lerobot/lerobot/scripts/control_robot.py

"""
Utilities to control a robot.

Useful to record a dataset, replay a recorded episode, run the policy on your robot
and record an evaluation dataset, and to recalibrate your robot if needed.

Examples of usage:

- Recalibrate your robot:
```bash
python lerobot/scripts/control_robot.py calibrate
```

- Unlimited teleoperation at highest frequency (~200 Hz is expected), to exit with CTRL+C:
```bash
python lerobot/scripts/control_robot.py teleoperate

# Remove the cameras from the robot definition. They are not used in 'teleoperate' anyway.
python lerobot/scripts/control_robot.py teleoperate --robot-overrides '~cameras'
```

- Unlimited teleoperation at a limited frequency of 30 Hz, to simulate data recording frequency:
```bash
python lerobot/scripts/control_robot.py teleoperate \
    --fps 30
```

- Record one episode in order to test replay:
```bash
python lerobot/scripts/control_robot.py record \
    --fps 30 \
    --repo-id $USER/koch_test \
    --num-episodes 1 \
    --run-compute-stats 0
```

- Visualize dataset:
```bash
python lerobot/scripts/visualize_dataset.py \
    --repo-id $USER/koch_test \
    --episode-index 0
```

- Replay this test episode:
```bash
python lerobot/scripts/control_robot.py replay \
    --fps 30 \
    --repo-id $USER/koch_test \
    --episode 0
```

- Record a full dataset in order to train a policy, with 2 seconds of warmup,
30 seconds of recording for each episode, and 10 seconds to reset the environment in between episodes:
```bash
python lerobot/scripts/control_robot.py record \
    --fps 30 \
    --repo-id $USER/koch_pick_place_lego \
    --num-episodes 50 \
    --warmup-time-s 2 \
    --episode-time-s 30 \
    --reset-time-s 10
```

**NOTE**: You can use your keyboard to control data recording flow.
- Tap right arrow key '->' to early exit while recording an episode and go to resseting the environment.
- Tap right arrow key '->' to early exit while resetting the environment and got to recording the next episode.
- Tap left arrow key '<-' to early exit and re-record the current episode.
- Tap escape key 'esc' to stop the data recording.
This might require a sudo permission to allow your terminal to monitor keyboard events.

**NOTE**: You can resume/continue data recording by running the same data recording command and adding `--resume 1`.
If the dataset you want to extend is not on the hub, you also need to add `--local-files-only 1`.

- Train on this dataset with the ACT policy:
```bash
python lerobot/scripts/train.py \
    policy=act_koch_real \
    env=koch_real \
    dataset_repo_id=$USER/koch_pick_place_lego \
    hydra.run.dir=outputs/train/act_koch_real
```

- Run the pretrained policy on the robot:
```bash
python lerobot/scripts/control_robot.py record \
    --fps 30 \
    --repo-id $USER/eval_act_koch_real \
    --num-episodes 10 \
    --warmup-time-s 2 \
    --episode-time-s 30 \
    --reset-time-s 10
    -p outputs/train/act_koch_real/checkpoints/080000/pretrained_model
```
"""

import argparse
import logging
import time
from pathlib import Path
from typing import List

# from safetensors.torch import load_file, save_file
from lerobot.common.datasets.lerobot_dataset import LeRobotDataset
from lerobot.common.robot_devices.control_utils import (
    control_loop,
    has_method,
    init_keyboard_listener,
    init_policy,
    log_control_info,
    record_episode,
    reset_environment,
    sanity_check_dataset_name,
    sanity_check_dataset_robot_compatibility,
    stop_recording,
    warmup_record,
)
from lerobot.common.robot_devices.robots.factory import make_robot
from lerobot.common.robot_devices.robots.utils import Robot
from lerobot.common.robot_devices.utils import busy_wait, safe_disconnect
from lerobot.common.utils.utils import init_hydra_config, init_logging, log_say, none_or_int

########################################################################################
# Control modes
########################################################################################


@safe_disconnect
def calibrate(robot: Robot, arms: list[str] | None):
    # TODO(aliberts): move this code in robots' classes
    if robot.robot_type.startswith("stretch"):
        if not robot.is_connected:
            robot.connect()
        if not robot.is_homed():
            robot.home()
        return

    if arms is None:
        arms = robot.available_arms

    unknown_arms = [arm_id for arm_id in arms if arm_id not in robot.available_arms]
    available_arms_str = " ".join(robot.available_arms)
    unknown_arms_str = " ".join(unknown_arms)

    if arms is None or len(arms) == 0:
        raise ValueError(
            "No arm provided. Use `--arms` as argument with one or more available arms.\n"
            f"For instance, to recalibrate all arms add: `--arms {available_arms_str}`"
        )

    if len(unknown_arms) > 0:
        raise ValueError(
            f"Unknown arms provided ('{unknown_arms_str}'). Available arms are `{available_arms_str}`."
        )

    for arm_id in arms:
        arm_calib_path = robot.calibration_dir / f"{arm_id}.json"
        if arm_calib_path.exists():
            print(f"Removing '{arm_calib_path}'")
            arm_calib_path.unlink()
        else:
            print(f"Calibration file not found '{arm_calib_path}'")

    if robot.is_connected:
        robot.disconnect()

    # Calling `connect` automatically runs calibration
    # when the calibration file is missing
    robot.connect()
    robot.disconnect()
    print("Calibration is done! You can now teleoperate and record datasets!")


@safe_disconnect
def teleoperate(
    robot: Robot, fps: int | None = None, teleop_time_s: float | None = None, display_cameras: bool = False
):
    control_loop(
        robot,
        control_time_s=teleop_time_s,
        fps=fps,
        teleoperate=True,
        display_cameras=display_cameras,
    )


@safe_disconnect
def record(
    robot: Robot,
    root: Path,
    repo_id: str,
    single_task: str,
    pretrained_policy_name_or_path: str | None = None,
    policy_overrides: List[str] | None = None,
    assign_rewards: bool = False,
    fps: int | None = None,
    warmup_time_s: int | float = 2,
    episode_time_s: int | float = 10,
    reset_time_s: int | float = 5,
    num_episodes: int = 50,
    video: bool = True,
    run_compute_stats: bool = True,
    push_to_hub: bool = True,
    tags: list[str] | None = None,
    num_image_writer_processes: int = 0,
    num_image_writer_threads_per_camera: int = 4,
    display_cameras: bool = True,
    play_sounds: bool = True,
    resume: bool = False,
    # TODO(rcadene, aliberts): remove local_files_only when refactor with dataset as argument
    local_files_only: bool = False,
) -> LeRobotDataset:
    # TODO(rcadene): Add option to record logs
    listener = None
    events = None
    policy = None
    device = None
    use_amp = None
    extra_features = (
        {"next.reward": {"dtype": "int64", "shape": (1,), "names": None}} if assign_rewards else None
    )

    if single_task:
        task = single_task
    else:
        raise NotImplementedError("Only single-task recording is supported for now")

    # Load pretrained policy
    if pretrained_policy_name_or_path is not None:
        policy, policy_fps, device, use_amp = init_policy(pretrained_policy_name_or_path, policy_overrides)

        if fps is None:
            fps = policy_fps
            logging.warning(f"No fps provided, so using the fps from policy config ({policy_fps}).")
        elif fps != policy_fps:
            logging.warning(
                f"There is a mismatch between the provided fps ({fps}) and the one from policy config ({policy_fps})."
            )

    if resume:
        dataset = LeRobotDataset(
            repo_id,
            root=root,
            local_files_only=local_files_only,
        )
        dataset.start_image_writer(
            num_processes=num_image_writer_processes,
            num_threads=num_image_writer_threads_per_camera * len(robot.cameras),
        )
        sanity_check_dataset_robot_compatibility(dataset, robot, fps, video)
    else:
        # Create empty dataset or load existing saved episodes
        sanity_check_dataset_name(repo_id, policy)
        dataset = LeRobotDataset.create(
            repo_id,
            fps,
            root=root,
            robot=robot,
            use_videos=video,
            image_writer_processes=num_image_writer_processes,
            image_writer_threads=num_image_writer_threads_per_camera * len(robot.cameras),
            features=extra_features,
        )

    if not robot.is_connected:
        robot.connect()
    listener, events = init_keyboard_listener(assign_rewards=assign_rewards)

    # Execute a few seconds without recording to:
    # 1. teleoperate the robot to move it in starting position if no policy provided,
    # 2. give times to the robot devices to connect and start synchronizing,
    # 3. place the cameras windows on screen
    enable_teleoperation = policy is None
    log_say("Warmup record", play_sounds)
    warmup_record(robot, events, enable_teleoperation, warmup_time_s, display_cameras, fps)

    if has_method(robot, "teleop_safety_stop"):
        robot.teleop_safety_stop()

    recorded_episodes = 0
    while True:
        if recorded_episodes >= num_episodes:
            break

        # TODO(aliberts): add task prompt for multitask here. Might need to temporarily disable event if
        # input() messes with them.
        # if multi_task:
        #     task = input("Enter your task description: ")

        log_say(f"Recording episode {dataset.num_episodes}", play_sounds)
        record_episode(
            dataset=dataset,
            robot=robot,
            events=events,
            episode_time_s=episode_time_s,
            display_cameras=display_cameras,
            policy=policy,
            device=device,
            use_amp=use_amp,
            fps=fps,
        )

        # Execute a few seconds without recording to give time to manually reset the environment
        # Current code logic doesn't allow to teleoperate during this time.
        # TODO(rcadene): add an option to enable teleoperation during reset
        # Skip reset for the last episode to be recorded
        if not events["stop_recording"] and (
            (recorded_episodes < num_episodes - 1) or events["rerecord_episode"]
        ):
            log_say("Reset the environment", play_sounds)
            reset_environment(robot, events, reset_time_s)

        if events["rerecord_episode"]:
            log_say("Re-record episode", play_sounds)
            events["rerecord_episode"] = False
            events["exit_early"] = False
            dataset.clear_episode_buffer()
            continue

        dataset.save_episode(task)
        recorded_episodes += 1

        if events["stop_recording"]:
            break

    log_say("Stop recording", play_sounds, blocking=True)
    stop_recording(robot, listener, display_cameras)

    if run_compute_stats:
        logging.info("Computing dataset statistics")

    dataset.consolidate(run_compute_stats)

    if push_to_hub:
        dataset.push_to_hub(tags=tags)

    log_say("Exiting", play_sounds)
    return dataset


@safe_disconnect
def replay(
    robot: Robot,
    root: Path,
    repo_id: str,
    episode: int,
    fps: int | None = None,
    play_sounds: bool = True,
    local_files_only: bool = False,
):
    # TODO(rcadene, aliberts): refactor with control_loop, once `dataset` is an instance of LeRobotDataset
    # TODO(rcadene): Add option to record logs

    dataset = LeRobotDataset(repo_id, root=root, episodes=[episode], local_files_only=local_files_only)
    actions = dataset.hf_dataset.select_columns("action")

    if not robot.is_connected:
        robot.connect()

    log_say("Replaying episode", play_sounds, blocking=True)
    for idx in range(dataset.num_frames):
        start_episode_t = time.perf_counter()

        action = actions[idx]["action"]
        robot.send_action(action)

        dt_s = time.perf_counter() - start_episode_t
        busy_wait(1 / fps - dt_s)

        dt_s = time.perf_counter() - start_episode_t
        log_control_info(robot, dt_s, fps=fps)


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    subparsers = parser.add_subparsers(dest="mode", required=True)

    # Set common options for all the subparsers
    base_parser = argparse.ArgumentParser(add_help=False)
    base_parser.add_argument(
        "--robot-path",
        type=str,
        default="lerobot/configs/robot/koch.yaml",
        help="Path to robot yaml file used to instantiate the robot using `make_robot` factory function.",
    )
    base_parser.add_argument(
        "--robot-overrides",
        type=str,
        nargs="*",
        help="Any key=value arguments to override config values (use dots for.nested=overrides)",
    )

    parser_calib = subparsers.add_parser("calibrate", parents=[base_parser])
    parser_calib.add_argument(
        "--arms",
        type=str,
        nargs="*",
        help="List of arms to calibrate (e.g. `--arms left_follower right_follower left_leader`)",
    )

    parser_teleop = subparsers.add_parser("teleoperate", parents=[base_parser])
    parser_teleop.add_argument(
        "--fps", type=none_or_int, default=None, help="Frames per second (set to None to disable)"
    )
    parser_teleop.add_argument(
        "--display-cameras",
        type=int,
        default=1,
        help="Display all cameras on screen (set to 1 to display or 0).",
    )

    parser_record = subparsers.add_parser("record", parents=[base_parser])
    task_args = parser_record.add_mutually_exclusive_group(required=True)
    parser_record.add_argument(
        "--fps", type=none_or_int, default=None, help="Frames per second (set to None to disable)"
    )
    task_args.add_argument(
        "--single-task",
        type=str,
        help="A short but accurate description of the task performed during the recording.",
    )
    # TODO(aliberts): add multi-task support
    # task_args.add_argument(
    #     "--multi-task",
    #     type=int,
    #     help="You will need to enter the task performed at the start of each episode.",
    # )
    parser_record.add_argument(
        "--root",
        type=Path,
        default=None,
        help="Root directory where the dataset will be stored (e.g. 'dataset/path').",
    )
    parser_record.add_argument(
        "--repo-id",
        type=str,
        default="lerobot/test",
        help="Dataset identifier. By convention it should match '{hf_username}/{dataset_name}' (e.g. `lerobot/test`).",
    )
    parser_record.add_argument(
        "--local-files-only",
        type=int,
        default=0,
        help="Use local files only. By default, this script will try to fetch the dataset from the hub if it exists.",
    )
    parser_record.add_argument(
        "--warmup-time-s",
        type=int,
        default=10,
        help="Number of seconds before starting data collection. It allows the robot devices to warmup and synchronize.",
    )
    parser_record.add_argument(
        "--episode-time-s",
        type=int,
        default=60,
        help="Number of seconds for data recording for each episode.",
    )
    parser_record.add_argument(
        "--reset-time-s",
        type=int,
        default=60,
        help="Number of seconds for resetting the environment after each episode.",
    )
    parser_record.add_argument("--num-episodes", type=int, default=50, help="Number of episodes to record.")
    parser_record.add_argument(
        "--run-compute-stats",
        type=int,
        default=1,
        help="By default, run the computation of the data statistics at the end of data collection. Compute intensive and not required to just replay an episode.",
    )
    parser_record.add_argument(
        "--push-to-hub",
        type=int,
        default=1,
        help="Upload dataset to Hugging Face hub.",
    )
    # parser_record.add_argument(
    #     "--tags",
    #     type=str,
    #     nargs="*",
    #     help="Add tags to your dataset on the hub.",
    # )
    parser_record.add_argument(
        "--num-image-writer-processes",
        type=int,
        default=0,
        help=(
            "Number of subprocesses handling the saving of frames as PNGs. Set to 0 to use threads only; "
            "set to ≥1 to use subprocesses, each using threads to write images. The best number of processes "
            "and threads depends on your system. We recommend 4 threads per camera with 0 processes. "
            "If fps is unstable, adjust the thread count. If still unstable, try using 1 or more subprocesses."
        ),
    )
    parser_record.add_argument(
        "--num-image-writer-threads-per-camera",
        type=int,
        default=4,
        help=(
            "Number of threads writing the frames as png images on disk, per camera. "
            "Too many threads might cause unstable teleoperation fps due to main thread being blocked. "
            "Not enough threads might cause low camera fps."
        ),
    )
    parser_record.add_argument(
        "--resume",
        type=int,
        default=0,
        help="Resume recording on an existing dataset.",
    )
    parser_record.add_argument(
        "-p",
        "--pretrained-policy-name-or-path",
        type=str,
        help=(
            "Either the repo ID of a model hosted on the Hub or a path to a directory containing weights "
            "saved using `Policy.save_pretrained`."
        ),
    )
    parser_record.add_argument(
        "--policy-overrides",
        type=str,
        nargs="*",
        help="Any key=value arguments to override config values (use dots for.nested=overrides)",
    )
    parser_record.add_argument(
        "--assign-rewards",
        type=int,
        default=0,
        help="Enables the assignation of rewards to frames (by default no assignation). When enabled, assign a 0 reward to frames until the space bar is pressed which assign a 1 reward. Press the space bar a second time to assign a 0 reward. The reward assigned is reset to 0 when the episode ends.",
    )

    parser_replay = subparsers.add_parser("replay", parents=[base_parser])
    parser_replay.add_argument(
        "--fps", type=none_or_int, default=None, help="Frames per second (set to None to disable)"
    )
    parser_replay.add_argument(
        "--root",
        type=Path,
        default=None,
        help="Root directory where the dataset will be stored (e.g. 'dataset/path').",
    )
    parser_replay.add_argument(
        "--repo-id",
        type=str,
        default="lerobot/test",
        help="Dataset identifier. By convention it should match '{hf_username}/{dataset_name}' (e.g. `lerobot/test`).",
    )
    parser_replay.add_argument(
        "--local-files-only",
        type=int,
        default=0,
        help="Use local files only. By default, this script will try to fetch the dataset from the hub if it exists.",
    )
    parser_replay.add_argument("--episode", type=int, default=0, help="Index of the episode to replay.")

    args = parser.parse_args()

    init_logging()

    control_mode = args.mode
    robot_path = args.robot_path
    robot_overrides = args.robot_overrides
    kwargs = vars(args)
    del kwargs["mode"]
    del kwargs["robot_path"]
    del kwargs["robot_overrides"]

    robot_cfg = init_hydra_config(robot_path, robot_overrides)
    robot = make_robot(robot_cfg)

    if control_mode == "calibrate":
        calibrate(robot, **kwargs)

    elif control_mode == "teleoperate":
        teleoperate(robot, **kwargs)

    elif control_mode == "record":
        record(robot, **kwargs)

    elif control_mode == "replay":
        replay(robot, **kwargs)

    if robot.is_connected:
        # Disconnect manually to avoid a "Core dump" during process
        # termination due to camera threads not properly exiting.
        robot.disconnect()