chore: remove unused code (#2062)

docs/source/hilserl.mdx

@@ -95,7 +95,6 @@ class HILSerlProcessorConfig:
 class ObservationConfig:
     add_joint_velocity_to_observation: bool = False    # Add joint velocities to state
     add_current_to_observation: bool = False    # Add motor currents to state
-    add_ee_pose_to_observation: bool = False    # Add end-effector pose to state
     display_cameras: bool = False    # Display camera feeds during execution
 
 class ImagePreprocessingConfig:
@@ -105,7 +104,6 @@ class ImagePreprocessingConfig:
 class GripperConfig:
     use_gripper: bool = True    # Enable gripper control
     gripper_penalty: float = 0.0    # Penalty for inappropriate gripper usage
-    gripper_penalty_in_reward: bool = False    # Include gripper penalty in reward
 
 class ResetConfig:
     fixed_reset_joint_positions: Any | None = None    # Joint positions for reset
@@ -288,7 +286,6 @@ You can enable multiple observation processing features simultaneously:
       "observation": {
         "add_joint_velocity_to_observation": true,
         "add_current_to_observation": true,
-        "add_ee_pose_to_observation": false,
         "display_cameras": false
       }
     }

docs/source/phone_teleop.mdx

@@ -136,13 +136,12 @@ Additionally you can customize mapping or safety limits by editing the processor
   ),
   ```
 
-- The `EEBoundsAndSafety` step clamps EE motion to a workspace and checks for large ee step jumps to ensure safety. The `end_effector_bounds` are the bounds for the EE pose and can be modified to change the workspace. The `max_ee_step_m` and `max_ee_twist_step_rad` are the step limits for the EE pose and can be modified to change the safety limits.
+- The `EEBoundsAndSafety` step clamps EE motion to a workspace and checks for large ee step jumps to ensure safety. The `end_effector_bounds` are the bounds for the EE pose and can be modified to change the workspace. The `max_ee_step_m` are the step limits for the EE pose and can be modified to change the safety limits.
 
   ```examples/phone_to_so100/teleoperate.py
   EEBoundsAndSafety(
       end_effector_bounds={"min": [-1.0, -1.0, -1.0], "max": [1.0, 1.0, 1.0]},
       max_ee_step_m=0.10,
-      max_ee_twist_step_rad=0.50,
   )
   ```
 

docs/source/processors_robots_teleop.mdx

@@ -38,7 +38,7 @@ phone_to_robot_ee_pose_processor = RobotProcessorPipeline[RobotAction, RobotActi
             kinematics=kinematics_solver, end_effector_step_sizes={"x": 0.5, "y": 0.5, "z": 0.5}, motor_names=list(robot.bus.motors.keys()),
         ),
         EEBoundsAndSafety(
-            end_effector_bounds={"min": [-1.0, -1.0, -1.0], "max": [1.0, 1.0, 1.0]}, max_ee_step_m=0.20, max_ee_twist_step_rad=0.50,
+            end_effector_bounds={"min": [-1.0, -1.0, -1.0], "max": [1.0, 1.0, 1.0]}, max_ee_step_m=0.20,
         ),
         GripperVelocityToJoint(),
     ],

examples/phone_to_so100/record.py

@@ -84,7 +84,6 @@ phone_to_robot_ee_pose_processor = RobotProcessorPipeline[tuple[RobotAction, Rob
         EEBoundsAndSafety(
             end_effector_bounds={"min": [-1.0, -1.0, -1.0], "max": [1.0, 1.0, 1.0]},
             max_ee_step_m=0.20,
-            max_ee_twist_step_rad=0.50,
         ),
         GripperVelocityToJoint(speed_factor=20.0),
     ],

examples/phone_to_so100/teleoperate.py

@@ -67,7 +67,6 @@ phone_to_robot_joints_processor = RobotProcessorPipeline[tuple[RobotAction, Robo
         EEBoundsAndSafety(
             end_effector_bounds={"min": [-1.0, -1.0, -1.0], "max": [1.0, 1.0, 1.0]},
             max_ee_step_m=0.10,
-            max_ee_twist_step_rad=0.50,
         ),
         GripperVelocityToJoint(
             speed_factor=20.0,

examples/so100_to_so100_EE/record.py

@@ -101,7 +101,6 @@ ee_to_follower_joints = RobotProcessorPipeline[tuple[RobotAction, RobotObservati
         EEBoundsAndSafety(
             end_effector_bounds={"min": [-1.0, -1.0, -1.0], "max": [1.0, 1.0, 1.0]},
             max_ee_step_m=0.10,
-            max_ee_twist_step_rad=0.50,
         ),
         InverseKinematicsEEToJoints(
             kinematics=follower_kinematics_solver,

examples/so100_to_so100_EE/teleoperate.py

@@ -78,7 +78,6 @@ ee_to_follower_joints = RobotProcessorPipeline[tuple[RobotAction, RobotObservati
         EEBoundsAndSafety(
             end_effector_bounds={"min": [-1.0, -1.0, -1.0], "max": [1.0, 1.0, 1.0]},
             max_ee_step_m=0.10,
-            max_ee_twist_step_rad=0.50,
         ),
         InverseKinematicsEEToJoints(
             kinematics=follower_kinematics_solver,

src/lerobot/async_inference/helpers.py

@@ -31,7 +31,6 @@ from lerobot.utils.constants import OBS_IMAGES, OBS_STATE, OBS_STR
 from lerobot.utils.utils import init_logging
 
 Action = torch.Tensor
-ActionChunk = torch.Tensor
 
 # observation as received from the robot
 RawObservation = dict[str, torch.Tensor]
@@ -46,7 +45,7 @@ Observation = dict[str, torch.Tensor]
 def visualize_action_queue_size(action_queue_size: list[int]) -> None:
     import matplotlib.pyplot as plt
 
-    fig, ax = plt.subplots()
+    _, ax = plt.subplots()
     ax.set_title("Action Queue Size Over Time")
     ax.set_xlabel("Environment steps")
     ax.set_ylabel("Action Queue Size")

src/lerobot/cameras/utils.py

@@ -15,14 +15,10 @@
 # limitations under the License.
 
 import platform
-from pathlib import Path
-from typing import TypeAlias
 
 from .camera import Camera
 from .configs import CameraConfig, Cv2Rotation
 
-IndexOrPath: TypeAlias = int | Path
-
 
 def make_cameras_from_configs(camera_configs: dict[str, CameraConfig]) -> dict[str, Camera]:
     cameras = {}

src/lerobot/configs/default.py

@@ -16,9 +16,6 @@
 
 from dataclasses import dataclass, field
 
-from lerobot import (
-    policies,  # noqa: F401
-)
 from lerobot.datasets.transforms import ImageTransformsConfig
 from lerobot.datasets.video_utils import get_safe_default_codec
 

src/lerobot/configs/types.py

@@ -15,7 +15,6 @@
 # https://stackoverflow.com/questions/24481852/serialising-an-enum-member-to-json
 from dataclasses import dataclass
 from enum import Enum
-from typing import Any, Protocol
 
 
 class FeatureType(str, Enum):
@@ -38,10 +37,6 @@ class NormalizationMode(str, Enum):
     IDENTITY = "IDENTITY"
 
 
-class DictLike(Protocol):
-    def __getitem__(self, key: Any) -> Any: ...
-
-
 @dataclass
 class PolicyFeature:
     type: FeatureType

src/lerobot/datasets/lerobot_dataset.py

@@ -848,11 +848,6 @@ class LeRobotDataset(torch.utils.data.Dataset):
 
         return item
 
-    def _add_padding_keys(self, item: dict, padding: dict[str, list[bool]]) -> dict:
-        for key, val in padding.items():
-            item[key] = torch.BoolTensor(val)
-        return item
-
     def __len__(self):
         return self.num_frames
 
@@ -1396,11 +1391,6 @@ class MultiLeRobotDataset(torch.utils.data.Dataset):
         """
         return {repo_id: i for i, repo_id in enumerate(self.repo_ids)}
 
-    @property
-    def repo_index_to_id(self):
-        """Return the inverse mapping if repo_id_to_index."""
-        return {v: k for k, v in self.repo_id_to_index}
-
     @property
     def fps(self) -> int:
         """Frames per second used during data collection.

src/lerobot/datasets/push_dataset_to_hub/utils.py

@@ -13,67 +13,10 @@
 # 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 inspect
-from concurrent.futures import ThreadPoolExecutor
-from pathlib import Path
 
 import datasets
-import numpy
-import PIL
 import torch
 
-from lerobot.datasets.video_utils import encode_video_frames
-
-
-def concatenate_episodes(ep_dicts):
-    data_dict = {}
-
-    keys = ep_dicts[0].keys()
-    for key in keys:
-        if torch.is_tensor(ep_dicts[0][key][0]):
-            data_dict[key] = torch.cat([ep_dict[key] for ep_dict in ep_dicts])
-        else:
-            if key not in data_dict:
-                data_dict[key] = []
-            for ep_dict in ep_dicts:
-                for x in ep_dict[key]:
-                    data_dict[key].append(x)
-
-    total_frames = data_dict["frame_index"].shape[0]
-    data_dict["index"] = torch.arange(0, total_frames, 1)
-    return data_dict
-
-
-def save_images_concurrently(imgs_array: numpy.array, out_dir: Path, max_workers: int = 4):
-    out_dir = Path(out_dir)
-    out_dir.mkdir(parents=True, exist_ok=True)
-
-    def save_image(img_array, i, out_dir):
-        img = PIL.Image.fromarray(img_array)
-        img.save(str(out_dir / f"frame_{i:06d}.png"), quality=100)
-
-    num_images = len(imgs_array)
-    with ThreadPoolExecutor(max_workers=max_workers) as executor:
-        [executor.submit(save_image, imgs_array[i], i, out_dir) for i in range(num_images)]
-
-
-def get_default_encoding() -> dict:
-    """Returns the default ffmpeg encoding parameters used by `encode_video_frames`."""
-    signature = inspect.signature(encode_video_frames)
-    return {
-        k: v.default
-        for k, v in signature.parameters.items()
-        if v.default is not inspect.Parameter.empty and k in ["vcodec", "pix_fmt", "g", "crf"]
-    }
-
-
-def check_repo_id(repo_id: str) -> None:
-    if len(repo_id.split("/")) != 2:
-        raise ValueError(
-            f"""`repo_id` is expected to contain a community or user id `/` the name of the dataset
-            (e.g. 'lerobot/pusht'), but contains '{repo_id}'."""
-        )
-
 
 # TODO(aliberts): remove
 def calculate_episode_data_index(hf_dataset: datasets.Dataset) -> dict[str, torch.Tensor]:

src/lerobot/datasets/streaming_dataset.py

@@ -298,9 +298,7 @@ class StreamingLeRobotDataset(torch.utils.data.IterableDataset):
 
         return padding_mask
 
-    def make_frame(
+    def make_frame(self, dataset_iterator: Backtrackable) -> Generator:
-        self, dataset_iterator: Backtrackable, previous_dataset_iterator: Backtrackable | None = None
-    ) -> Generator:
         """Makes a frame starting from a dataset iterator"""
         item = next(dataset_iterator)
         item = item_to_torch(item)

src/lerobot/datasets/utils.py

@@ -67,18 +67,6 @@ DEFAULT_IMAGE_PATH = "images/{image_key}/episode-{episode_index:06d}/frame-{fram
 LEGACY_EPISODES_PATH = "meta/episodes.jsonl"
 LEGACY_EPISODES_STATS_PATH = "meta/episodes_stats.jsonl"
 LEGACY_TASKS_PATH = "meta/tasks.jsonl"
-LEGACY_DEFAULT_VIDEO_PATH = "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4"
-LEGACY_DEFAULT_PARQUET_PATH = "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet"
-
-DATASET_CARD_TEMPLATE = """
----
-# Metadata will go there
----
-This dataset was created using [LeRobot](https://github.com/huggingface/lerobot).
-
-## {}
-
-"""
 
 DEFAULT_FEATURES = {
     "timestamp": {"dtype": "float32", "shape": (1,), "names": None},
@@ -383,12 +371,6 @@ def load_episodes(local_dir: Path) -> datasets.Dataset:
     return episodes
 
 
-def backward_compatible_episodes_stats(
-    stats: dict[str, dict[str, np.ndarray]], episodes: list[int]
-) -> dict[int, dict[str, dict[str, np.ndarray]]]:
-    return dict.fromkeys(episodes, stats)
-
-
 def load_image_as_numpy(
     fpath: str | Path, dtype: np.dtype = np.float32, channel_first: bool = True
 ) -> np.ndarray:
@@ -1346,12 +1328,6 @@ class Backtrackable(Generic[T]):
         # When cursor<0, slice so the order remains chronological
         return list(self._back_buf)[: self._cursor or None]
 
-    def lookahead_buffer(self) -> list[T]:
-        """
-        Return a copy of the current lookahead buffer.
-        """
-        return list(self._ahead_buf)
-
     def can_peek_back(self, steps: int = 1) -> bool:
         """
         Check if we can go back `steps` items without raising an IndexError.
@@ -1377,31 +1353,6 @@ class Backtrackable(Generic[T]):
         except StopIteration:
             return False
 
-    def reset_cursor(self) -> None:
-        """
-        Reset cursor to the most recent position (equivalent to calling next()
-        until you're back to the latest item).
-        """
-        self._cursor = 0
-
-    def clear_ahead_buffer(self) -> None:
-        """
-        Clear the ahead buffer, discarding any pre-fetched items.
-        """
-        self._ahead_buf.clear()
-
-    def switch_source_iterable(self, new_source: Iterable[T]) -> None:
-        """
-        Switch the source of the backtrackable to a new iterable, keeping the history.
-
-        This is useful when iterating over a sequence of datasets. The history from the
-        previous source is kept, but the lookahead buffer is cleared. The cursor is reset
-        to the present.
-        """
-        self._source = iter(new_source)
-        self.clear_ahead_buffer()
-        self.reset_cursor()
-
 
 def safe_shard(dataset: datasets.IterableDataset, index: int, num_shards: int) -> datasets.Dataset:
     """

src/lerobot/datasets/video_utils.py

@@ -585,19 +585,6 @@ def get_video_pixel_channels(pix_fmt: str) -> int:
         raise ValueError("Unknown format")
 
 
-def get_image_pixel_channels(image: Image):
-    if image.mode == "L":
-        return 1  # Grayscale
-    elif image.mode == "LA":
-        return 2  # Grayscale + Alpha
-    elif image.mode == "RGB":
-        return 3  # RGB
-    elif image.mode == "RGBA":
-        return 4  # RGBA
-    else:
-        raise ValueError("Unknown format")
-
-
 def get_video_duration_in_s(video_path: Path | str) -> float:
     """
     Get the duration of a video file in seconds using PyAV.

src/lerobot/envs/configs.py

@@ -193,7 +193,6 @@ class ObservationConfig:
 
     add_joint_velocity_to_observation: bool = False
     add_current_to_observation: bool = False
-    add_ee_pose_to_observation: bool = False
     display_cameras: bool = False
 
 
@@ -203,7 +202,6 @@ class GripperConfig:
 
     use_gripper: bool = True
     gripper_penalty: float = 0.0
-    gripper_penalty_in_reward: bool = False
 
 
 @dataclass

src/lerobot/motors/motors_bus.py

@@ -99,12 +99,6 @@ class Motor:
     norm_mode: MotorNormMode
 
 
-class JointOutOfRangeError(Exception):
-    def __init__(self, message="Joint is out of range"):
-        self.message = message
-        super().__init__(self.message)
-
-
 class PortHandler(Protocol):
     def __init__(self, port_name):
         self.is_open: bool

src/lerobot/policies/sac/configuration_sac.py

@@ -139,8 +139,6 @@ class SACConfig(PreTrainedConfig):
     # Training parameter
     # Number of steps for online training
     online_steps: int = 1000000
-    # Seed for the online environment
-    online_env_seed: int = 10000
     # Capacity of the online replay buffer
     online_buffer_capacity: int = 100000
     # Capacity of the offline replay buffer

src/lerobot/policies/sac/modeling_sac.py

@@ -1061,15 +1061,3 @@ class TanhMultivariateNormalDiag(TransformedDistribution):
             x = transform(x)
 
         return x
-
-
-def _convert_normalization_params_to_tensor(normalization_params: dict) -> dict:
-    converted_params = {}
-    for outer_key, inner_dict in normalization_params.items():
-        converted_params[outer_key] = {}
-        for key, value in inner_dict.items():
-            converted_params[outer_key][key] = torch.tensor(value)
-            if "image" in outer_key:
-                converted_params[outer_key][key] = converted_params[outer_key][key].view(3, 1, 1)
-
-    return converted_params

src/lerobot/policies/vqbet/configuration_vqbet.py

@@ -82,7 +82,6 @@ class VQBeTConfig(PreTrainedConfig):
         gpt_n_head: Number of headers of GPT
         gpt_hidden_dim: Size of hidden dimensions of GPT
         dropout: Dropout rate for GPT
-        mlp_hidden_dim: Size of hidden dimensions of offset header / bin prediction headers parts of VQ-BeT
         offset_loss_weight:  A constant that is multiplied to the offset loss
         primary_code_loss_weight: A constant that is multiplied to the primary code prediction loss
         secondary_code_loss_weight: A constant that is multiplied to the secondary code prediction loss
@@ -125,7 +124,6 @@ class VQBeTConfig(PreTrainedConfig):
     gpt_n_head: int = 8
     gpt_hidden_dim: int = 512
     dropout: float = 0.1
-    mlp_hidden_dim: int = 1024
     offset_loss_weight: float = 10000.0
     primary_code_loss_weight: float = 5.0
     secondary_code_loss_weight: float = 0.5

src/lerobot/policies/vqbet/vqbet_utils.py

@@ -231,16 +231,6 @@ class GPT(nn.Module):
             torch.nn.init.zeros_(module.bias)
             torch.nn.init.ones_(module.weight)
 
-    def crop_block_size(self, gpt_block_size):
-        # model surgery to decrease the block size if necessary
-        # e.g. we may load the GPT2 pretrained model checkpoint (block size 1024)
-        # but want to use a smaller block size for some smaller, simpler model
-        assert gpt_block_size <= self.config.gpt_block_size
-        self.config.gpt_block_size = gpt_block_size
-        self.transformer.wpe.weight = nn.Parameter(self.transformer.wpe.weight[:gpt_block_size])
-        for block in self.transformer.h:
-            block.attn.bias = block.attn.bias[:, :, :gpt_block_size, :gpt_block_size]
-
     def configure_parameters(self):
         """
         This long function is unfortunately doing something very simple and is being very defensive:

src/lerobot/processor/delta_action_processor.py

@@ -83,14 +83,12 @@ class MapDeltaActionToRobotActionStep(RobotActionProcessorStep):
 
     Attributes:
         position_scale: A factor to scale the delta position inputs.
-        rotation_scale: A factor to scale the delta rotation inputs (currently unused).
         noise_threshold: The magnitude below which delta inputs are considered noise
                          and do not trigger an "enabled" state.
     """
 
     # Scale factors for delta movements
     position_scale: float = 1.0
-    rotation_scale: float = 0.0  # No rotation deltas for gamepad/keyboard
     noise_threshold: float = 1e-3  # 1 mm threshold to filter out noise
 
     def action(self, action: RobotAction) -> RobotAction:

src/lerobot/rl/actor.py

@@ -97,8 +97,6 @@ from .gym_manipulator import (
     step_env_and_process_transition,
 )
 
-ACTOR_SHUTDOWN_TIMEOUT = 30
-
 # Main entry point
 
 

src/lerobot/rl/learner.py

@@ -102,8 +102,6 @@ from lerobot.utils.utils import (
 
 from .learner_service import MAX_WORKERS, SHUTDOWN_TIMEOUT, LearnerService
 
-LOG_PREFIX = "[LEARNER]"
-
 
 @parser.wrap()
 def train_cli(cfg: TrainRLServerPipelineConfig):

src/lerobot/robots/hope_jr/hope_jr_arm.py

@@ -105,7 +105,7 @@ class HopeJrArm(Robot):
     def is_calibrated(self) -> bool:
         return self.bus.is_calibrated
 
-    def calibrate(self, limb_name: str = None) -> None:
+    def calibrate(self) -> None:
         groups = {
             "all": list(self.bus.motors.keys()),
             "shoulder": ["shoulder_pitch", "shoulder_yaw", "shoulder_roll"],

src/lerobot/robots/so100_follower/robot_kinematic_processor.py

@@ -193,16 +193,12 @@ class EEBoundsAndSafety(RobotActionProcessorStep):
     Attributes:
         end_effector_bounds: A dictionary with "min" and "max" keys for position clipping.
         max_ee_step_m: The maximum allowed change in position (in meters) between steps.
-        max_ee_twist_step_rad: The maximum allowed change in orientation (in radians) between steps.
         _last_pos: Internal state storing the last commanded position.
-        _last_twist: Internal state storing the last commanded orientation.
     """
 
     end_effector_bounds: dict
     max_ee_step_m: float = 0.05
-    max_ee_twist_step_rad: float = 0.20
     _last_pos: np.ndarray | None = field(default=None, init=False, repr=False)
-    _last_twist: np.ndarray | None = field(default=None, init=False, repr=False)
 
     def action(self, action: RobotAction) -> RobotAction:
         x = action["ee.x"]
@@ -233,7 +229,6 @@ class EEBoundsAndSafety(RobotActionProcessorStep):
                 raise ValueError(f"EE jump {n:.3f}m > {self.max_ee_step_m}m")
 
         self._last_pos = pos
-        self._last_twist = twist
 
         action["ee.x"] = float(pos[0])
         action["ee.y"] = float(pos[1])
@@ -246,7 +241,6 @@ class EEBoundsAndSafety(RobotActionProcessorStep):
     def reset(self):
         """Resets the last known position and orientation."""
         self._last_pos = None
-        self._last_twist = None
 
     def transform_features(
         self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]]

src/lerobot/robots/stretch3/configuration_stretch3.py

@@ -49,5 +49,3 @@ class Stretch3RobotConfig(RobotConfig):
             ),
         }
     )
-
-    mock: bool = False

src/lerobot/robots/stretch3/robot_stretch3.py

@@ -164,10 +164,6 @@ class Stretch3Robot(Robot):
         # TODO(aliberts): return action_sent when motion is limited
         return action
 
-    def print_logs(self) -> None:
-        pass
-        # TODO(aliberts): move robot-specific logs logic here
-
     def teleop_safety_stop(self) -> None:
         if self.teleop is not None:
             self.teleop._safety_stop(robot=self)

src/lerobot/teleoperators/gamepad/gamepad_utils.py

@@ -52,10 +52,6 @@ class InputController:
         """Get the current movement deltas (dx, dy, dz) in meters."""
         return 0.0, 0.0, 0.0
 
-    def should_quit(self):
-        """Return True if the user has requested to quit."""
-        return not self.running
-
     def update(self):
         """Update controller state - call this once per frame."""
         pass
@@ -198,14 +194,6 @@ class KeyboardController(InputController):
 
         return delta_x, delta_y, delta_z
 
-    def should_quit(self):
-        """Return True if ESC was pressed."""
-        return self.key_states["quit"]
-
-    def should_save(self):
-        """Return True if Enter was pressed (save episode)."""
-        return self.key_states["success"] or self.key_states["failure"]
-
 
 class GamepadController(InputController):
     """Generate motion deltas from gamepad input."""
@@ -351,8 +339,6 @@ class GamepadControllerHID(InputController):
 
         # Button states
         self.buttons = {}
-        self.quit_requested = False
-        self.save_requested = False
 
     def find_device(self):
         """Look for the gamepad device by vendor and product ID."""
@@ -472,11 +458,3 @@ class GamepadControllerHID(InputController):
         delta_z = -self.right_y * self.z_step_size  # Up/down
 
         return delta_x, delta_y, delta_z
-
-    def should_quit(self):
-        """Return True if quit button was pressed."""
-        return self.quit_requested
-
-    def should_save(self):
-        """Return True if save button was pressed."""
-        return self.save_requested

src/lerobot/teleoperators/keyboard/configuration_keyboard.py

@@ -22,8 +22,9 @@ from ..config import TeleoperatorConfig
 @TeleoperatorConfig.register_subclass("keyboard")
 @dataclass
 class KeyboardTeleopConfig(TeleoperatorConfig):
+    """KeyboardTeleopConfig"""
+
     # TODO(Steven): Consider setting in here the keys that we want to capture/listen
-    mock: bool = False
 
 
 @TeleoperatorConfig.register_subclass("keyboard_ee")

src/lerobot/teleoperators/stretch3_gamepad/configuration_stretch3.py

@@ -22,4 +22,4 @@ from ..config import TeleoperatorConfig
 @TeleoperatorConfig.register_subclass("stretch3")
 @dataclass
 class Stretch3GamePadConfig(TeleoperatorConfig):
-    mock: bool = False
+    """Stretch3GamePadConfig"""

src/lerobot/teleoperators/stretch3_gamepad/stretch3_gamepad.py

@@ -112,10 +112,6 @@ class Stretch3GamePad(Teleoperator):
     def send_feedback(self, feedback: np.ndarray) -> None:
         pass
 
-    def print_logs(self) -> None:
-        pass
-        # TODO(aliberts): move robot-specific logs logic here
-
     def disconnect(self) -> None:
         self.api.stop()
         self.is_connected = False

src/lerobot/utils/constants.py

@@ -33,7 +33,6 @@ TRUNCATED = "next.truncated"
 DONE = "next.done"
 
 ROBOTS = "robots"
-ROBOT_TYPE = "robot_type"
 TELEOPERATORS = "teleoperators"
 
 # files & directories

src/lerobot/utils/errors.py

@@ -30,14 +30,3 @@ class DeviceAlreadyConnectedError(ConnectionError):
     ):
         self.message = message
         super().__init__(self.message)
-
-
-class InvalidActionError(ValueError):
-    """Exception raised when an action is already invalid."""
-
-    def __init__(
-        self,
-        message="The action is invalid. Check the value follows what it is expected from the action space.",
-    ):
-        self.message = message
-        super().__init__(self.message)

src/lerobot/utils/utils.py

@@ -330,10 +330,6 @@ class TimerManager:
     def history(self) -> list[float]:
         return deepcopy(self._history)
 
-    @property
-    def fps_history(self) -> list[float]:
-        return [1.0 / t for t in self._history]
-
     @property
     def fps_last(self) -> float:
         return 0.0 if self.last == 0 else 1.0 / self.last

tests/policies/test_sac_config.py

@@ -69,7 +69,6 @@ def test_sac_config_default_initialization():
 
     # Training parameters
     assert config.online_steps == 1000000
-    assert config.online_env_seed == 10000
     assert config.online_buffer_capacity == 100000
     assert config.offline_buffer_capacity == 100000
     assert config.async_prefetch is False