diff --git a/examples/12_use_roarm_m3.md b/examples/12_use_roarm_m3.md deleted file mode 100644 index eabeaf8a..00000000 --- a/examples/12_use_roarm_m3.md +++ /dev/null @@ -1,140 +0,0 @@ -# Using the [roarm_m3](https://github.com/waveshareteam/roarm_m3) with LeRobot - -## Table of Contents - - - [A. Teleoperate](#a-teleoperate) - - [B. Record a dataset](#b-record-a-dataset) - - [C. Visualize a dataset](#c-visualize-a-dataset) - - [D. Replay an episode](#d-replay-an-episode) - - [E. Train a policy](#e-train-a-policy) - - [F. Evaluate your policy](#f-evaluate-your-policy) - - [G. More Information](#g-more-information) - -## A. Teleoperate - -**Simple teleop** -#### a. Teleop without displaying cameras -You will be able to teleoperate your robot! (it won't connect and display the cameras): -```bash -python lerobot/scripts/control_robot.py \ - --robot.type=roarm_m3 \ - --robot.cameras='{}' \ - --control.type=teleoperate -``` - - -#### b. Teleop with displaying cameras -You will be able to display the cameras while you are teleoperating by running the following code. This is useful to prepare your setup before recording your first dataset. -```bash -python lerobot/scripts/control_robot.py \ - --robot.type=roarm_m3 \ - --control.type=teleoperate -``` - -## B. Record a dataset - -Once you're familiar with teleoperation, you can record your first dataset with roarm_m3. - -If you want to use the Hugging Face hub features for uploading your dataset and you haven't previously done it, make sure you've logged in using a write-access token, which can be generated from the [Hugging Face settings](https://huggingface.co/settings/tokens): -```bash -huggingface-cli login --token ${HUGGINGFACE_TOKEN} --add-to-git-credential -``` - -Store your Hugging Face repository name in a variable to run these commands: -```bash -HF_USER=$(huggingface-cli whoami | head -n 1) -echo $HF_USER -``` - -Record 2 episodes and upload your dataset to the hub: -```bash -python lerobot/scripts/control_robot.py \ - --robot.type=roarm_m3 \ - --control.type=record \ - --control.fps=30 \ - --control.single_task="Grasp a block and put it in the bin." \ - --control.repo_id=${HF_USER}/roarm_m3_test \ - --control.tags='["roarm_m3","tutorial"]' \ - --control.warmup_time_s=5 \ - --control.episode_time_s=30 \ - --control.reset_time_s=30 \ - --control.num_episodes=2 \ - --control.push_to_hub=true -``` - -Note: You can resume recording by adding `--control.resume=true`. - -## C. Visualize a dataset - -If you uploaded your dataset to the hub with `--control.push_to_hub=true`, you can [visualize your dataset online](https://huggingface.co/spaces/lerobot/visualize_dataset) by copy pasting your repo id given by: -```bash -echo ${HF_USER}/roarm_m3_test -``` - -If you didn't upload with `--control.push_to_hub=false`, you can also visualize it locally with (a window can be opened in the browser `http://ip:9090` with the visualization tool): -```bash -python lerobot/scripts/visualize_dataset_html.py \ - --repo-id ${HF_USER}/roarm_m3_test \ - --host ip \ - --local-files-only 1 -``` - -## D. Replay an episode - -Now try to replay episode nth on your bot: -```bash -python lerobot/scripts/control_robot.py \ - --robot.type=roarm_m3 \ - --control.type=replay \ - --control.fps=30 \ - --control.repo_id=${HF_USER}/roarm_m3_test \ - --control.episode=n-1 -``` - -## E. Train a policy - -To train a policy to control your robot, use the [`python lerobot/scripts/train.py`](../lerobot/scripts/train.py) script. A few arguments are required. Here is an example command: -```bash -python lerobot/scripts/train.py \ - --dataset.repo_id=${HF_USER}/roarm_m3_test \ - --policy.type=act \ - --output_dir=outputs/train/act_roarm_m3_test \ - --job_name=act_roarm_m3_test \ - --device=cuda \ - --wandb.enable=true -``` - -Let's explain it: -1. We provided the dataset as argument with `--dataset.repo_id=${HF_USER}/roarm_m3_test`. -2. We provided the policy with `policy.type=act`. This loads configurations from [`configuration_act.py`](../lerobot/common/policies/act/configuration_act.py). Importantly, this policy will automatically adapt to the number of motor sates, motor actions and cameras of your robot (e.g. `laptop` and `phone`) which have been saved in your dataset. -4. We provided `device=cuda` since we are training on a Nvidia GPU, but you could use `device=mps` to train on Apple silicon. -5. We provided `wandb.enable=true` to use [Weights and Biases](https://docs.wandb.ai/quickstart) for visualizing training plots. This is optional but if you use it, make sure you are logged in by running `wandb login`. - -Training should take several hours. You will find checkpoints in `outputs/train/act_roarm_m3_test/checkpoints`. - -## F. Evaluate your policy - -You can use the `record` function from [`lerobot/scripts/control_robot.py`](../lerobot/scripts/control_robot.py) but with a policy checkpoint as input. For instance, run this command to record 10 evaluation episodes: -```bash -python lerobot/scripts/control_robot.py \ - --robot.type=roarm_m3 \ - --control.type=record \ - --control.fps=30 \ - --control.single_task="Grasp a block and put it in the bin." \ - --control.repo_id=${HF_USER}/eval_act_roarm_m3_test \ - --control.tags='["tutorial"]' \ - --control.warmup_time_s=5 \ - --control.episode_time_s=30 \ - --control.reset_time_s=30 \ - --control.num_episodes=10 \ - --control.push_to_hub=true \ - --control.policy.path=outputs/train/act_roarm_m3_test/checkpoints/last/pretrained_model -``` - -As you can see, it's almost the same command as previously used to record your training dataset. Two things changed: -1. There is an additional `--control.policy.path` argument which indicates the path to your policy checkpoint with (e.g. `outputs/train/eval_act_roarm_m3_test/checkpoints/last/pretrained_model`). You can also use the model repository if you uploaded a model checkpoint to the hub (e.g. `${HF_USER}/act_roarm_m3_test`). -2. The name of dataset begins by `eval` to reflect that you are running inference (e.g. `${HF_USER}/eval_act_roarm_m3_test`). - -## G. More Information - -Follow this [previous tutorial](https://github.com/huggingface/lerobot/blob/main/examples/7_get_started_with_real_robot.md#4-train-a-policy-on-your-data) for a more in-depth tutorial on controlling real robots with LeRobot. diff --git a/lerobot/common/datasets/lerobot_dataset.py b/lerobot/common/datasets/lerobot_dataset.py index 505c33b2..5414c76d 100644 --- a/lerobot/common/datasets/lerobot_dataset.py +++ b/lerobot/common/datasets/lerobot_dataset.py @@ -13,6 +13,7 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. +import contextlib import logging import shutil from pathlib import Path @@ -27,6 +28,7 @@ import torch.utils from datasets import concatenate_datasets, load_dataset from huggingface_hub import HfApi, snapshot_download from huggingface_hub.constants import REPOCARD_NAME +from huggingface_hub.errors import RevisionNotFoundError from lerobot.common.constants import HF_LEROBOT_HOME from lerobot.common.datasets.compute_stats import aggregate_stats, compute_episode_stats @@ -312,7 +314,7 @@ class LeRobotDatasetMetadata: obj.repo_id = repo_id obj.root = Path(root) if root is not None else HF_LEROBOT_HOME / repo_id - obj.root.mkdir(parents=True, exist_ok=True) + obj.root.mkdir(parents=True, exist_ok=False) if robot is not None: features = get_features_from_robot(robot, use_videos) @@ -478,7 +480,7 @@ class LeRobotDataset(torch.utils.data.Dataset): self.image_writer = None self.episode_buffer = None - self.root.mkdir(exist_ok=False, parents=True) + self.root.mkdir(exist_ok=True, parents=True) # Load metadata self.meta = LeRobotDatasetMetadata( @@ -517,6 +519,7 @@ class LeRobotDataset(torch.utils.data.Dataset): branch: str | None = None, tags: list | None = None, license: str | None = "apache-2.0", + tag_version: bool = True, push_videos: bool = True, private: bool = False, allow_patterns: list[str] | str | None = None, @@ -562,6 +565,11 @@ class LeRobotDataset(torch.utils.data.Dataset): ) card.push_to_hub(repo_id=self.repo_id, repo_type="dataset", revision=branch) + if tag_version: + with contextlib.suppress(RevisionNotFoundError): + hub_api.delete_tag(self.repo_id, tag=CODEBASE_VERSION, repo_type="dataset") + hub_api.create_tag(self.repo_id, tag=CODEBASE_VERSION, revision=branch, repo_type="dataset") + def pull_from_repo( self, allow_patterns: list[str] | str | None = None, diff --git a/lerobot/common/datasets/utils.py b/lerobot/common/datasets/utils.py index 89adb163..7e297b35 100644 --- a/lerobot/common/datasets/utils.py +++ b/lerobot/common/datasets/utils.py @@ -31,6 +31,7 @@ import packaging.version import torch from datasets.table import embed_table_storage from huggingface_hub import DatasetCard, DatasetCardData, HfApi +from huggingface_hub.errors import RevisionNotFoundError from PIL import Image as PILImage from torchvision import transforms @@ -325,6 +326,19 @@ def get_safe_version(repo_id: str, version: str | packaging.version.Version) -> ) hub_versions = get_repo_versions(repo_id) + if not hub_versions: + raise RevisionNotFoundError( + f"""Your dataset must be tagged with a codebase version. + Assuming _version_ is the codebase_version value in the info.json, you can run this: + ```python + from huggingface_hub import HfApi + + hub_api = HfApi() + hub_api.create_tag("{repo_id}", tag="_version_", repo_type="dataset") + ``` + """ + ) + if target_version in hub_versions: return f"v{target_version}" diff --git a/lerobot/common/datasets/v2/convert_dataset_v1_to_v2.py b/lerobot/common/datasets/v2/convert_dataset_v1_to_v2.py index fe658ce4..acf0282f 100644 --- a/lerobot/common/datasets/v2/convert_dataset_v1_to_v2.py +++ b/lerobot/common/datasets/v2/convert_dataset_v1_to_v2.py @@ -166,9 +166,6 @@ def parse_robot_config(robot_cfg: RobotConfig) -> tuple[str, dict]: for arm in robot_cfg.leader_arms for motor in robot_cfg.leader_arms[arm].motors ] - elif robot_cfg.type == "roarm_m3": - state_names = ["roam_m3","roam_m3","roam_m3","roam_m3","roam_m3","roam_m3"] - action_names = ["roam_m3","roam_m3","roam_m3","roam_m3","roam_m3","roam_m3"] # elif robot_cfg["robot_type"] == "stretch3": TODO else: raise NotImplementedError( diff --git a/lerobot/common/datasets/v21/convert_dataset_v20_to_v21.py b/lerobot/common/datasets/v21/convert_dataset_v20_to_v21.py index 20bda75b..163a6003 100644 --- a/lerobot/common/datasets/v21/convert_dataset_v20_to_v21.py +++ b/lerobot/common/datasets/v21/convert_dataset_v20_to_v21.py @@ -57,7 +57,7 @@ def convert_dataset( dataset.meta.info["codebase_version"] = CODEBASE_VERSION write_info(dataset.meta.info, dataset.root) - dataset.push_to_hub(branch=branch, allow_patterns="meta/") + dataset.push_to_hub(branch=branch, tag_version=False, allow_patterns="meta/") # delete old stats.json file if (dataset.root / STATS_PATH).is_file: diff --git a/lerobot/common/policies/pi0/modeling_pi0.py b/lerobot/common/policies/pi0/modeling_pi0.py index c8b12caf..bc53bf85 100644 --- a/lerobot/common/policies/pi0/modeling_pi0.py +++ b/lerobot/common/policies/pi0/modeling_pi0.py @@ -313,7 +313,7 @@ class PI0Policy(PreTrainedPolicy): state = self.prepare_state(batch) lang_tokens, lang_masks = self.prepare_language(batch) actions = self.prepare_action(batch) - actions_is_pad = batch.get("actions_id_pad") + actions_is_pad = batch.get("actions_is_pad") loss_dict = {} losses = self.model.forward(images, img_masks, lang_tokens, lang_masks, state, actions, noise, time) diff --git a/lerobot/common/robot_devices/cameras/opencv.py b/lerobot/common/robot_devices/cameras/opencv.py index 506f6075..93c791fa 100644 --- a/lerobot/common/robot_devices/cameras/opencv.py +++ b/lerobot/common/robot_devices/cameras/opencv.py @@ -30,20 +30,6 @@ from lerobot.common.utils.utils import capture_timestamp_utc # treat the same cameras as new devices. Thus we select a higher bound to search indices. MAX_OPENCV_INDEX = 60 -undistort = True -def undistort_image(image): - import cv2 - camera_matrix = np.array([ - [289.11451, 0., 347.23664], - [0., 289.75319, 235.67429], - [0., 0., 1.] - ]) - - dist_coeffs = np.array([-0.208848, 0.028006, -0.000705, -0.000820, 0.000000]) - - undistorted_image = cv2.undistort(image, camera_matrix, dist_coeffs) - - return undistorted_image def find_cameras(raise_when_empty=False, max_index_search_range=MAX_OPENCV_INDEX, mock=False) -> list[dict]: cameras = [] @@ -382,9 +368,6 @@ class OpenCVCamera: color_image = cv2.cvtColor(color_image, cv2.COLOR_BGR2RGB) - if undistort: - color_image = undistort_image(color_image) - h, w, _ = color_image.shape if h != self.height or w != self.width: raise OSError( diff --git a/lerobot/common/robot_devices/cameras/utils.py b/lerobot/common/robot_devices/cameras/utils.py index 88288ea3..ef6d8266 100644 --- a/lerobot/common/robot_devices/cameras/utils.py +++ b/lerobot/common/robot_devices/cameras/utils.py @@ -31,7 +31,7 @@ def make_cameras_from_configs(camera_configs: dict[str, CameraConfig]) -> list[C cameras[key] = IntelRealSenseCamera(cfg) else: - raise ValueError(f"The motor type '{cfg.type}' is not valid.") + raise ValueError(f"The camera type '{cfg.type}' is not valid.") return cameras diff --git a/lerobot/common/robot_devices/control_utils.py b/lerobot/common/robot_devices/control_utils.py index c5870e1f..d2361a64 100644 --- a/lerobot/common/robot_devices/control_utils.py +++ b/lerobot/common/robot_devices/control_utils.py @@ -113,73 +113,46 @@ def predict_action(observation, policy, device, use_amp): return action -# def init_keyboard_listener(): -# # Allow to exit early while recording an episode or resetting the environment, -# # by tapping the right arrow key '->'. This might require a sudo permission -# # to allow your terminal to monitor keyboard events. -# events = {} -# events["exit_early"] = False -# events["rerecord_episode"] = False -# events["stop_recording"] = False - -# if is_headless(): -# logging.warning( -# "Headless environment detected. On-screen cameras display and keyboard inputs will not be available." -# ) -# listener = None -# return listener, events - -# # Only import pynput if not in a headless environment -# from pynput import keyboard - -# def on_press(key): -# try: -# if key == keyboard.Key.right: -# print("Right arrow key pressed. Exiting loop...") -# events["exit_early"] = True -# elif key == keyboard.Key.left: -# print("Left arrow key pressed. Exiting loop and rerecord the last episode...") -# events["rerecord_episode"] = True -# events["exit_early"] = True -# elif key == keyboard.Key.esc: -# print("Escape key pressed. Stopping data recording...") -# events["stop_recording"] = True -# events["exit_early"] = True -# except Exception as e: -# print(f"Error handling key press: {e}") - -# listener = keyboard.Listener(on_press=on_press) -# listener.start() - -# return listener, events - def init_keyboard_listener(): + # Allow to exit early while recording an episode or resetting the environment, + # by tapping the right arrow key '->'. This might require a sudo permission + # to allow your terminal to monitor keyboard events. events = {} events["exit_early"] = False events["rerecord_episode"] = False events["stop_recording"] = False - from sshkeyboard import listen_keyboard - import threading + + if is_headless(): + logging.warning( + "Headless environment detected. On-screen cameras display and keyboard inputs will not be available." + ) + listener = None + return listener, events + + # Only import pynput if not in a headless environment + from pynput import keyboard + def on_press(key): try: - if key == "right": + if key == keyboard.Key.right: print("Right arrow key pressed. Exiting loop...") events["exit_early"] = True - elif key == "left": + elif key == keyboard.Key.left: print("Left arrow key pressed. Exiting loop and rerecord the last episode...") events["rerecord_episode"] = True events["exit_early"] = True - elif key == "q": - print("Q key pressed. Stopping data recording...") + elif key == keyboard.Key.esc: + print("Escape key pressed. Stopping data recording...") events["stop_recording"] = True events["exit_early"] = True except Exception as e: print(f"Error handling key press: {e}") - listener = threading.Thread(target=listen_keyboard, kwargs={"on_press": on_press}) + listener = keyboard.Listener(on_press=on_press) listener.start() - return listener,events + return listener, events + def warmup_record( robot, @@ -283,8 +256,7 @@ def control_loop( frame = {**observation, **action, "task": single_task} dataset.add_frame(frame) - if display_cameras: - # if display_cameras and not is_headless(): + if display_cameras and not is_headless(): image_keys = [key for key in observation if "image" in key] for key in image_keys: cv2.imshow(key, cv2.cvtColor(observation[key].numpy(), cv2.COLOR_RGB2BGR)) @@ -316,25 +288,17 @@ def reset_environment(robot, events, reset_time_s, fps): teleoperate=True, ) -# def stop_recording(robot, listener, display_cameras): -# robot.disconnect() - -# if not is_headless(): -# if listener is not None: -# listener.stop() - -# if display_cameras: -# cv2.destroyAllWindows() def stop_recording(robot, listener, display_cameras): robot.disconnect() - from sshkeyboard import stop_listening - if listener is not None: - stop_listening() + if not is_headless(): + if listener is not None: + listener.stop() + + if display_cameras: + cv2.destroyAllWindows() - if display_cameras: - cv2.destroyAllWindows() def sanity_check_dataset_name(repo_id, policy_cfg): _, dataset_name = repo_id.split("/") diff --git a/lerobot/common/robot_devices/robots/configs.py b/lerobot/common/robot_devices/robots/configs.py index ac38c7eb..88cb4e6f 100644 --- a/lerobot/common/robot_devices/robots/configs.py +++ b/lerobot/common/robot_devices/robots/configs.py @@ -479,45 +479,6 @@ class So100RobotConfig(ManipulatorRobotConfig): mock: bool = False -@RobotConfig.register_subclass("roarm_m3") -@dataclass -class RoarmRobotConfig(RobotConfig): - # `max_relative_target` limits the magnitude of the relative positional target vector for safety purposes. - # Set this to a positive scalar to have the same value for all motors, or a list that is the same length as - # the number of motors in your follower arms. - max_relative_target: int | None = None - - leader_arms: dict[str, str] = field( - default_factory=lambda: { - "main": "/dev/ttyUSB0", - } - ) - - # Follower arms configuration: left and right ports - follower_arms: dict[str, str] = field( - default_factory=lambda: { - "main": "/dev/ttyUSB1", - } - ) - - cameras: dict[str, CameraConfig] = field( - default_factory=lambda: { - "laptop": OpenCVCameraConfig( - camera_index=0, - fps=30, - width=640, - height=480, - ), - "phone": OpenCVCameraConfig( - camera_index=1, - fps=30, - width=640, - height=480, - ), - } - ) - - mock: bool = False @RobotConfig.register_subclass("stretch") @dataclass @@ -554,6 +515,7 @@ class StretchRobotConfig(RobotConfig): mock: bool = False + @RobotConfig.register_subclass("lekiwi") @dataclass class LeKiwiRobotConfig(RobotConfig): diff --git a/lerobot/common/robot_devices/robots/mobile_manipulator.py b/lerobot/common/robot_devices/robots/mobile_manipulator.py index b20c61f7..c2cad227 100644 --- a/lerobot/common/robot_devices/robots/mobile_manipulator.py +++ b/lerobot/common/robot_devices/robots/mobile_manipulator.py @@ -392,21 +392,19 @@ class MobileManipulator: for name in self.leader_arms: pos = self.leader_arms[name].read("Present_Position") pos_tensor = torch.from_numpy(pos).float() - # Instead of pos_tensor.item(), use tolist() to convert the entire tensor to a list arm_positions.extend(pos_tensor.tolist()) - # (The rest of your code for generating wheel commands remains unchanged) - x_cmd = 0.0 # m/s forward/backward - y_cmd = 0.0 # m/s lateral + y_cmd = 0.0 # m/s forward/backward + x_cmd = 0.0 # m/s lateral theta_cmd = 0.0 # deg/s rotation if self.pressed_keys["forward"]: - x_cmd += xy_speed - if self.pressed_keys["backward"]: - x_cmd -= xy_speed - if self.pressed_keys["left"]: y_cmd += xy_speed - if self.pressed_keys["right"]: + if self.pressed_keys["backward"]: y_cmd -= xy_speed + if self.pressed_keys["left"]: + x_cmd += xy_speed + if self.pressed_keys["right"]: + x_cmd -= xy_speed if self.pressed_keys["rotate_left"]: theta_cmd += theta_speed if self.pressed_keys["rotate_right"]: @@ -584,8 +582,8 @@ class MobileManipulator: # Create the body velocity vector [x, y, theta_rad]. velocity_vector = np.array([x_cmd, y_cmd, theta_rad]) - # Define the wheel mounting angles with a -90° offset. - angles = np.radians(np.array([240, 120, 0]) - 90) + # Define the wheel mounting angles (defined from y axis cw) + angles = np.radians(np.array([300, 180, 60])) # Build the kinematic matrix: each row maps body velocities to a wheel’s linear speed. # The third column (base_radius) accounts for the effect of rotation. m = np.array([[np.cos(a), np.sin(a), base_radius] for a in angles]) @@ -641,8 +639,8 @@ class MobileManipulator: # Compute each wheel’s linear speed (m/s) from its angular speed. wheel_linear_speeds = wheel_radps * wheel_radius - # Define the wheel mounting angles with a -90° offset. - angles = np.radians(np.array([240, 120, 0]) - 90) + # Define the wheel mounting angles (defined from y axis cw) + angles = np.radians(np.array([300, 180, 60])) m = np.array([[np.cos(a), np.sin(a), base_radius] for a in angles]) # Solve the inverse kinematics: body_velocity = M⁻¹ · wheel_linear_speeds. diff --git a/lerobot/common/robot_devices/robots/roarm_m3.py b/lerobot/common/robot_devices/robots/roarm_m3.py deleted file mode 100644 index 24461194..00000000 --- a/lerobot/common/robot_devices/robots/roarm_m3.py +++ /dev/null @@ -1,306 +0,0 @@ -"""Contains logic to instantiate a robot, read information from its motors and cameras, -and send orders to its motors. -""" -# TODO(rcadene, aliberts): reorganize the codebase into one file per robot, with the associated -# calibration procedure, to make it easy for people to add their own robot. - -import json -import logging -import time -import warnings -from pathlib import Path - -import numpy as np -import torch - -from lerobot.common.robot_devices.cameras.utils import make_cameras_from_configs -from lerobot.common.robot_devices.robots.configs import RoarmRobotConfig -from lerobot.common.robot_devices.utils import RobotDeviceAlreadyConnectedError, RobotDeviceNotConnectedError -from roarm_sdk.roarm import roarm - -def ensure_safe_goal_position( - goal_pos: torch.Tensor, present_pos: torch.Tensor, max_relative_target: float | list[float] -): - # Cap relative action target magnitude for safety. - diff = goal_pos - present_pos - max_relative_target = torch.tensor(max_relative_target) - safe_diff = torch.minimum(diff, max_relative_target) - safe_diff = torch.maximum(safe_diff, -max_relative_target) - safe_goal_pos = present_pos + safe_diff - - if not torch.allclose(goal_pos, safe_goal_pos): - logging.warning( - "Relative goal position magnitude had to be clamped to be safe.\n" - f" requested relative goal position target: {diff}\n" - f" clamped relative goal position target: {safe_diff}" - ) - - return safe_goal_pos - -def make_roarm_from_configs(configs: dict[str, str]) -> (dict[str, roarm]): - roarms = {} - - for key, port in configs.items(): - roarms[key] = roarm(roarm_type="roarm_m3", port=port, baudrate=115200) - - return roarms - -class RoarmRobot: - - def __init__( - self, - config: RoarmRobotConfig, - ): - self.config = config - self.robot_type = self.config.type - self.leader_arms = make_roarm_from_configs(self.config.leader_arms) - self.follower_arms = make_roarm_from_configs(self.config.follower_arms) - self.cameras = make_cameras_from_configs(self.config.cameras) - self.is_connected = False - self.logs = {} - - @property - def camera_features(self) -> dict: - cam_ft = {} - for cam_key, cam in self.cameras.items(): - key = f"observation.images.{cam_key}" - cam_ft[key] = { - "shape": (cam.height, cam.width, cam.channels), - "names": ["height", "width", "channels"], - "info": None, - } - return cam_ft - - @property - def motor_features(self) -> dict: - return { - "action": { - "dtype": "float32", - "shape": (6,), - "names": "roam_m3", - }, - "observation.state": { - "dtype": "float32", - "shape": (6,), - "names": "roam_m3", - }, - } - - @property - def features(self): - return {**self.motor_features, **self.camera_features} - - @property - def has_camera(self): - return len(self.cameras) > 0 - - @property - def num_cameras(self): - return len(self.cameras) - - def connect(self): - if self.is_connected: - raise RobotDeviceAlreadyConnectedError( - "RoarmRobot is already connected. Do not run `robot.connect()` twice." - ) - - if not self.leader_arms and not self.follower_arms and not self.cameras: - raise ValueError( - "RoarmRobot doesn't have any device to connect. See example of usage in docstring of the class." - ) - - for name in self.follower_arms: - self.follower_arms[name].joints_angle_get() - for name in self.leader_arms: - self.leader_arms[name].joints_angle_get() - - # Connect the cameras - for name in self.cameras: - self.cameras[name].connect() - - self.is_connected = True - - def teleop_step( - self, record_data=False - ) -> None | tuple[dict[str, torch.Tensor], dict[str, torch.Tensor]]: - if not self.is_connected: - raise RobotDeviceNotConnectedError( - "RoarmRobot is not connected. You need to run `robot.connect()`." - ) - - # Prepare to assign the position of the leader to the follower - leader_pos = {} - for name in self.leader_arms: - before_lread_t = time.perf_counter() - leader_pos[name] = np.array(self.leader_arms[name].joints_angle_get(), dtype=np.float32) - leader_pos[name] = torch.from_numpy(leader_pos[name]) - self.logs[f"read_leader_{name}_pos_dt_s"] = time.perf_counter() - before_lread_t - - # Send goal position to the follower - follower_goal_pos = {} - for name in self.follower_arms: - before_fwrite_t = time.perf_counter() - goal_pos = leader_pos[name] - - # Cap goal position when too far away from present position. - # Slower fps expected due to reading from the follower. - if self.config.max_relative_target is not None: - present_pos = np.array(self.follower_arms[name].joints_angle_get(), dtype=np.float32) - present_pos = torch.from_numpy(present_pos) - goal_pos = ensure_safe_goal_position(goal_pos, present_pos, self.config.max_relative_target) - - # Used when record_data=True - follower_goal_pos[name] = goal_pos - - goal_pos = goal_pos.numpy().astype(np.int32) - goal_pos = goal_pos.tolist() if isinstance(goal_pos, (np.ndarray, torch.Tensor)) else goal_pos - self.follower_arms[name].joints_angle_ctrl(angles=goal_pos, speed=0, acc=0) - self.logs[f"write_follower_{name}_goal_pos_dt_s"] = time.perf_counter() - before_fwrite_t - - # Early exit when recording data is not requested - if not record_data: - return - - # TODO(rcadene): Add velocity and other info - # Read follower position - follower_pos = {} - for name in self.follower_arms: - before_fread_t = time.perf_counter() - follower_pos[name] = np.array(self.follower_arms[name].joints_angle_get(), dtype=np.float32) - follower_pos[name] = torch.from_numpy(follower_pos[name]) - self.logs[f"read_follower_{name}_pos_dt_s"] = time.perf_counter() - before_fread_t - - # Create state by concatenating follower current position - state = [] - for name in self.follower_arms: - if name in follower_pos: - state.append(follower_pos[name]) - state = torch.cat(state) - - # Create action by concatenating follower goal position - action = [] - for name in self.follower_arms: - if name in follower_goal_pos: - action.append(follower_goal_pos[name]) - action = torch.cat(action) - - # Capture images from cameras - images = {} - for name in self.cameras: - before_camread_t = time.perf_counter() - images[name] = self.cameras[name].async_read() - images[name] = torch.from_numpy(images[name]) - self.logs[f"read_camera_{name}_dt_s"] = self.cameras[name].logs["delta_timestamp_s"] - self.logs[f"async_read_camera_{name}_dt_s"] = time.perf_counter() - before_camread_t - - # Populate output dictionaries - obs_dict, action_dict = {}, {} - obs_dict["observation.state"] = state - action_dict["action"] = action - for name in self.cameras: - obs_dict[f"observation.images.{name}"] = images[name] - - return obs_dict, action_dict - - def capture_observation(self): - """The returned observations do not have a batch dimension.""" - if not self.is_connected: - raise RobotDeviceNotConnectedError( - "RoarmRobot is not connected. You need to run `robot.connect()`." - ) - - # Read follower position - follower_pos = {} - for name in self.follower_arms: - before_fread_t = time.perf_counter() - follower_pos[name] = np.array(self.follower_arms[name].joints_angle_get(), dtype=np.float32) - follower_pos[name] = torch.from_numpy(follower_pos[name]) - self.logs[f"read_follower_{name}_pos_dt_s"] = time.perf_counter() - before_fread_t - - # Create state by concatenating follower current position - state = [] - for name in self.follower_arms: - if name in follower_pos: - state.append(follower_pos[name]) - state = torch.cat(state) - - # Capture images from cameras - images = {} - for name in self.cameras: - before_camread_t = time.perf_counter() - images[name] = self.cameras[name].async_read() - images[name] = torch.from_numpy(images[name]) - self.logs[f"read_camera_{name}_dt_s"] = self.cameras[name].logs["delta_timestamp_s"] - self.logs[f"async_read_camera_{name}_dt_s"] = time.perf_counter() - before_camread_t - - # Populate output dictionaries and format to pytorch - obs_dict = {} - obs_dict["observation.state"] = state - for name in self.cameras: - obs_dict[f"observation.images.{name}"] = images[name] - return obs_dict - - def send_action(self, action: torch.Tensor) -> torch.Tensor: - """Command the follower arms to move to a target joint configuration. - - The relative action magnitude may be clipped depending on the configuration parameter - `max_relative_target`. In this case, the action sent differs from original action. - Thus, this function always returns the action actually sent. - - Args: - action: tensor containing the concatenated goal positions for the follower arms. - """ - if not self.is_connected: - raise RobotDeviceNotConnectedError( - "RoarmRobot is not connected. You need to run `robot.connect()`." - ) - - from_idx = 0 - to_idx = 6 - action_sent = [] - for name in self.follower_arms: - # Get goal position of each follower arm by splitting the action vector - goal_pos = action[from_idx:to_idx] - from_idx = to_idx - - # Cap goal position when too far away from present position. - # Slower fps expected due to reading from the follower. - if self.config.max_relative_target is not None: - present_pos = np.array(self.follower_arms[name].joints_angle_get(), dtype=np.float32) - present_pos = torch.from_numpy(present_pos) - goal_pos = ensure_safe_goal_position(goal_pos, present_pos, self.config.max_relative_target) - - # Save tensor to concat and return - action_sent.append(goal_pos) - - # Send goal position to each follower - goal_pos = goal_pos.numpy().astype(np.int32) - goal_pos = goal_pos.tolist() if isinstance(goal_pos, (np.ndarray, torch.Tensor)) else goal_pos - self.follower_arms[name].joints_angle_ctrl(angles=goal_pos, speed=0, acc=0) - - return torch.cat(action_sent) - - def print_logs(self): - pass - # TODO(aliberts): move robot-specific logs logic here - - def disconnect(self): - if not self.is_connected: - raise RobotDeviceNotConnectedError( - "RoarmRobot is not connected. You need to run `robot.connect()` before disconnecting." - ) - - for name in self.follower_arms: - self.follower_arms[name].disconnect() - - for name in self.leader_arms: - self.leader_arms[name].disconnect() - - for name in self.cameras: - self.cameras[name].disconnect() - - self.is_connected = False - - def __del__(self): - if getattr(self, "is_connected", False): - self.disconnect() diff --git a/lerobot/common/robot_devices/robots/utils.py b/lerobot/common/robot_devices/robots/utils.py index d418ec34..47e2519b 100644 --- a/lerobot/common/robot_devices/robots/utils.py +++ b/lerobot/common/robot_devices/robots/utils.py @@ -9,7 +9,6 @@ from lerobot.common.robot_devices.robots.configs import ( MossRobotConfig, RobotConfig, So100RobotConfig, - RoarmRobotConfig, StretchRobotConfig, ) @@ -45,8 +44,6 @@ def make_robot_config(robot_type: str, **kwargs) -> RobotConfig: return MossRobotConfig(**kwargs) elif robot_type == "so100": return So100RobotConfig(**kwargs) - elif robot_type == "roarm_m3": - return RoarmRobotConfig(**kwargs) elif robot_type == "stretch": return StretchRobotConfig(**kwargs) elif robot_type == "lekiwi": @@ -64,15 +61,12 @@ def make_robot_from_config(config: RobotConfig): from lerobot.common.robot_devices.robots.mobile_manipulator import MobileManipulator return MobileManipulator(config) - elif isinstance(config, RoarmRobotConfig): - from lerobot.common.robot_devices.robots.roarm_m3 import RoarmRobot - - return RoarmRobot(config) else: from lerobot.common.robot_devices.robots.stretch import StretchRobot return StretchRobot(config) + def make_robot(robot_type: str, **kwargs) -> Robot: config = make_robot_config(robot_type, **kwargs) return make_robot_from_config(config) diff --git a/lerobot/common/utils/utils.py b/lerobot/common/utils/utils.py index d0c12b30..cd26f04b 100644 --- a/lerobot/common/utils/utils.py +++ b/lerobot/common/utils/utils.py @@ -17,6 +17,7 @@ import logging import os import os.path as osp import platform +import subprocess from copy import copy from datetime import datetime, timezone from pathlib import Path @@ -165,23 +166,31 @@ def capture_timestamp_utc(): def say(text, blocking=False): - # Check if mac, linux, or windows. - if platform.system() == "Darwin": - cmd = f'say "{text}"' - if not blocking: - cmd += " &" - elif platform.system() == "Linux": - cmd = f'spd-say "{text}"' - if blocking: - cmd += " --wait" - elif platform.system() == "Windows": - # TODO(rcadene): Make blocking option work for Windows - cmd = ( - 'PowerShell -Command "Add-Type -AssemblyName System.Speech; ' - f"(New-Object System.Speech.Synthesis.SpeechSynthesizer).Speak('{text}')\"" - ) + system = platform.system() - os.system(cmd) + if system == "Darwin": + cmd = ["say", text] + + elif system == "Linux": + cmd = ["spd-say", text] + if blocking: + cmd.append("--wait") + + elif system == "Windows": + cmd = [ + "PowerShell", + "-Command", + "Add-Type -AssemblyName System.Speech; " + f"(New-Object System.Speech.Synthesis.SpeechSynthesizer).Speak('{text}')", + ] + + else: + raise RuntimeError("Unsupported operating system for text-to-speech.") + + if blocking: + subprocess.run(cmd, check=True) + else: + subprocess.Popen(cmd, creationflags=subprocess.CREATE_NO_WINDOW if system == "Windows" else 0) def log_say(text, play_sounds, blocking=False): diff --git a/lerobot/scripts/control_robot.py b/lerobot/scripts/control_robot.py index 32271b80..ab5d0e8a 100644 --- a/lerobot/scripts/control_robot.py +++ b/lerobot/scripts/control_robot.py @@ -259,7 +259,6 @@ def record( if not robot.is_connected: robot.connect() - #listener, events = init_keyboard_listener() listener, events = init_keyboard_listener() # Execute a few seconds without recording to: @@ -315,9 +314,7 @@ def record( if events["stop_recording"]: break - # log_say("Stop recording", cfg.play_sounds, blocking=True) - log_say("Stop recording", cfg.play_sounds) - + log_say("Stop recording", cfg.play_sounds, blocking=True) stop_recording(robot, listener, cfg.display_cameras) if cfg.push_to_hub: diff --git a/lerobot/scripts/eval.py b/lerobot/scripts/eval.py index a4f79afc..47225993 100644 --- a/lerobot/scripts/eval.py +++ b/lerobot/scripts/eval.py @@ -454,7 +454,7 @@ def _compile_episode_data( @parser.wrap() -def eval(cfg: EvalPipelineConfig): +def eval_main(cfg: EvalPipelineConfig): logging.info(pformat(asdict(cfg))) # Check device is available @@ -499,4 +499,4 @@ def eval(cfg: EvalPipelineConfig): if __name__ == "__main__": init_logging() - eval() + eval_main() diff --git a/lerobot/scripts/visualize_dataset_html.py b/lerobot/scripts/visualize_dataset_html.py index a0da0869..d5825aa6 100644 --- a/lerobot/scripts/visualize_dataset_html.py +++ b/lerobot/scripts/visualize_dataset_html.py @@ -158,7 +158,7 @@ def run_server( if major_version < 2: return "Make sure to convert your LeRobotDataset to v2 & above." - episode_data_csv_str, columns = get_episode_data(dataset, episode_id) + episode_data_csv_str, columns, ignored_columns = get_episode_data(dataset, episode_id) dataset_info = { "repo_id": f"{dataset_namespace}/{dataset_name}", "num_samples": dataset.num_frames @@ -194,7 +194,7 @@ def run_server( ] response = requests.get( - f"https://huggingface.co/datasets/{repo_id}/resolve/main/meta/episodes.jsonl" + f"https://huggingface.co/datasets/{repo_id}/resolve/main/meta/episodes.jsonl", timeout=5 ) response.raise_for_status() # Split into lines and parse each line as JSON @@ -218,6 +218,7 @@ def run_server( videos_info=videos_info, episode_data_csv_str=episode_data_csv_str, columns=columns, + ignored_columns=ignored_columns, ) app.run(host=host, port=port) @@ -236,6 +237,14 @@ def get_episode_data(dataset: LeRobotDataset | IterableNamespace, episode_index) selected_columns = [col for col, ft in dataset.features.items() if ft["dtype"] == "float32"] selected_columns.remove("timestamp") + ignored_columns = [] + for column_name in selected_columns: + shape = dataset.features[column_name]["shape"] + shape_dim = len(shape) + if shape_dim > 1: + selected_columns.remove(column_name) + ignored_columns.append(column_name) + # init header of csv with state and action names header = ["timestamp"] @@ -291,7 +300,7 @@ def get_episode_data(dataset: LeRobotDataset | IterableNamespace, episode_index) csv_writer.writerows(rows) csv_string = csv_buffer.getvalue() - return csv_string, columns + return csv_string, columns, ignored_columns def get_episode_video_paths(dataset: LeRobotDataset, ep_index: int) -> list[str]: @@ -318,7 +327,9 @@ def get_episode_language_instruction(dataset: LeRobotDataset, ep_index: int) -> def get_dataset_info(repo_id: str) -> IterableNamespace: - response = requests.get(f"https://huggingface.co/datasets/{repo_id}/resolve/main/meta/info.json") + response = requests.get( + f"https://huggingface.co/datasets/{repo_id}/resolve/main/meta/info.json", timeout=5 + ) response.raise_for_status() # Raises an HTTPError for bad responses dataset_info = response.json() dataset_info["repo_id"] = repo_id diff --git a/lerobot/templates/visualize_dataset_template.html b/lerobot/templates/visualize_dataset_template.html index d81ce630..cf9d40f1 100644 --- a/lerobot/templates/visualize_dataset_template.html +++ b/lerobot/templates/visualize_dataset_template.html @@ -42,22 +42,22 @@ - +
- -
- +
- @@ -83,7 +83,7 @@

- @@ -224,49 +224,58 @@

- - - - - - - - -