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chore: remove unused code (#2062)

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This commit is contained in:
Steven Palma
2025-09-29 10:49:36 +02:00
committed by GitHub
parent 62e9849ffd
commit f59eb54f5c
37 changed files with 8 additions and 254 deletions
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3
docs/source/hilserl.mdx
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@@ -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
}
}

3
docs/source/phone_teleop.mdx
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@@ -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,
)
```

2
docs/source/processors_robots_teleop.mdx
View File

@@ -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(),
],

1
examples/phone_to_so100/record.py
View File

@@ -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),
],

1
examples/phone_to_so100/teleoperate.py
View File

@@ -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,

1
examples/so100_to_so100_EE/record.py
View File

@@ -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,

1
examples/so100_to_so100_EE/teleoperate.py
View File

@@ -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,

3
src/lerobot/async_inference/helpers.py
View File

@@ -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")

4
src/lerobot/cameras/utils.py
View File

@@ -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 = {}

3
src/lerobot/configs/default.py
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@@ -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

5
src/lerobot/configs/types.py
View File

@@ -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

10
src/lerobot/datasets/lerobot_dataset.py
View File

@@ -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.

57
src/lerobot/datasets/push_dataset_to_hub/utils.py
View File

@@ -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]:

4
src/lerobot/datasets/streaming_dataset.py
View File

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

49
src/lerobot/datasets/utils.py
View File

@@ -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:
"""

13
src/lerobot/datasets/video_utils.py
View File

@@ -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.

2
src/lerobot/envs/configs.py
View File

@@ -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

6
src/lerobot/motors/motors_bus.py
View File

@@ -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

2
src/lerobot/policies/sac/configuration_sac.py
View File

@@ -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

12
src/lerobot/policies/sac/modeling_sac.py
View File

@@ -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

2
src/lerobot/policies/vqbet/configuration_vqbet.py
View File

@@ -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

10
src/lerobot/policies/vqbet/vqbet_utils.py
View File

@@ -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:

2
src/lerobot/processor/delta_action_processor.py
View File

@@ -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:

2
src/lerobot/rl/actor.py
View File

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

2
src/lerobot/rl/learner.py
View File

@@ -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):

2
src/lerobot/robots/hope_jr/hope_jr_arm.py
View File

@@ -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"],

6
src/lerobot/robots/so100_follower/robot_kinematic_processor.py
View File

@@ -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]]

2
src/lerobot/robots/stretch3/configuration_stretch3.py
View File

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

4
src/lerobot/robots/stretch3/robot_stretch3.py
View File

@@ -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)

22
src/lerobot/teleoperators/gamepad/gamepad_utils.py
View File

@@ -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

3
src/lerobot/teleoperators/keyboard/configuration_keyboard.py
View File

@@ -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")

2
src/lerobot/teleoperators/stretch3_gamepad/configuration_stretch3.py
View File

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

4
src/lerobot/teleoperators/stretch3_gamepad/stretch3_gamepad.py
View File

@@ -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

1
src/lerobot/utils/constants.py
View File

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

11
src/lerobot/utils/errors.py
View File

@@ -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)

4
src/lerobot/utils/utils.py
View File

@@ -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

1
tests/policies/test_sac_config.py
View File

@@ -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