for the last frame + data_dict = concatenate_episodes(ep_dicts) + + features = {} + + keys = [key for key in data_dict if "observation.image_" in key] + for key in keys: + features[key.replace("observation.image_", + "observation.images.")] = VideoFrame() + data_dict[key.replace("observation.image_", + "observation.images.")] = data_dict[key] + del data_dict[key] + + features["observation.state"] = Sequence( + length=data_dict["observation.state"].shape[1], feature=Value( + dtype="float32", id=None) + ) + features["action"] = Sequence( + length=data_dict["action"].shape[1], feature=Value(dtype="float32", id=None)) + features["episode_index"] = Value(dtype="int64", id=None) + features["frame_index"] = Value(dtype="int64", id=None) + features["timestamp"] = Value(dtype="float32", id=None) + features["next.done"] = Value(dtype="bool", id=None) + features["index"] = Value(dtype="int64", id=None) + + hf_dataset = Dataset.from_dict( + data_dict, features=Features(features)) + hf_dataset.set_transform(hf_transform_to_torch) + return hf_dataset + + def construct_lerobot_dataset_and_save_to_disk(self, *, hf_dataset, episode_data_index, episode_fps) -> LeRobotDataset: + info = { + # to have a good tolerance in data processing for the slowest video + "fps": sum(episode_fps) / len(episode_fps), + "video": 1, + } + + print("from preloaded") + lerobot_dataset = LeRobotDataset.from_preloaded( + repo_id=args.repo_id, + hf_dataset=hf_dataset, + episode_data_index=episode_data_index, + info=info, + videos_dir=self.videos_data_path, + ) + + print("compute stats") + stats = compute_stats( + lerobot_dataset, num_workers=args.num_workers) + + print("save to disk") + # to remove transforms that cant be saved + hf_dataset = hf_dataset.with_format(None) + hf_dataset.save_to_disk(self.hf_dataset_path) + + save_meta_data(info, stats, episode_data_index, self.meta_data_path) + + return lerobot_dataset + + def maybe_push_to_hub(self, *, hf_dataset, repo_id: str, revision: str): + if not args.push_to_hub: + return + + print(f"Pushing dataset to '{repo_id}'") + hf_dataset.push_to_hub(repo_id, token=True, revision="main") + hf_dataset.push_to_hub(repo_id, token=True, revision=revision) + + push_meta_data_to_hub(repo_id, self.meta_data_path, revision="main") + push_meta_data_to_hub( + repo_id, self.meta_data_path, revision=revision) + + push_videos_to_hub(repo_id, self.videos_data_path, revision="main") + push_videos_to_hub(repo_id, self.videos_data_path, revision=revision) + + +########################################################################################## +# Controlling the robot (teleop) and data recording + +class EpisodeState(): + def __init__(self): + self.teleoperation_action = None + self.is_dropping_episode: bool | None = None + self.is_concluding_episode: bool | None = None + self.is_stopping: bool | None = None + + +def set_up_keyboard_teleop(): + print("Setting up keyboard teleop...") + + print("Keyboard controls:") + print("* UP|DOWN : move hand forwards|backwards (+y|-y)") + print("* LEFT|RIGHT : move hand left|right (-x|+x)") + print("* PAGE UP|PAGE DOWN : move hand up|down (+z|-z)") + print("* CMD|SHIFT : move gripper open|close") + print("* DELETE : Discard Episode and reset env") + print("* HOME : Save episode and reset env") + print("* END : Save episode and end data collection") + + episode_state.teleoperation_action = None + episode_state.is_dropping_episode = False + episode_state.is_concluding_episode = False + episode_state.is_stopping = False + + def on_press(key): + if np.isnan(np.linalg.norm(episode_state.teleoperation_action)): + # Assign good initial action + # FIXME: This is robot-specific (Low-Cost Robot Arm) + episode_state.teleoperation_action[0] = 0.0 + episode_state.teleoperation_action[1] = 0.14 + episode_state.teleoperation_action[2] = 0.17 + episode_state.teleoperation_action[3] = 0.0 + assert np.isfinite(np.linalg.norm(episode_state.teleoperation_action)) + + # Y + if key == Key.up: + episode_state.teleoperation_action[1] += 0.01 + elif key == Key.down: + episode_state.teleoperation_action[1] -= 0.01 + # X + elif key == Key.left: + episode_state.teleoperation_action[0] -= 0.01 + elif key == Key.right: + episode_state.teleoperation_action[0] += 0.01 + # Z + elif key == Key.page_down: + episode_state.teleoperation_action[2] -= 0.01 + elif key == Key.page_up: + episode_state.teleoperation_action[2] += 0.01 + # Gripper + elif key == Key.ctrl: + episode_state.teleoperation_action[3] -= 0.1 + elif key == Key.shift: + episode_state.teleoperation_action[3] += 0.1 + # Training + elif key == Key.delete: + episode_state.is_dropping_episode = True + episode_state.is_concluding_episode = True + elif key == Key.home: + episode_state.is_concluding_episode = True + elif key == Key.end: + episode_state.is_concluding_episode = True + episode_state.is_stopping = True + + listener = keyboard.Listener(on_press=on_press) + + def start_fn(): + listener.start() + + def stop_fn(): + listener.stop() + + # assign teleoperation shut-down function. + return [start_fn, stop_fn] + + +def set_up_arm_teleop(): + episode_state.teleoperation_action = None + episode_state.is_dropping_episode = False + episode_state.is_concluding_episode = False + episode_state.is_stopping = False + + print("Setting up keyboard & Arm teleop...") + + print("Keyboard controls:") + print("* DELETE : Discard Episode and reset env") + print("* HOME : Save episode and reset env") + print("* END : Save episode and end data collection") + + episode_state.teleoperation_action = None + episode_state.is_dropping_episode = False + episode_state.is_concluding_episode = False + episode_state.is_stopping = False + + def on_press(key): + # Training + if key == Key.delete: + episode_state.is_dropping_episode = True + episode_state.is_concluding_episode = True + elif key == Key.home: + episode_state.is_concluding_episode = True + elif key == Key.end: + episode_state.is_concluding_episode = True + episode_state.is_stopping = True + + listener = keyboard.Listener(on_press=on_press) + + # Separate process to get data from arm. + import multiprocessing + + def update_controls(joint_commands: multiprocessing.Array): + from lerobot.common.robot_devices.motors.dynamixel import DynamixelMotorsBus + leader_arm = DynamixelMotorsBus( + port=args.leader_arm_dev, + motors={ + # name: (index, model) + "shoulder_pan": (1, "xl330-m077"), + "shoulder_lift": (2, "xl330-m077"), + "elbow_flex": (3, "xl330-m077"), + "wrist_flex": (4, "xl330-m077"), + "wrist_roll": (5, "xl330-m077"), + "gripper": (6, "xl330-m077"), + }, + ) + + if not leader_arm.is_connected: + leader_arm.connect() + + counts_to_radians = np.pi * 2. / 4096. + tare_positions = [2048, 2048, 2048, 3072, 2048, 2048] + axis_direction = [-1, -1, -1, 1, -1, -1] + while True: + positions = leader_arm.read("Present_Position") + assert len(joint_commands) == len(positions) + for i in range(len(joint_commands)): + joint_commands[i] = axis_direction[i] * \ + (positions[i] - tare_positions[i]) * counts_to_radians + + joint_commands = multiprocessing.Array('d', [0, 0, 0, 0, 0, 0]) + run_robot_process = multiprocessing.Process( + target=update_controls, args=(joint_commands,)) + + # Separate thread to copy arm data to teleop_command (this shares compute time with simulation). + from threading import Thread + + def copy_data(joint_commands: multiprocessing.Array, is_stopping: bool): + while not is_stopping: + while episode_state.teleoperation_action is None: + print(f"Waiting for teleop to start...") + time.sleep(1.0) + assert len(joint_commands) == len( + episode_state.teleoperation_action) + for i in range(len(joint_commands)): + episode_state.teleoperation_action[i] = joint_commands[i] + + time.sleep(0.01) + + copy_data_thread = Thread(target=copy_data, args=( + joint_commands, episode_state.is_stopping)) + + def start_fn(): + listener.start() + run_robot_process.start() + copy_data_thread.start() + + def stop_fn(): + listener.stop() + run_robot_process.join() + copy_data_thread.join() + del robot + + return [start_fn, stop_fn] + + +def set_up_teleop(teleop_method: str): + start_fn = None + stop_fn = None + + if teleop_method == "keyboard": + (start_fn, stop_fn) = set_up_keyboard_teleop() + elif teleop_method == "arm": + (start_fn, stop_fn) = set_up_arm_teleop() + else: + raise Exception( + "A teleoperation method must be selected (Currently only 'keyboard' teleop is suppored)!") + + if not isinstance(start_fn, types.FunctionType): + raise ValueError( + "start_fn is not callable but a teleoperation system was initialized.") + + if not isinstance(stop_fn, types.FunctionType): + raise ValueError( + "stop_fn is not callable but a teleoperation system was initialized.") + + return [start_fn, stop_fn] + + +########################################################################################## +# Running the task +env_kwargs = None + +def construct_and_set_up_env(): + lerobot.common.utils.utils.set_global_seed(args.random_seed) + # Create the gym environment - check the kwargs in gym_real_world/gym_environment.py + env = gym.make(args.env_name, + disable_env_checker=True, + **env_kwargs + ) + + return env + + +def get_next_action_in_episode(replay_helper: ReplayHelper | None): + if replay_helper is not None: + action = replay_helper.get_action_at_frame() + state_observation = replay_helper.get_state_observation_at_next_frame() + if replay_helper.is_at_last_frame_in_dataset: + episode_state.is_concluding_episode = True + episode_state.is_stopping = True + elif replay_helper.is_at_last_frame_in_episode: + episode_state.is_concluding_episode = True + replay_helper.increment_frame_index() + else: + replay_helper.increment_frame_index() + + return [action, state_observation] + else: + # Wait for first command to arrive. + while np.isnan(np.linalg.norm(episode_state.teleoperation_action)): + print(f"Waiting for first teleoperation command to arrive...") + time.sleep(1) + + return [episode_state.teleoperation_action, None] + + +def set_up_next_episode(env): + # Reset the environment + observation, info = env.reset() + output_helper.maybe_set_up_data_keys(observation) + + # Set up correctly-sized action. + episode_state.teleoperation_action = env.action_space.sample() * np.nan + + +def run_sim_while_recording_dataset(*, replay_helper: ReplayHelper | None = None) -> LeRobotDataset: + output_helper.increment_dataset_counter() + print(f"gym environment created") + + episode_counter = 0 + env = construct_and_set_up_env() + + episode_state.is_stopping = False + while not episode_state.is_stopping: + set_up_next_episode(env) + + print(f"Starting episode #{episode_counter}") + + # init buffers + observations = {k: [] for k in env.observation_space} + actions = [] + timestamps = [] + + episode_state.is_dropping_episode = False + episode_state.is_concluding_episode = False + step_counter = 0 + while not episode_state.is_concluding_episode: + (action, expected_state_observation) = get_next_action_in_episode( + replay_helper) + + # Apply the next action (provided by teleop) + observation, reward, terminted, truncated, info = env.step( + action=action) + t = info["timestamp"] + print("@t={}".format(t)) + + if expected_state_observation is not None: + state_observation = torch.tensor(np.concatenate( + output_helper.vectorize_state_observations(observation), axis=0)) + if not torch.norm(expected_state_observation - state_observation) < 1e-6: + print("action=\n{}".format(action)) + print("diff =\n{}".format( + expected_state_observation - state_observation)) + print("norm(diff) =\n{}".format(torch.norm( + expected_state_observation - state_observation))) + # print("expected_state_observation ({})=\n{}".format( + # type(expected_state_observation), expected_state_observation)) + # print("state_observation ({})=\n{}".format( + # type(state_observation), state_observation)) + # raise AssertionError( + # "State observations should be equal between source dataset and replay.") + + # Render the simultion + env.render() + + # store data + for key in observation: + observations[key].append(copy.deepcopy(observation[key])) + actions.append(copy.deepcopy(action)) + timestamps.append(t) + + step_counter += 1 + + if episode_state.is_dropping_episode: + continue + + print(f"saving episode {episode_counter}...") + + output_helper.save_episode_data(num_steps=step_counter, + episode_index=episode_counter, + observations=observations, + actions=actions, + timestamps=timestamps) + + episode_counter += 1 + + lerobot_dataset = output_helper.save_data_and_maybe_push_to_hub() + + return lerobot_dataset + + +def teleop_robot_and_record_data() -> LeRobotDataset: + (start_teleoperation_fn, stop_teleoperation_fn) = set_up_teleop(args.teleop_method) + + # start teleoperation listener + start_teleoperation_fn() + + new_dataset = run_sim_while_recording_dataset() + + # stop teleoperation listener + stop_teleoperation_fn() + + return new_dataset + + +def replay_dataset_actions_in_sim(*, source_dataset: LeRobotDataset | None = None) -> LeRobotDataset: + # use latest recording for replay, if missing. + if source_dataset is None: + source_dataset = output_helper.output_data_path + + new_dataset = run_sim_while_recording_dataset( + replay_helper=ReplayHelper(lerobot_dataset=source_dataset), + ) + return new_dataset + + +def import_module_containing_gym(module_name: str): + # import the gym module containing the environment + try: + # because we want to import using a variable, do it this way + module_obj = __import__(module_name) + # create a global object containging our module + globals()[module_name] = module_obj + return module_obj + except ImportError: + sys.stderr.write("ERROR: missing python module: " + + module_name + "\n") + sys.exit(1) + + # Check that module contains necessary attributes: + print(f"Imported python module: '{module_name}'") + assert hasattr( + module_obj, 'ASSETS_PATH'), "Module must have the 'ASSETS_PATH' attribute!" + + +def process_args(): + # parse the repo_id name via command line + parser = argparse.ArgumentParser() + parser.add_argument("--random-seed", type=int, default="0") + + parser.add_argument("--env-name", type=str, default="PickPlaceCube-v0") + parser.add_argument("--teleop-method", type=str, default="keyboard") + parser.add_argument("--num-workers", type=int, default=1) + + # Arguments for pushing to HF Dataset Hub + parser.add_argument("--repo-id", type=str, default="myrepo") + parser.add_argument("--push-to-hub", action="store_true") + parser.add_argument("--leader-arm-dev", type=str, default="/dev/ttyACM1") + parser.add_argument("--gym-config-yaml", type=str, default="lerobot/configs/env/koch_drake_leader_arm_teleop.yaml") + parser.add_argument("--gym-reconfig-yamls", type=str, default="lerobot/configs/env/koch_drake_leader_arm_teleop_replay_1.yaml;lerobot/configs/env/koch_drake_leader_arm_teleop_replay_2.yaml") + + parser.add_argument( + "--revision", type=str, default=CODEBASE_VERSION, help="Codebase version used to generate the dataset." + ) + + return parser.parse_args() + + +if __name__ == "__main__": + import yaml + from pprint import pprint + args = process_args() + logging.basicConfig(level=logging.CRITICAL) + + output_helper = OutputHelper(repo_id=args.repo_id) + episode_state = EpisodeState() + + module_name = "gym_drake_lca" + module_obj = import_module_containing_gym(module_name) + + print("Recording initial dataset w/ teleop") + with open(args.gym_config_yaml, 'r') as f: + env_kwargs = yaml.safe_load(f) + pprint(env_kwargs) + lerobot_dataset = teleop_robot_and_record_data() + + is_first_replay = True + for reconfig_yaml in args.gym_reconfig_yamls.split(sep=';'): + print("Replaying from previous dataset") + with open(reconfig_yaml, 'r') as f: + env_kwargs = yaml.safe_load(f) + pprint(env_kwargs) + + if is_first_replay: + print(" -- from disk (previous run)") + replay_dataset_actions_in_sim() + is_first_replay = False + else: + # Automatically re-record datasets with different models for the cube. + print(" -- from dataset in memory") + replay_dataset_actions_in_sim(source_dataset=lerobot_dataset) diff --git a/poetry.lock b/poetry.lock index 43089048d..95fce46cc 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand. +# This 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