[HIL-SERL]Remove overstrict pre-commit modifications (#1028)

benchmarks/video/run_video_benchmark.py

@@ -32,11 +32,7 @@ import numpy as np
 import pandas as pd
 import PIL
 import torch
-from skimage.metrics import (
-    mean_squared_error,
-    peak_signal_noise_ratio,
-    structural_similarity,
-)
+from skimage.metrics import mean_squared_error, peak_signal_noise_ratio, structural_similarity
 from tqdm import tqdm
 
 from lerobot.common.datasets.lerobot_dataset import LeRobotDataset
@@ -98,11 +94,7 @@ def load_original_frames(imgs_dir: Path, timestamps: list[float], fps: int) -> t
 
 
 def save_decoded_frames(
-    imgs_dir: Path,
-    save_dir: Path,
-    frames: torch.Tensor,
-    timestamps: list[float],
-    fps: int,
+    imgs_dir: Path, save_dir: Path, frames: torch.Tensor, timestamps: list[float], fps: int
 ) -> None:
     if save_dir.exists() and len(list(save_dir.glob("frame_*.png"))) == len(timestamps):
         return
@@ -112,10 +104,7 @@ def save_decoded_frames(
         idx = int(ts * fps)
         frame_hwc = (frames[i].permute((1, 2, 0)) * 255).type(torch.uint8).cpu().numpy()
         PIL.Image.fromarray(frame_hwc).save(save_dir / f"frame_{idx:06d}_decoded.png")
-        shutil.copyfile(
-            imgs_dir / f"frame_{idx:06d}.png",
-            save_dir / f"frame_{idx:06d}_original.png",
-        )
+        shutil.copyfile(imgs_dir / f"frame_{idx:06d}.png", save_dir / f"frame_{idx:06d}_original.png")
 
 
 def save_first_episode(imgs_dir: Path, dataset: LeRobotDataset) -> None:
@@ -131,11 +120,7 @@ def save_first_episode(imgs_dir: Path, dataset: LeRobotDataset) -> None:
     imgs_dataset = hf_dataset.select_columns(img_keys[0])
 
     for i, item in enumerate(
-        tqdm(
-            imgs_dataset,
-            desc=f"saving {dataset.repo_id} first episode images",
-            leave=False,
-        )
+        tqdm(imgs_dataset, desc=f"saving {dataset.repo_id} first episode images", leave=False)
     ):
         img = item[img_keys[0]]
         img.save(str(imgs_dir / f"frame_{i:06d}.png"), quality=100)
@@ -290,9 +275,7 @@ def benchmark_encoding_decoding(
     random.seed(seed)
     benchmark_table = []
     for timestamps_mode in tqdm(
-        decoding_cfg["timestamps_modes"],
-        desc="decodings (timestamps_modes)",
-        leave=False,
+        decoding_cfg["timestamps_modes"], desc="decodings (timestamps_modes)", leave=False
     ):
         for backend in tqdm(decoding_cfg["backends"], desc="decodings (backends)", leave=False):
             benchmark_row = benchmark_decoding(

examples/1_load_lerobot_dataset.py

@@ -32,10 +32,7 @@ import torch
 from huggingface_hub import HfApi
 
 import lerobot
-from lerobot.common.datasets.lerobot_dataset import (
-    LeRobotDataset,
-    LeRobotDatasetMetadata,
-)
+from lerobot.common.datasets.lerobot_dataset import LeRobotDataset, LeRobotDatasetMetadata
 
 # We ported a number of existing datasets ourselves, use this to see the list:
 print("List of available datasets:")

examples/3_train_policy.py

@@ -22,10 +22,7 @@ from pathlib import Path
 
 import torch
 
-from lerobot.common.datasets.lerobot_dataset import (
-    LeRobotDataset,
-    LeRobotDatasetMetadata,
-)
+from lerobot.common.datasets.lerobot_dataset import LeRobotDataset, LeRobotDatasetMetadata
 from lerobot.common.datasets.utils import dataset_to_policy_features
 from lerobot.common.policies.diffusion.configuration_diffusion import DiffusionConfig
 from lerobot.common.policies.diffusion.modeling_diffusion import DiffusionPolicy
@@ -80,24 +77,7 @@ def main():
         # Load the previous action (-0.1), the next action to be executed (0.0),
         # and 14 future actions with a 0.1 seconds spacing. All these actions will be
         # used to supervise the policy.
-        "action": [
-            -0.1,
-            0.0,
-            0.1,
-            0.2,
-            0.3,
-            0.4,
-            0.5,
-            0.6,
-            0.7,
-            0.8,
-            0.9,
-            1.0,
-            1.1,
-            1.2,
-            1.3,
-            1.4,
-        ],
+        "action": [-0.1, 0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2, 1.3, 1.4],
     }
 
     # We can then instantiate the dataset with these delta_timestamps configuration.

examples/advanced/2_calculate_validation_loss.py

@@ -26,10 +26,7 @@ import math
 
 import torch
 
-from lerobot.common.datasets.lerobot_dataset import (
-    LeRobotDataset,
-    LeRobotDatasetMetadata,
-)
+from lerobot.common.datasets.lerobot_dataset import LeRobotDataset, LeRobotDatasetMetadata
 from lerobot.common.policies.diffusion.modeling_diffusion import DiffusionPolicy
 
 
@@ -54,24 +51,7 @@ def main():
         # Load the previous action (-0.1), the next action to be executed (0.0),
         # and 14 future actions with a 0.1 seconds spacing. All these actions will be
         # used to calculate the loss.
-        "action": [
-            -0.1,
-            0.0,
-            0.1,
-            0.2,
-            0.3,
-            0.4,
-            0.5,
-            0.6,
-            0.7,
-            0.8,
-            0.9,
-            1.0,
-            1.1,
-            1.2,
-            1.3,
-            1.4,
-        ],
+        "action": [-0.1, 0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2, 1.3, 1.4],
     }
 
     # Load the last 10% of episodes of the dataset as a validation set.

lerobot/common/datasets/compute_stats.py

@@ -19,10 +19,7 @@ from lerobot.common.datasets.utils import load_image_as_numpy
 
 
 def estimate_num_samples(
-    dataset_len: int,
-    min_num_samples: int = 100,
-    max_num_samples: int = 10_000,
-    power: float = 0.75,
+    dataset_len: int, min_num_samples: int = 100, max_num_samples: int = 10_000, power: float = 0.75
 ) -> int:
     """Heuristic to estimate the number of samples based on dataset size.
     The power controls the sample growth relative to dataset size.
@@ -126,9 +123,7 @@ def _assert_type_and_shape(stats_list: list[dict[str, dict]]):
                     raise ValueError(f"Shape of '{k}' must be (3,1,1), but is {v.shape} instead.")
 
 
-def aggregate_feature_stats(
-    stats_ft_list: list[dict[str, dict]],
-) -> dict[str, dict[str, np.ndarray]]:
+def aggregate_feature_stats(stats_ft_list: list[dict[str, dict]]) -> dict[str, dict[str, np.ndarray]]:
     """Aggregates stats for a single feature."""
     means = np.stack([s["mean"] for s in stats_ft_list])
     variances = np.stack([s["std"] ** 2 for s in stats_ft_list])
@@ -157,9 +152,7 @@ def aggregate_feature_stats(
     }
 
 
-def aggregate_stats(
-    stats_list: list[dict[str, dict]],
-) -> dict[str, dict[str, np.ndarray]]:
+def aggregate_stats(stats_list: list[dict[str, dict]]) -> dict[str, dict[str, np.ndarray]]:
     """Aggregate stats from multiple compute_stats outputs into a single set of stats.
 
     The final stats will have the union of all data keys from each of the stats dicts.

lerobot/common/datasets/online_buffer.py

@@ -154,32 +154,14 @@ class OnlineBuffer(torch.utils.data.Dataset):
             OnlineBuffer.NEXT_INDEX_KEY: {"dtype": np.dtype("int64"), "shape": ()},
             # Since the memmap is initialized with all-zeros, this keeps track of which indices are occupied
             # with real data rather than the dummy initialization.
-            OnlineBuffer.OCCUPANCY_MASK_KEY: {
-                "dtype": np.dtype("?"),
-                "shape": (buffer_capacity,),
-            },
-            OnlineBuffer.INDEX_KEY: {
-                "dtype": np.dtype("int64"),
-                "shape": (buffer_capacity,),
-            },
-            OnlineBuffer.FRAME_INDEX_KEY: {
-                "dtype": np.dtype("int64"),
-                "shape": (buffer_capacity,),
-            },
-            OnlineBuffer.EPISODE_INDEX_KEY: {
-                "dtype": np.dtype("int64"),
-                "shape": (buffer_capacity,),
-            },
-            OnlineBuffer.TIMESTAMP_KEY: {
-                "dtype": np.dtype("float64"),
-                "shape": (buffer_capacity,),
-            },
+            OnlineBuffer.OCCUPANCY_MASK_KEY: {"dtype": np.dtype("?"), "shape": (buffer_capacity,)},
+            OnlineBuffer.INDEX_KEY: {"dtype": np.dtype("int64"), "shape": (buffer_capacity,)},
+            OnlineBuffer.FRAME_INDEX_KEY: {"dtype": np.dtype("int64"), "shape": (buffer_capacity,)},
+            OnlineBuffer.EPISODE_INDEX_KEY: {"dtype": np.dtype("int64"), "shape": (buffer_capacity,)},
+            OnlineBuffer.TIMESTAMP_KEY: {"dtype": np.dtype("float64"), "shape": (buffer_capacity,)},
         }
         for k, v in data_spec.items():
-            complete_data_spec[k] = {
-                "dtype": v["dtype"],
-                "shape": (buffer_capacity, *v["shape"]),
-            }
+            complete_data_spec[k] = {"dtype": v["dtype"], "shape": (buffer_capacity, *v["shape"])}
         return complete_data_spec
 
     def add_data(self, data: dict[str, np.ndarray]):

lerobot/common/datasets/push_dataset_to_hub/utils.py

@@ -77,9 +77,7 @@ def check_repo_id(repo_id: str) -> None:
 
 
 # TODO(aliberts): remove
-def calculate_episode_data_index(
-    hf_dataset: datasets.Dataset,
-) -> Dict[str, torch.Tensor]:
+def calculate_episode_data_index(hf_dataset: datasets.Dataset) -> Dict[str, torch.Tensor]:
     """
     Calculate episode data index for the provided HuggingFace Dataset. Relies on episode_index column of hf_dataset.
 

lerobot/common/datasets/sampler.py

@@ -43,10 +43,7 @@ class EpisodeAwareSampler:
         ):
             if episode_indices_to_use is None or episode_idx in episode_indices_to_use:
                 indices.extend(
-                    range(
-                        start_index.item() + drop_n_first_frames,
-                        end_index.item() - drop_n_last_frames,
-                    )
+                    range(start_index.item() + drop_n_first_frames, end_index.item() - drop_n_last_frames)
                 )
 
         self.indices = indices

lerobot/common/datasets/v2/batch_convert_dataset_v1_to_v2.py

@@ -118,10 +118,7 @@ DATASETS = {
         "single_task": "Place the battery into the slot of the remote controller.",
         **ALOHA_STATIC_INFO,
     },
-    "aloha_static_candy": {
-        "single_task": "Pick up the candy and unwrap it.",
-        **ALOHA_STATIC_INFO,
-    },
+    "aloha_static_candy": {"single_task": "Pick up the candy and unwrap it.", **ALOHA_STATIC_INFO},
     "aloha_static_coffee": {
         "single_task": "Place the coffee capsule inside the capsule container, then place the cup onto the center of the cup tray, then push the 'Hot Water' and 'Travel Mug' buttons.",
         **ALOHA_STATIC_INFO,
@@ -170,22 +167,13 @@ DATASETS = {
         "single_task": "Pick up the plastic cup with the left arm, then pop its lid open with the right arm.",
         **ALOHA_STATIC_INFO,
     },
-    "aloha_static_ziploc_slide": {
-        "single_task": "Slide open the ziploc bag.",
-        **ALOHA_STATIC_INFO,
-    },
-    "aloha_sim_insertion_scripted": {
-        "single_task": "Insert the peg into the socket.",
-        **ALOHA_STATIC_INFO,
-    },
+    "aloha_static_ziploc_slide": {"single_task": "Slide open the ziploc bag.", **ALOHA_STATIC_INFO},
+    "aloha_sim_insertion_scripted": {"single_task": "Insert the peg into the socket.", **ALOHA_STATIC_INFO},
     "aloha_sim_insertion_scripted_image": {
         "single_task": "Insert the peg into the socket.",
         **ALOHA_STATIC_INFO,
     },
-    "aloha_sim_insertion_human": {
-        "single_task": "Insert the peg into the socket.",
-        **ALOHA_STATIC_INFO,
-    },
+    "aloha_sim_insertion_human": {"single_task": "Insert the peg into the socket.", **ALOHA_STATIC_INFO},
     "aloha_sim_insertion_human_image": {
         "single_task": "Insert the peg into the socket.",
         **ALOHA_STATIC_INFO,
@@ -206,19 +194,10 @@ DATASETS = {
         "single_task": "Pick up the cube with the right arm and transfer it to the left arm.",
         **ALOHA_STATIC_INFO,
     },
-    "pusht": {
-        "single_task": "Push the T-shaped block onto the T-shaped target.",
-        **PUSHT_INFO,
-    },
-    "pusht_image": {
-        "single_task": "Push the T-shaped block onto the T-shaped target.",
-        **PUSHT_INFO,
-    },
+    "pusht": {"single_task": "Push the T-shaped block onto the T-shaped target.", **PUSHT_INFO},
+    "pusht_image": {"single_task": "Push the T-shaped block onto the T-shaped target.", **PUSHT_INFO},
     "unitreeh1_fold_clothes": {"single_task": "Fold the sweatshirt.", **UNITREEH_INFO},
-    "unitreeh1_rearrange_objects": {
-        "single_task": "Put the object into the bin.",
-        **UNITREEH_INFO,
-    },
+    "unitreeh1_rearrange_objects": {"single_task": "Put the object into the bin.", **UNITREEH_INFO},
     "unitreeh1_two_robot_greeting": {
         "single_task": "Greet the other robot with a high five.",
         **UNITREEH_INFO,
@@ -228,31 +207,13 @@ DATASETS = {
         **UNITREEH_INFO,
     },
     "xarm_lift_medium": {"single_task": "Pick up the cube and lift it.", **XARM_INFO},
-    "xarm_lift_medium_image": {
-        "single_task": "Pick up the cube and lift it.",
-        **XARM_INFO,
-    },
-    "xarm_lift_medium_replay": {
-        "single_task": "Pick up the cube and lift it.",
-        **XARM_INFO,
-    },
-    "xarm_lift_medium_replay_image": {
-        "single_task": "Pick up the cube and lift it.",
-        **XARM_INFO,
-    },
+    "xarm_lift_medium_image": {"single_task": "Pick up the cube and lift it.", **XARM_INFO},
+    "xarm_lift_medium_replay": {"single_task": "Pick up the cube and lift it.", **XARM_INFO},
+    "xarm_lift_medium_replay_image": {"single_task": "Pick up the cube and lift it.", **XARM_INFO},
     "xarm_push_medium": {"single_task": "Push the cube onto the target.", **XARM_INFO},
-    "xarm_push_medium_image": {
-        "single_task": "Push the cube onto the target.",
-        **XARM_INFO,
-    },
-    "xarm_push_medium_replay": {
-        "single_task": "Push the cube onto the target.",
-        **XARM_INFO,
-    },
-    "xarm_push_medium_replay_image": {
-        "single_task": "Push the cube onto the target.",
-        **XARM_INFO,
-    },
+    "xarm_push_medium_image": {"single_task": "Push the cube onto the target.", **XARM_INFO},
+    "xarm_push_medium_replay": {"single_task": "Push the cube onto the target.", **XARM_INFO},
+    "xarm_push_medium_replay_image": {"single_task": "Push the cube onto the target.", **XARM_INFO},
     "umi_cup_in_the_wild": {
         "single_task": "Put the cup on the plate.",
         "license": "apache-2.0",

lerobot/common/datasets/v2/convert_dataset_v1_to_v2.py

@@ -379,12 +379,7 @@ def fix_lfs_video_files_tracking(work_dir: Path, lfs_untracked_videos: list[str]
     for i in range(0, len(lfs_untracked_videos), 100):
         files = lfs_untracked_videos[i : i + 100]
         try:
-            subprocess.run(
-                ["git", "rm", "--cached", *files],
-                cwd=work_dir,
-                capture_output=True,
-                check=True,
-            )
+            subprocess.run(["git", "rm", "--cached", *files], cwd=work_dir, capture_output=True, check=True)
         except subprocess.CalledProcessError as e:
             print("git rm --cached ERROR:")
             print(e.stderr)
@@ -407,17 +402,7 @@ def _lfs_clone(repo_id: str, work_dir: Path, branch: str) -> None:
     repo_url = f"https://huggingface.co/datasets/{repo_id}"
     env = {"GIT_LFS_SKIP_SMUDGE": "1"}  # Prevent downloading LFS files
     subprocess.run(
-        [
-            "git",
-            "clone",
-            "--branch",
-            branch,
-            "--single-branch",
-            "--depth",
-            "1",
-            repo_url,
-            str(work_dir),
-        ],
+        ["git", "clone", "--branch", branch, "--single-branch", "--depth", "1", repo_url, str(work_dir)],
         check=True,
         env=env,
     )
@@ -425,11 +410,7 @@ def _lfs_clone(repo_id: str, work_dir: Path, branch: str) -> None:
 
 def _get_lfs_untracked_videos(work_dir: Path, video_files: list[str]) -> list[str]:
     lfs_tracked_files = subprocess.run(
-        ["git", "lfs", "ls-files", "-n"],
-        cwd=work_dir,
-        capture_output=True,
-        text=True,
-        check=True,
+        ["git", "lfs", "ls-files", "-n"], cwd=work_dir, capture_output=True, text=True, check=True
     )
     lfs_tracked_files = set(lfs_tracked_files.stdout.splitlines())
     return [f for f in video_files if f not in lfs_tracked_files]
@@ -443,11 +424,7 @@ def get_videos_info(repo_id: str, local_dir: Path, video_keys: list[str], branch
     ]
     hub_api = HfApi()
     hub_api.snapshot_download(
-        repo_id=repo_id,
-        repo_type="dataset",
-        local_dir=local_dir,
-        revision=branch,
-        allow_patterns=video_files,
+        repo_id=repo_id, repo_type="dataset", local_dir=local_dir, revision=branch, allow_patterns=video_files
     )
     videos_info_dict = {}
     for vid_key, vid_path in zip(video_keys, video_files, strict=True):
@@ -474,11 +451,7 @@ def convert_dataset(
 
     hub_api = HfApi()
     hub_api.snapshot_download(
-        repo_id=repo_id,
-        repo_type="dataset",
-        revision=v1,
-        local_dir=v1x_dir,
-        ignore_patterns="videos*/",
+        repo_id=repo_id, repo_type="dataset", revision=v1, local_dir=v1x_dir, ignore_patterns="videos*/"
     )
     branch = "main"
     if test_branch:
@@ -536,21 +509,12 @@ def convert_dataset(
         dataset = dataset.remove_columns(video_keys)
         clean_gitattr = Path(
             hub_api.hf_hub_download(
-                repo_id=GITATTRIBUTES_REF,
-                repo_type="dataset",
-                local_dir=local_dir,
-                filename=".gitattributes",
+                repo_id=GITATTRIBUTES_REF, repo_type="dataset", local_dir=local_dir, filename=".gitattributes"
             )
         ).absolute()
         with tempfile.TemporaryDirectory() as tmp_video_dir:
             move_videos(
-                repo_id,
-                video_keys,
-                total_episodes,
-                total_chunks,
-                Path(tmp_video_dir),
-                clean_gitattr,
-                branch,
+                repo_id, video_keys, total_episodes, total_chunks, Path(tmp_video_dir), clean_gitattr, branch
             )
         videos_info = get_videos_info(repo_id, v1x_dir, video_keys=video_keys, branch=branch)
         for key in video_keys:
@@ -579,11 +543,7 @@ def convert_dataset(
 
     # Episodes
     episodes = [
-        {
-            "episode_index": ep_idx,
-            "tasks": tasks_by_episodes[ep_idx],
-            "length": episode_lengths[ep_idx],
-        }
+        {"episode_index": ep_idx, "tasks": tasks_by_episodes[ep_idx], "length": episode_lengths[ep_idx]}
         for ep_idx in episode_indices
     ]
     write_jsonlines(episodes, v20_dir / EPISODES_PATH)
@@ -612,12 +572,7 @@ def convert_dataset(
         hub_api.delete_folder(repo_id=repo_id, path_in_repo="data", repo_type="dataset", revision=branch)
 
     with contextlib.suppress(EntryNotFoundError, HfHubHTTPError):
-        hub_api.delete_folder(
-            repo_id=repo_id,
-            path_in_repo="meta_data",
-            repo_type="dataset",
-            revision=branch,
-        )
+        hub_api.delete_folder(repo_id=repo_id, path_in_repo="meta_data", repo_type="dataset", revision=branch)
 
     with contextlib.suppress(EntryNotFoundError, HfHubHTTPError):
         hub_api.delete_folder(repo_id=repo_id, path_in_repo="meta", repo_type="dataset", revision=branch)

lerobot/common/datasets/v21/convert_dataset_v20_to_v21.py

@@ -37,16 +37,8 @@ import logging
 from huggingface_hub import HfApi
 
 from lerobot.common.datasets.lerobot_dataset import CODEBASE_VERSION, LeRobotDataset
-from lerobot.common.datasets.utils import (
-    EPISODES_STATS_PATH,
-    STATS_PATH,
-    load_stats,
-    write_info,
-)
-from lerobot.common.datasets.v21.convert_stats import (
-    check_aggregate_stats,
-    convert_stats,
-)
+from lerobot.common.datasets.utils import EPISODES_STATS_PATH, STATS_PATH, load_stats, write_info
+from lerobot.common.datasets.v21.convert_stats import check_aggregate_stats, convert_stats
 
 V20 = "v2.0"
 V21 = "v2.1"
@@ -87,16 +79,10 @@ def convert_dataset(
 
     hub_api = HfApi()
     if hub_api.file_exists(
-        repo_id=dataset.repo_id,
-        filename=STATS_PATH,
-        revision=branch,
-        repo_type="dataset",
+        repo_id=dataset.repo_id, filename=STATS_PATH, revision=branch, repo_type="dataset"
     ):
         hub_api.delete_file(
-            path_in_repo=STATS_PATH,
-            repo_id=dataset.repo_id,
-            revision=branch,
-            repo_type="dataset",
+            path_in_repo=STATS_PATH, repo_id=dataset.repo_id, revision=branch, repo_type="dataset"
         )
 
     hub_api.create_tag(repo_id, tag=CODEBASE_VERSION, revision=branch, repo_type="dataset")

lerobot/common/datasets/v21/convert_stats.py

@@ -17,11 +17,7 @@ from concurrent.futures import ThreadPoolExecutor, as_completed
 import numpy as np
 from tqdm import tqdm
 
-from lerobot.common.datasets.compute_stats import (
-    aggregate_stats,
-    get_feature_stats,
-    sample_indices,
-)
+from lerobot.common.datasets.compute_stats import aggregate_stats, get_feature_stats, sample_indices
 from lerobot.common.datasets.lerobot_dataset import LeRobotDataset
 from lerobot.common.datasets.utils import write_episode_stats
 
@@ -99,9 +95,5 @@ def check_aggregate_stats(
             if key in reference_stats and stat in reference_stats[key]:
                 err_msg = f"feature='{key}' stats='{stat}'"
                 np.testing.assert_allclose(
-                    val,
-                    reference_stats[key][stat],
-                    rtol=rtol,
-                    atol=atol,
-                    err_msg=err_msg,
+                    val, reference_stats[key][stat], rtol=rtol, atol=atol, err_msg=err_msg
                 )

lerobot/common/optim/schedulers.py

@@ -49,11 +49,7 @@ class DiffuserSchedulerConfig(LRSchedulerConfig):
     def build(self, optimizer: Optimizer, num_training_steps: int) -> LambdaLR:
         from diffusers.optimization import get_scheduler
 
-        kwargs = {
-            **asdict(self),
-            "num_training_steps": num_training_steps,
-            "optimizer": optimizer,
-        }
+        kwargs = {**asdict(self), "num_training_steps": num_training_steps, "optimizer": optimizer}
         return get_scheduler(**kwargs)
 
 
@@ -75,10 +71,7 @@ class VQBeTSchedulerConfig(LRSchedulerConfig):
                 progress = float(adjusted_step - self.num_warmup_steps) / float(
                     max(1, num_training_steps - self.num_warmup_steps)
                 )
-                return max(
-                    0.0,
-                    0.5 * (1.0 + math.cos(math.pi * float(self.num_cycles) * 2.0 * progress)),
-                )
+                return max(0.0, 0.5 * (1.0 + math.cos(math.pi * float(self.num_cycles) * 2.0 * progress)))
 
         return LambdaLR(optimizer, lr_lambda, -1)
 

lerobot/common/policies/act/modeling_act.py

@@ -241,9 +241,7 @@ class ACTTemporalEnsembler:
             # Note: The last dimension is unsqueeze to make sure we can broadcast properly for tensor
             # operations later.
             self.ensembled_actions_count = torch.ones(
-                (self.chunk_size, 1),
-                dtype=torch.long,
-                device=self.ensembled_actions.device,
+                (self.chunk_size, 1), dtype=torch.long, device=self.ensembled_actions.device
             )
         else:
             # self.ensembled_actions will have shape (batch_size, chunk_size - 1, action_dim). Compute
@@ -255,10 +253,7 @@ class ACTTemporalEnsembler:
             # The last action, which has no prior online average, needs to get concatenated onto the end.
             self.ensembled_actions = torch.cat([self.ensembled_actions, actions[:, -1:]], dim=1)
             self.ensembled_actions_count = torch.cat(
-                [
-                    self.ensembled_actions_count,
-                    torch.ones_like(self.ensembled_actions_count[-1:]),
-                ]
+                [self.ensembled_actions_count, torch.ones_like(self.ensembled_actions_count[-1:])]
             )
         # "Consume" the first action.
         action, self.ensembled_actions, self.ensembled_actions_count = (
@@ -338,11 +333,7 @@ class ACT(nn.Module):
         # Backbone for image feature extraction.
         if self.config.image_features:
             backbone_model = getattr(torchvision.models, config.vision_backbone)(
-                replace_stride_with_dilation=[
-                    False,
-                    False,
-                    config.replace_final_stride_with_dilation,
-                ],
+                replace_stride_with_dilation=[False, False, config.replace_final_stride_with_dilation],
                 weights=config.pretrained_backbone_weights,
                 norm_layer=FrozenBatchNorm2d,
             )
@@ -436,11 +427,7 @@ class ACT(nn.Module):
             action_embed = self.vae_encoder_action_input_proj(batch["action"])  # (B, S, D)
 
             if self.config.robot_state_feature:
-                vae_encoder_input = [
-                    cls_embed,
-                    robot_state_embed,
-                    action_embed,
-                ]  # (B, S+2, D)
+                vae_encoder_input = [cls_embed, robot_state_embed, action_embed]  # (B, S+2, D)
             else:
                 vae_encoder_input = [cls_embed, action_embed]
             vae_encoder_input = torch.cat(vae_encoder_input, axis=1)
@@ -553,10 +540,7 @@ class ACTEncoder(nn.Module):
         self.norm = nn.LayerNorm(config.dim_model) if config.pre_norm else nn.Identity()
 
     def forward(
-        self,
-        x: Tensor,
-        pos_embed: Tensor | None = None,
-        key_padding_mask: Tensor | None = None,
+        self, x: Tensor, pos_embed: Tensor | None = None, key_padding_mask: Tensor | None = None
     ) -> Tensor:
         for layer in self.layers:
             x = layer(x, pos_embed=pos_embed, key_padding_mask=key_padding_mask)
@@ -619,10 +603,7 @@ class ACTDecoder(nn.Module):
     ) -> Tensor:
         for layer in self.layers:
             x = layer(
-                x,
-                encoder_out,
-                decoder_pos_embed=decoder_pos_embed,
-                encoder_pos_embed=encoder_pos_embed,
+                x, encoder_out, decoder_pos_embed=decoder_pos_embed, encoder_pos_embed=encoder_pos_embed
             )
         if self.norm is not None:
             x = self.norm(x)

lerobot/common/policies/diffusion/modeling_diffusion.py

@@ -209,10 +209,7 @@ class DiffusionModel(nn.Module):
 
     # ========= inference  ============
     def conditional_sample(
-        self,
-        batch_size: int,
-        global_cond: Tensor | None = None,
-        generator: torch.Generator | None = None,
+        self, batch_size: int, global_cond: Tensor | None = None, generator: torch.Generator | None = None
     ) -> Tensor:
         device = get_device_from_parameters(self)
         dtype = get_dtype_from_parameters(self)
@@ -257,10 +254,7 @@ class DiffusionModel(nn.Module):
                 # Separate batch and sequence dims back out. The camera index dim gets absorbed into the
                 # feature dim (effectively concatenating the camera features).
                 img_features = einops.rearrange(
-                    img_features_list,
-                    "(n b s) ... -> b s (n ...)",
-                    b=batch_size,
-                    s=n_obs_steps,
+                    img_features_list, "(n b s) ... -> b s (n ...)", b=batch_size, s=n_obs_steps
                 )
             else:
                 # Combine batch, sequence, and "which camera" dims before passing to shared encoder.
@@ -270,10 +264,7 @@ class DiffusionModel(nn.Module):
                 # Separate batch dim and sequence dim back out. The camera index dim gets absorbed into the
                 # feature dim (effectively concatenating the camera features).
                 img_features = einops.rearrange(
-                    img_features,
-                    "(b s n) ... -> b s (n ...)",
-                    b=batch_size,
-                    s=n_obs_steps,
+                    img_features, "(b s n) ... -> b s (n ...)", b=batch_size, s=n_obs_steps
                 )
             global_cond_feats.append(img_features)
 
@@ -524,9 +515,7 @@ class DiffusionRgbEncoder(nn.Module):
 
 
 def _replace_submodules(
-    root_module: nn.Module,
-    predicate: Callable[[nn.Module], bool],
-    func: Callable[[nn.Module], nn.Module],
+    root_module: nn.Module, predicate: Callable[[nn.Module], bool], func: Callable[[nn.Module], nn.Module]
 ) -> nn.Module:
     """
     Args:
@@ -644,14 +633,10 @@ class DiffusionConditionalUnet1d(nn.Module):
         self.mid_modules = nn.ModuleList(
             [
                 DiffusionConditionalResidualBlock1d(
-                    config.down_dims[-1],
-                    config.down_dims[-1],
-                    **common_res_block_kwargs,
+                    config.down_dims[-1], config.down_dims[-1], **common_res_block_kwargs
                 ),
                 DiffusionConditionalResidualBlock1d(
-                    config.down_dims[-1],
-                    config.down_dims[-1],
-                    **common_res_block_kwargs,
+                    config.down_dims[-1], config.down_dims[-1], **common_res_block_kwargs
                 ),
             ]
         )

lerobot/common/policies/pi0/conversion_scripts/convert_pi0_to_hf_lerobot.py

@@ -61,11 +61,7 @@ from lerobot.common.policies.pi0.conversion_scripts.conversion_utils import (
 )
 from lerobot.common.policies.pi0.modeling_pi0 import PI0Policy
 
-PRECISIONS = {
-    "bfloat16": torch.bfloat16,
-    "float32": torch.float32,
-    "float16": torch.float16,
-}
+PRECISIONS = {"bfloat16": torch.bfloat16, "float32": torch.float32, "float16": torch.float16}
 
 
 def slice_paligemma_state_dict(state_dict, config):

lerobot/common/policies/pi0/flex_attention.py

@@ -48,32 +48,18 @@ def flex_attention_forward(
 
     key_states = key_states[:, :, :, None, :]
     key_states = key_states.expand(
-        batch_size,
-        key_states.shape[1],
-        num_key_value_heads,
-        num_key_value_groups,
-        head_dim,
+        batch_size, key_states.shape[1], num_key_value_heads, num_key_value_groups, head_dim
     )
     key_states = key_states.reshape(
-        batch_size,
-        key_states.shape[1],
-        num_key_value_heads * num_key_value_groups,
-        head_dim,
+        batch_size, key_states.shape[1], num_key_value_heads * num_key_value_groups, head_dim
     )
 
     value_states = value_states[:, :, :, None, :]
     value_states = value_states.expand(
-        batch_size,
-        value_states.shape[1],
-        num_key_value_heads,
-        num_key_value_groups,
-        head_dim,
+        batch_size, value_states.shape[1], num_key_value_heads, num_key_value_groups, head_dim
     )
     value_states = value_states.reshape(
-        batch_size,
-        value_states.shape[1],
-        num_key_value_heads * num_key_value_groups,
-        head_dim,
+        batch_size, value_states.shape[1], num_key_value_heads * num_key_value_groups, head_dim
     )
 
     query_states = query_states.transpose(1, 2)

lerobot/common/policies/pi0/modeling_pi0.py

@@ -69,11 +69,7 @@ from lerobot.common.utils.utils import get_safe_dtype
 
 
 def create_sinusoidal_pos_embedding(
-    time: torch.tensor,
-    dimension: int,
-    min_period: float,
-    max_period: float,
-    device="cpu",
+    time: torch.tensor, dimension: int, min_period: float, max_period: float, device="cpu"
 ) -> Tensor:
     """Computes sine-cosine positional embedding vectors for scalar positions."""
     if dimension % 2 != 0:
@@ -581,11 +577,7 @@ class PI0FlowMatching(nn.Module):
 
         # Embed timestep using sine-cosine positional encoding with sensitivity in the range [0, 1]
         time_emb = create_sinusoidal_pos_embedding(
-            timestep,
-            self.config.proj_width,
-            min_period=4e-3,
-            max_period=4.0,
-            device=device,
+            timestep, self.config.proj_width, min_period=4e-3, max_period=4.0, device=device
         )
         time_emb = time_emb.type(dtype=dtype)
 
@@ -617,15 +609,7 @@ class PI0FlowMatching(nn.Module):
         return embs, pad_masks, att_masks
 
     def forward(
-        self,
-        images,
-        img_masks,
-        lang_tokens,
-        lang_masks,
-        state,
-        actions,
-        noise=None,
-        time=None,
+        self, images, img_masks, lang_tokens, lang_masks, state, actions, noise=None, time=None
     ) -> Tensor:
         """Do a full training forward pass and compute the loss (batch_size x num_steps x num_motors)"""
         if noise is None:
@@ -671,11 +655,7 @@ class PI0FlowMatching(nn.Module):
         device = state.device
 
         if noise is None:
-            actions_shape = (
-                bsize,
-                self.config.n_action_steps,
-                self.config.max_action_dim,
-            )
+            actions_shape = (bsize, self.config.n_action_steps, self.config.max_action_dim)
             noise = self.sample_noise(actions_shape, device)
 
         prefix_embs, prefix_pad_masks, prefix_att_masks = self.embed_prefix(

lerobot/common/policies/pi0/paligemma_with_expert.py

@@ -293,18 +293,12 @@ class PaliGemmaWithExpertModel(PreTrainedModel):
                     # in `transformers`. (molbap)
                     key_states = torch.cat([past_key_values[layer_idx]["key_states"], key_states], dim=1)
                     value_states = torch.cat(
-                        [past_key_values[layer_idx]["value_states"], value_states],
-                        dim=1,
+                        [past_key_values[layer_idx]["value_states"], value_states], dim=1
                     )
 
             attention_interface = self.get_attention_interface()
             att_output = attention_interface(
-                attention_mask,
-                batch_size,
-                head_dim,
-                query_states,
-                key_states,
-                value_states,
+                attention_mask, batch_size, head_dim, query_states, key_states, value_states
             )
             att_output = att_output.to(dtype=torch.bfloat16)
 
@@ -364,24 +358,12 @@ class PaliGemmaWithExpertModel(PreTrainedModel):
         return attention_interface
 
     def flash_attention_forward(
-        self,
-        attention_mask,
-        batch_size,
-        head_dim,
-        query_states,
-        key_states,
-        value_states,
+        self, attention_mask, batch_size, head_dim, query_states, key_states, value_states
     ):
         raise NotImplementedError("FA2 is not implemented (yet)")
 
     def eager_attention_forward(
-        self,
-        attention_mask,
-        batch_size,
-        head_dim,
-        query_states,
-        key_states,
-        value_states,
+        self, attention_mask, batch_size, head_dim, query_states, key_states, value_states
     ):
         num_att_heads = self.config.paligemma_config.text_config.num_attention_heads
         num_key_value_heads = self.config.paligemma_config.text_config.num_key_value_heads
@@ -393,31 +375,17 @@ class PaliGemmaWithExpertModel(PreTrainedModel):
         sequence_length = key_states.shape[1]
 
         key_states = key_states[:, :, :, None, :].expand(
-            batch_size,
-            sequence_length,
-            num_key_value_heads,
-            num_key_value_groups,
-            head_dim,
+            batch_size, sequence_length, num_key_value_heads, num_key_value_groups, head_dim
         )
         key_states = key_states.reshape(
-            batch_size,
-            sequence_length,
-            num_key_value_heads * num_key_value_groups,
-            head_dim,
+            batch_size, sequence_length, num_key_value_heads * num_key_value_groups, head_dim
         )
 
         value_states = value_states[:, :, :, None, :].expand(
-            batch_size,
-            sequence_length,
-            num_key_value_heads,
-            num_key_value_groups,
-            head_dim,
+            batch_size, sequence_length, num_key_value_heads, num_key_value_groups, head_dim
         )
         value_states = value_states.reshape(
-            batch_size,
-            sequence_length,
-            num_key_value_heads * num_key_value_groups,
-            head_dim,
+            batch_size, sequence_length, num_key_value_heads * num_key_value_groups, head_dim
         )
 
         # Attention here is upcasted to float32 to match the original eager implementation.

lerobot/common/policies/tdmpc/modeling_tdmpc.py

@@ -39,11 +39,7 @@ from lerobot.common.constants import OBS_ENV, OBS_ROBOT
 from lerobot.common.policies.normalize import Normalize, Unnormalize
 from lerobot.common.policies.pretrained import PreTrainedPolicy
 from lerobot.common.policies.tdmpc.configuration_tdmpc import TDMPCConfig
-from lerobot.common.policies.utils import (
-    get_device_from_parameters,
-    get_output_shape,
-    populate_queues,
-)
+from lerobot.common.policies.utils import get_device_from_parameters, get_output_shape, populate_queues
 
 
 class TDMPCPolicy(PreTrainedPolicy):
@@ -67,11 +63,7 @@ class TDMPCPolicy(PreTrainedPolicy):
     config_class = TDMPCConfig
     name = "tdmpc"
 
-    def __init__(
-        self,
-        config: TDMPCConfig,
-        dataset_stats: dict[str, dict[str, Tensor]] | None = None,
-    ):
+    def __init__(self, config: TDMPCConfig, dataset_stats: dict[str, dict[str, Tensor]] | None = None):
         """
         Args:
             config: Policy configuration class instance or None, in which case the default instantiation of
@@ -197,20 +189,13 @@ class TDMPCPolicy(PreTrainedPolicy):
 
         # In the CEM loop we will need this for a call to estimate_value with the gaussian sampled
         # trajectories.
-        z = einops.repeat(
-            z,
-            "b d -> n b d",
-            n=self.config.n_gaussian_samples + self.config.n_pi_samples,
-        )
+        z = einops.repeat(z, "b d -> n b d", n=self.config.n_gaussian_samples + self.config.n_pi_samples)
 
         # Model Predictive Path Integral (MPPI) with the cross-entropy method (CEM) as the optimization
         # algorithm.
         # The initial mean and standard deviation for the cross-entropy method (CEM).
         mean = torch.zeros(
-            self.config.horizon,
-            batch_size,
-            self.config.action_feature.shape[0],
-            device=device,
+            self.config.horizon, batch_size, self.config.action_feature.shape[0], device=device
         )
         # Maybe warm start CEM with the mean from the previous step.
         if self._prev_mean is not None:
@@ -306,10 +291,9 @@ class TDMPCPolicy(PreTrainedPolicy):
         if self.config.q_ensemble_size > 2:
             G += (
                 running_discount
-                * torch.min(
-                    terminal_values[torch.randint(0, self.config.q_ensemble_size, size=(2,))],
-                    dim=0,
-                )[0]
+                * torch.min(terminal_values[torch.randint(0, self.config.q_ensemble_size, size=(2,))], dim=0)[
+                    0
+                ]
             )
         else:
             G += running_discount * torch.min(terminal_values, dim=0)[0]
@@ -345,10 +329,7 @@ class TDMPCPolicy(PreTrainedPolicy):
         # Apply random image augmentations.
         if self.config.image_features and self.config.max_random_shift_ratio > 0:
             observations["observation.image"] = flatten_forward_unflatten(
-                partial(
-                    random_shifts_aug,
-                    max_random_shift_ratio=self.config.max_random_shift_ratio,
-                ),
+                partial(random_shifts_aug, max_random_shift_ratio=self.config.max_random_shift_ratio),
                 observations["observation.image"],
             )
 
@@ -572,10 +553,7 @@ class TDMPCTOLD(nn.Module):
         self._Qs = nn.ModuleList(
             [
                 nn.Sequential(
-                    nn.Linear(
-                        config.latent_dim + config.action_feature.shape[0],
-                        config.mlp_dim,
-                    ),
+                    nn.Linear(config.latent_dim + config.action_feature.shape[0], config.mlp_dim),
                     nn.LayerNorm(config.mlp_dim),
                     nn.Tanh(),
                     nn.Linear(config.mlp_dim, config.mlp_dim),
@@ -724,26 +702,11 @@ class TDMPCObservationEncoder(nn.Module):
                     stride=2,
                 ),
                 nn.ReLU(),
-                nn.Conv2d(
-                    config.image_encoder_hidden_dim,
-                    config.image_encoder_hidden_dim,
-                    5,
-                    stride=2,
-                ),
+                nn.Conv2d(config.image_encoder_hidden_dim, config.image_encoder_hidden_dim, 5, stride=2),
                 nn.ReLU(),
-                nn.Conv2d(
-                    config.image_encoder_hidden_dim,
-                    config.image_encoder_hidden_dim,
-                    3,
-                    stride=2,
-                ),
+                nn.Conv2d(config.image_encoder_hidden_dim, config.image_encoder_hidden_dim, 3, stride=2),
                 nn.ReLU(),
-                nn.Conv2d(
-                    config.image_encoder_hidden_dim,
-                    config.image_encoder_hidden_dim,
-                    3,
-                    stride=2,
-                ),
+                nn.Conv2d(config.image_encoder_hidden_dim, config.image_encoder_hidden_dim, 3, stride=2),
                 nn.ReLU(),
             )
             dummy_shape = (1, *next(iter(config.image_features.values())).shape)
@@ -786,8 +749,7 @@ class TDMPCObservationEncoder(nn.Module):
         if self.config.image_features:
             feat.append(
                 flatten_forward_unflatten(
-                    self.image_enc_layers,
-                    obs_dict[next(iter(self.config.image_features))],
+                    self.image_enc_layers, obs_dict[next(iter(self.config.image_features))]
                 )
             )
         if self.config.env_state_feature:
@@ -834,9 +796,7 @@ def update_ema_parameters(ema_net: nn.Module, net: nn.Module, alpha: float):
     """Update EMA parameters in place with ema_param <- alpha * ema_param + (1 - alpha) * param."""
     for ema_module, module in zip(ema_net.modules(), net.modules(), strict=True):
         for (n_p_ema, p_ema), (n_p, p) in zip(
-            ema_module.named_parameters(recurse=False),
-            module.named_parameters(recurse=False),
-            strict=True,
+            ema_module.named_parameters(recurse=False), module.named_parameters(recurse=False), strict=True
         ):
             assert n_p_ema == n_p, "Parameter names don't match for EMA model update"
             if isinstance(p, dict):

lerobot/common/policies/vqbet/configuration_vqbet.py

@@ -193,12 +193,7 @@ class VQBeTConfig(PreTrainedConfig):
 
     @property
     def action_delta_indices(self) -> list:
-        return list(
-            range(
-                1 - self.n_obs_steps,
-                self.n_action_pred_token + self.action_chunk_size - 1,
-            )
-        )
+        return list(range(1 - self.n_obs_steps, self.n_action_pred_token + self.action_chunk_size - 1))
 
     @property
     def reward_delta_indices(self) -> None:

lerobot/common/policies/vqbet/modeling_vqbet.py

@@ -29,11 +29,7 @@ from torch import Tensor, nn
 
 from lerobot.common.policies.normalize import Normalize, Unnormalize
 from lerobot.common.policies.pretrained import PreTrainedPolicy
-from lerobot.common.policies.utils import (
-    get_device_from_parameters,
-    get_output_shape,
-    populate_queues,
-)
+from lerobot.common.policies.utils import get_device_from_parameters, get_output_shape, populate_queues
 from lerobot.common.policies.vqbet.configuration_vqbet import VQBeTConfig
 from lerobot.common.policies.vqbet.vqbet_utils import GPT, ResidualVQ
 
@@ -328,8 +324,7 @@ class VQBeTModel(nn.Module):
 
         # To input state and observation features into GPT layers, we first project the features to fit the shape of input size of GPT.
         self.state_projector = MLP(
-            config.robot_state_feature.shape[0],
-            hidden_channels=[self.config.gpt_input_dim],
+            config.robot_state_feature.shape[0], hidden_channels=[self.config.gpt_input_dim]
         )
         self.rgb_feature_projector = MLP(
             self.rgb_encoder.feature_dim, hidden_channels=[self.config.gpt_input_dim]
@@ -359,11 +354,7 @@ class VQBeTModel(nn.Module):
         )
         # Separate batch and sequence dims.
         img_features = einops.rearrange(
-            img_features,
-            "(b s n) ... -> b s n ...",
-            b=batch_size,
-            s=n_obs_steps,
-            n=self.num_images,
+            img_features, "(b s n) ... -> b s n ...", b=batch_size, s=n_obs_steps, n=self.num_images
         )
 
         # Arrange prior and current observation step tokens as shown in the class docstring.
@@ -400,11 +391,7 @@ class VQBeTModel(nn.Module):
         # Thus, it predicts a historical action sequence, in addition to current and future actions (predicting future actions : optional).
         if len_additional_action_token > 0:
             features = torch.cat(
-                [
-                    features[:, historical_act_pred_index],
-                    features[:, -len_additional_action_token:],
-                ],
-                dim=1,
+                [features[:, historical_act_pred_index], features[:, -len_additional_action_token:]], dim=1
             )
         else:
             features = features[:, historical_act_pred_index]
@@ -527,13 +514,7 @@ class VQBeTHead(nn.Module):
 
             cbet_secondary_logits = self.map_to_cbet_preds_secondary_bin(
                 torch.cat(
-                    (
-                        x,
-                        F.one_hot(
-                            sampled_primary_centers,
-                            num_classes=self.config.vqvae_n_embed,
-                        ),
-                    ),
+                    (x, F.one_hot(sampled_primary_centers, num_classes=self.config.vqvae_n_embed)),
                     axis=1,
                 )
             )
@@ -551,9 +532,7 @@ class VQBeTHead(nn.Module):
         else:
             cbet_logits = self.map_to_cbet_preds_bin(x)
             cbet_logits = einops.rearrange(
-                cbet_logits,
-                "(NT) (G C) -> (NT) G C",
-                G=self.vqvae_model.vqvae_num_layers,
+                cbet_logits, "(NT) (G C) -> (NT) G C", G=self.vqvae_model.vqvae_num_layers
             )
             cbet_probs = torch.softmax(cbet_logits / self.config.bet_softmax_temperature, dim=-1)
             NT, G, choices = cbet_probs.shape
@@ -751,9 +730,7 @@ class VQBeTRgbEncoder(nn.Module):
 
 
 def _replace_submodules(
-    root_module: nn.Module,
-    predicate: Callable[[nn.Module], bool],
-    func: Callable[[nn.Module], nn.Module],
+    root_module: nn.Module, predicate: Callable[[nn.Module], bool], func: Callable[[nn.Module], nn.Module]
 ) -> nn.Module:
     """
     Args:

lerobot/common/policies/vqbet/vqbet_utils.py

@@ -377,10 +377,7 @@ class ResidualVQ(nn.Module):
         self.layers = nn.ModuleList(
             [
                 VectorQuantize(
-                    dim=codebook_dim,
-                    codebook_dim=codebook_dim,
-                    accept_image_fmap=accept_image_fmap,
-                    **kwargs,
+                    dim=codebook_dim, codebook_dim=codebook_dim, accept_image_fmap=accept_image_fmap, **kwargs
                 )
                 for _ in range(num_quantizers)
             ]

lerobot/common/robot_devices/cameras/intelrealsense.py

@@ -297,11 +297,7 @@ class IntelRealSenseCamera:
         if self.fps and self.capture_width and self.capture_height:
             # TODO(rcadene): can we set rgb8 directly?
             config.enable_stream(
-                rs.stream.color,
-                self.capture_width,
-                self.capture_height,
-                rs.format.rgb8,
-                self.fps,
+                rs.stream.color, self.capture_width, self.capture_height, rs.format.rgb8, self.fps
             )
         else:
             config.enable_stream(rs.stream.color)
@@ -309,11 +305,7 @@ class IntelRealSenseCamera:
         if self.use_depth:
             if self.fps and self.capture_width and self.capture_height:
                 config.enable_stream(
-                    rs.stream.depth,
-                    self.capture_width,
-                    self.capture_height,
-                    rs.format.z16,
-                    self.fps,
+                    rs.stream.depth, self.capture_width, self.capture_height, rs.format.z16, self.fps
                 )
             else:
                 config.enable_stream(rs.stream.depth)

lerobot/common/robot_devices/cameras/utils.py

@@ -41,9 +41,7 @@ def make_cameras_from_configs(camera_configs: dict[str, CameraConfig]) -> list[C
             cameras[key] = OpenCVCamera(cfg)
 
         elif cfg.type == "intelrealsense":
-            from lerobot.common.robot_devices.cameras.intelrealsense import (
-                IntelRealSenseCamera,
-            )
+            from lerobot.common.robot_devices.cameras.intelrealsense import IntelRealSenseCamera
 
             cameras[key] = IntelRealSenseCamera(cfg)
         else:
@@ -60,9 +58,7 @@ def make_camera(camera_type, **kwargs) -> Camera:
         return OpenCVCamera(config)
 
     elif camera_type == "intelrealsense":
-        from lerobot.common.robot_devices.cameras.intelrealsense import (
-            IntelRealSenseCamera,
-        )
+        from lerobot.common.robot_devices.cameras.intelrealsense import IntelRealSenseCamera
 
         config = IntelRealSenseCameraConfig(**kwargs)
         return IntelRealSenseCamera(config)

lerobot/common/robot_devices/motors/dynamixel.py

@@ -23,10 +23,7 @@ import numpy as np
 import tqdm
 
 from lerobot.common.robot_devices.motors.configs import DynamixelMotorsBusConfig
-from lerobot.common.robot_devices.utils import (
-    RobotDeviceAlreadyConnectedError,
-    RobotDeviceNotConnectedError,
-)
+from lerobot.common.robot_devices.utils import RobotDeviceAlreadyConnectedError, RobotDeviceNotConnectedError
 from lerobot.common.utils.utils import capture_timestamp_utc
 
 PROTOCOL_VERSION = 2.0
@@ -787,12 +784,7 @@ class DynamixelMotorsBus:
                 f"{self.packet_handler.getTxRxResult(comm)}"
             )
 
-    def write(
-        self,
-        data_name,
-        values: int | float | np.ndarray,
-        motor_names: str | list[str] | None = None,
-    ):
+    def write(self, data_name, values: int | float | np.ndarray, motor_names: str | list[str] | None = None):
         if not self.is_connected:
             raise RobotDeviceNotConnectedError(
                 f"DynamixelMotorsBus({self.port}) is not connected. You need to run `motors_bus.connect()`."

lerobot/common/robot_devices/motors/feetech.py

@@ -23,10 +23,7 @@ import numpy as np
 import tqdm
 
 from lerobot.common.robot_devices.motors.configs import FeetechMotorsBusConfig
-from lerobot.common.robot_devices.utils import (
-    RobotDeviceAlreadyConnectedError,
-    RobotDeviceNotConnectedError,
-)
+from lerobot.common.robot_devices.utils import RobotDeviceAlreadyConnectedError, RobotDeviceNotConnectedError
 from lerobot.common.utils.utils import capture_timestamp_utc
 
 PROTOCOL_VERSION = 0
@@ -812,12 +809,7 @@ class FeetechMotorsBus:
                 f"{self.packet_handler.getTxRxResult(comm)}"
             )
 
-    def write(
-        self,
-        data_name,
-        values: int | float | np.ndarray,
-        motor_names: str | list[str] | None = None,
-    ):
+    def write(self, data_name, values: int | float | np.ndarray, motor_names: str | list[str] | None = None):
         if not self.is_connected:
             raise RobotDeviceNotConnectedError(
                 f"FeetechMotorsBus({self.port}) is not connected. You need to run `motors_bus.connect()`."

lerobot/common/robot_devices/motors/utils.py

@@ -30,9 +30,7 @@ class MotorsBus(Protocol):
     def write(self): ...
 
 
-def make_motors_buses_from_configs(
-    motors_bus_configs: dict[str, MotorsBusConfig],
-) -> list[MotorsBus]:
+def make_motors_buses_from_configs(motors_bus_configs: dict[str, MotorsBusConfig]) -> list[MotorsBus]:
     motors_buses = {}
 
     for key, cfg in motors_bus_configs.items():

lerobot/common/robot_devices/robots/feetech_calibration.py

@@ -207,10 +207,7 @@ def run_arm_auto_calibration_so100(arm: MotorsBus, robot_type: str, arm_name: st
 
     print("Calibrate elbow_flex")
     calib["elbow_flex"] = move_to_calibrate(
-        arm,
-        "elbow_flex",
-        positive_first=False,
-        in_between_move_hook=in_between_move_hook,
+        arm, "elbow_flex", positive_first=False, in_between_move_hook=in_between_move_hook
     )
     calib["elbow_flex"] = apply_offset(calib["elbow_flex"], offset=80 - 1024)
 
@@ -242,11 +239,7 @@ def run_arm_auto_calibration_so100(arm: MotorsBus, robot_type: str, arm_name: st
         }
         arm.write("Goal_Position", list(positions.values()), list(positions.keys()))
 
-    arm.write(
-        "Goal_Position",
-        round(calib["shoulder_lift"]["zero_pos"] - 1600),
-        "shoulder_lift",
-    )
+    arm.write("Goal_Position", round(calib["shoulder_lift"]["zero_pos"] - 1600), "shoulder_lift")
     time.sleep(2)
     arm.write("Goal_Position", round(calib["elbow_flex"]["zero_pos"] + 1700), "elbow_flex")
     time.sleep(2)
@@ -257,11 +250,7 @@ def run_arm_auto_calibration_so100(arm: MotorsBus, robot_type: str, arm_name: st
 
     print("Calibrate wrist_roll")
     calib["wrist_roll"] = move_to_calibrate(
-        arm,
-        "wrist_roll",
-        invert_drive_mode=True,
-        positive_first=False,
-        while_move_hook=while_move_hook,
+        arm, "wrist_roll", invert_drive_mode=True, positive_first=False, while_move_hook=while_move_hook
     )
 
     arm.write("Goal_Position", calib["wrist_roll"]["zero_pos"], "wrist_roll")

lerobot/common/robot_devices/robots/lekiwi_remote.py

@@ -61,9 +61,7 @@ def calibrate_follower_arm(motors_bus, calib_dir_str):
     calib_dir.mkdir(parents=True, exist_ok=True)
     calib_file = calib_dir / "main_follower.json"
     try:
-        from lerobot.common.robot_devices.robots.feetech_calibration import (
-            run_arm_manual_calibration,
-        )
+        from lerobot.common.robot_devices.robots.feetech_calibration import run_arm_manual_calibration
     except ImportError:
         print("[WARNING] Calibration function not available. Skipping calibration.")
         return
@@ -118,14 +116,7 @@ def run_lekiwi(robot_config):
     robot = LeKiwi(motors_bus)
 
     # Define the expected arm motor IDs.
-    arm_motor_ids = [
-        "shoulder_pan",
-        "shoulder_lift",
-        "elbow_flex",
-        "wrist_flex",
-        "wrist_roll",
-        "gripper",
-    ]
+    arm_motor_ids = ["shoulder_pan", "shoulder_lift", "elbow_flex", "wrist_flex", "wrist_roll", "gripper"]
 
     # Disable torque for each arm motor.
     for motor in arm_motor_ids:
@@ -139,9 +130,7 @@ def run_lekiwi(robot_config):
     images_lock = threading.Lock()
     stop_event = threading.Event()
     cam_thread = threading.Thread(
-        target=run_camera_capture,
-        args=(cameras, images_lock, latest_images_dict, stop_event),
-        daemon=True,
+        target=run_camera_capture, args=(cameras, images_lock, latest_images_dict, stop_event), daemon=True
     )
     cam_thread.start()
 

lerobot/common/robot_devices/robots/mobile_manipulator.py

@@ -25,14 +25,9 @@ import zmq
 
 from lerobot.common.robot_devices.cameras.utils import make_cameras_from_configs
 from lerobot.common.robot_devices.motors.feetech import TorqueMode
-from lerobot.common.robot_devices.motors.utils import (
-    MotorsBus,
-    make_motors_buses_from_configs,
-)
+from lerobot.common.robot_devices.motors.utils import MotorsBus, make_motors_buses_from_configs
 from lerobot.common.robot_devices.robots.configs import LeKiwiRobotConfig
-from lerobot.common.robot_devices.robots.feetech_calibration import (
-    run_arm_manual_calibration,
-)
+from lerobot.common.robot_devices.robots.feetech_calibration import run_arm_manual_calibration
 from lerobot.common.robot_devices.robots.utils import get_arm_id
 from lerobot.common.robot_devices.utils import RobotDeviceNotConnectedError
 
@@ -329,11 +324,7 @@ class MobileManipulator:
         socks = dict(poller.poll(15))
         if self.video_socket not in socks or socks[self.video_socket] != zmq.POLLIN:
             # No new data arrived → reuse ALL old data
-            return (
-                self.last_frames,
-                self.last_present_speed,
-                self.last_remote_arm_state,
-            )
+            return (self.last_frames, self.last_present_speed, self.last_remote_arm_state)
 
         # Drain all messages, keep only the last
         last_msg = None
@@ -346,11 +337,7 @@ class MobileManipulator:
 
         if not last_msg:
             # No new message → also reuse old
-            return (
-                self.last_frames,
-                self.last_present_speed,
-                self.last_remote_arm_state,
-            )
+            return (self.last_frames, self.last_present_speed, self.last_remote_arm_state)
 
         # Decode only the final message
         try:
@@ -388,11 +375,7 @@ class MobileManipulator:
         except Exception as e:
             print(f"[DEBUG] Error decoding video message: {e}")
             # If decode fails, fall back to old data
-            return (
-                self.last_frames,
-                self.last_present_speed,
-                self.last_remote_arm_state,
-            )
+            return (self.last_frames, self.last_present_speed, self.last_remote_arm_state)
 
         return frames, present_speed, remote_arm_state_tensor
 
@@ -478,11 +461,7 @@ class MobileManipulator:
 
         body_state = self.wheel_raw_to_body(present_speed)
 
-        body_state_mm = (
-            body_state[0] * 1000.0,
-            body_state[1] * 1000.0,
-            body_state[2],
-        )  # Convert x,y to mm/s
+        body_state_mm = (body_state[0] * 1000.0, body_state[1] * 1000.0, body_state[2])  # Convert x,y to mm/s
         wheel_state_tensor = torch.tensor(body_state_mm, dtype=torch.float32)
         combined_state_tensor = torch.cat((remote_arm_state_tensor, wheel_state_tensor), dim=0)
 
@@ -641,11 +620,7 @@ class MobileManipulator:
         # Convert each wheel’s angular speed (deg/s) to a raw integer.
         wheel_raw = [MobileManipulator.degps_to_raw(deg) for deg in wheel_degps]
 
-        return {
-            "left_wheel": wheel_raw[0],
-            "back_wheel": wheel_raw[1],
-            "right_wheel": wheel_raw[2],
-        }
+        return {"left_wheel": wheel_raw[0], "back_wheel": wheel_raw[1], "right_wheel": wheel_raw[2]}
 
     def wheel_raw_to_body(
         self, wheel_raw: dict, wheel_radius: float = 0.05, base_radius: float = 0.125

lerobot/common/robot_devices/robots/utils.py

@@ -72,9 +72,7 @@ def make_robot_from_config(config: RobotConfig):
 
         return ManipulatorRobot(config)
     elif isinstance(config, LeKiwiRobotConfig):
-        from lerobot.common.robot_devices.robots.mobile_manipulator import (
-            MobileManipulator,
-        )
+        from lerobot.common.robot_devices.robots.mobile_manipulator import MobileManipulator
 
         return MobileManipulator(config)
     else:

lerobot/common/robot_devices/utils.py

@@ -48,8 +48,7 @@ class RobotDeviceNotConnectedError(Exception):
     """Exception raised when the robot device is not connected."""
 
     def __init__(
-        self,
-        message="This robot device is not connected. Try calling `robot_device.connect()` first.",
+        self, message="This robot device is not connected. Try calling `robot_device.connect()` first."
     ):
         self.message = message
         super().__init__(self.message)

lerobot/common/utils/random_utils.py

@@ -42,11 +42,7 @@ def deserialize_python_rng_state(rng_state_dict: dict[str, torch.Tensor]) -> Non
     """
     Restores the rng state for `random` from a dictionary produced by `serialize_python_rng_state()`.
     """
-    py_state = (
-        rng_state_dict["py_rng_version"].item(),
-        tuple(rng_state_dict["py_rng_state"].tolist()),
-        None,
-    )
+    py_state = (rng_state_dict["py_rng_version"].item(), tuple(rng_state_dict["py_rng_state"].tolist()), None)
     random.setstate(py_state)
 
 

lerobot/configs/parser.py

@@ -42,10 +42,7 @@ def get_cli_overrides(field_name: str, args: Sequence[str] | None = None) -> lis
         args = sys.argv[1:]
     attr_level_args = []
     detect_string = f"--{field_name}."
-    exclude_strings = (
-        f"--{field_name}.{draccus.CHOICE_TYPE_KEY}=",
-        f"--{field_name}.{PATH_KEY}=",
-    )
+    exclude_strings = (f"--{field_name}.{draccus.CHOICE_TYPE_KEY}=", f"--{field_name}.{PATH_KEY}=")
     for arg in args:
         if arg.startswith(detect_string) and not arg.startswith(exclude_strings):
             denested_arg = f"--{arg.removeprefix(detect_string)}"

lerobot/configs/policies.py

@@ -26,11 +26,7 @@ from huggingface_hub.errors import HfHubHTTPError
 from lerobot.common.optim.optimizers import OptimizerConfig
 from lerobot.common.optim.schedulers import LRSchedulerConfig
 from lerobot.common.utils.hub import HubMixin
-from lerobot.common.utils.utils import (
-    auto_select_torch_device,
-    is_amp_available,
-    is_torch_device_available,
-)
+from lerobot.common.utils.utils import auto_select_torch_device, is_amp_available, is_torch_device_available
 from lerobot.configs.types import FeatureType, NormalizationMode, PolicyFeature
 
 # Generic variable that is either PreTrainedConfig or a subclass thereof

lerobot/scripts/configure_motor.py

@@ -38,12 +38,7 @@ def get_motor_bus_cls(brand: str) -> tuple:
             FeetechMotorsBus,
         )
 
-        return (
-            FeetechMotorsBusConfig,
-            FeetechMotorsBus,
-            MODEL_BAUDRATE_TABLE,
-            SCS_SERIES_BAUDRATE_TABLE,
-        )
+        return FeetechMotorsBusConfig, FeetechMotorsBus, MODEL_BAUDRATE_TABLE, SCS_SERIES_BAUDRATE_TABLE
 
     elif brand == "dynamixel":
         from lerobot.common.robot_devices.motors.configs import DynamixelMotorsBusConfig
@@ -53,12 +48,7 @@ def get_motor_bus_cls(brand: str) -> tuple:
             DynamixelMotorsBus,
         )
 
-        return (
-            DynamixelMotorsBusConfig,
-            DynamixelMotorsBus,
-            MODEL_BAUDRATE_TABLE,
-            X_SERIES_BAUDRATE_TABLE,
-        )
+        return DynamixelMotorsBusConfig, DynamixelMotorsBus, MODEL_BAUDRATE_TABLE, X_SERIES_BAUDRATE_TABLE
 
     else:
         raise ValueError(
@@ -174,25 +164,12 @@ def configure_motor(port, brand, model, motor_idx_des, baudrate_des):
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "--port",
-        type=str,
-        required=True,
-        help="Motors bus port (e.g. dynamixel,feetech)",
-    )
+    parser.add_argument("--port", type=str, required=True, help="Motors bus port (e.g. dynamixel,feetech)")
     parser.add_argument("--brand", type=str, required=True, help="Motor brand (e.g. dynamixel,feetech)")
     parser.add_argument("--model", type=str, required=True, help="Motor model (e.g. xl330-m077,sts3215)")
+    parser.add_argument("--ID", type=int, required=True, help="Desired ID of the current motor (e.g. 1,2,3)")
     parser.add_argument(
-        "--ID",
-        type=int,
-        required=True,
-        help="Desired ID of the current motor (e.g. 1,2,3)",
-    )
-    parser.add_argument(
-        "--baudrate",
-        type=int,
-        default=1000000,
-        help="Desired baudrate for the motor (default: 1000000)",
+        "--baudrate", type=int, default=1000000, help="Desired baudrate for the motor (default: 1000000)"
     )
     args = parser.parse_args()
 

lerobot/scripts/control_sim_robot.py

@@ -149,11 +149,7 @@ def init_sim_calibration(robot, cfg):
     axis_directions = np.array(cfg.get("axis_directions", [1]))
     offsets = np.array(cfg.get("offsets", [0])) * np.pi
 
-    return {
-        "start_pos": start_pos,
-        "axis_directions": axis_directions,
-        "offsets": offsets,
-    }
+    return {"start_pos": start_pos, "axis_directions": axis_directions, "offsets": offsets}
 
 
 def real_positions_to_sim(real_positions, axis_directions, start_pos, offsets):
@@ -203,7 +199,6 @@ def record(
     run_compute_stats: bool = True,
 ) -> LeRobotDataset:
     # Load pretrained policy
-
     policy = None
     if pretrained_policy_name_or_path is not None:
         policy, policy_fps, device, use_amp = init_policy(pretrained_policy_name_or_path, policy_overrides)
@@ -246,11 +241,7 @@ def record(
             shape = env.observation_space[key].shape
             if not key.startswith("observation.image."):
                 key = "observation.image." + key
-            features[key] = {
-                "dtype": "video",
-                "names": ["channels", "height", "width"],
-                "shape": shape,
-            }
+            features[key] = {"dtype": "video", "names": ["channels", "height", "width"], "shape": shape}
 
         for key, obs_key in state_keys_dict.items():
             features[key] = {
@@ -259,11 +250,7 @@ def record(
                 "shape": env.observation_space[obs_key].shape,
             }
 
-        features["action"] = {
-            "dtype": "float32",
-            "shape": env.action_space.shape,
-            "names": None,
-        }
+        features["action"] = {"dtype": "float32", "shape": env.action_space.shape, "names": None}
 
         # Create empty dataset or load existing saved episodes
         sanity_check_dataset_name(repo_id, policy)
@@ -374,12 +361,7 @@ def record(
 
 
 def replay(
-    env,
-    root: Path,
-    repo_id: str,
-    episode: int,
-    fps: int | None = None,
-    local_files_only: bool = True,
+    env, root: Path, repo_id: str, episode: int, fps: int | None = None, local_files_only: bool = True
 ):
     env = env()
 
@@ -426,10 +408,7 @@ if __name__ == "__main__":
 
     parser_record = subparsers.add_parser("record", parents=[base_parser])
     parser_record.add_argument(
-        "--fps",
-        type=none_or_int,
-        default=None,
-        help="Frames per second (set to None to disable)",
+        "--fps", type=none_or_int, default=None, help="Frames per second (set to None to disable)"
     )
     parser_record.add_argument(
         "--root",
@@ -507,19 +486,9 @@ if __name__ == "__main__":
         default=0,
         help="Resume recording on an existing dataset.",
     )
-    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)",
+        "--fps", type=none_or_int, default=None, help="Frames per second (set to None to disable)"
     )
     parser_replay.add_argument(
         "--root",

lerobot/scripts/eval.py

@@ -293,8 +293,7 @@ def eval_policy(
             seeds = None
         else:
             seeds = range(
-                start_seed + (batch_ix * env.num_envs),
-                start_seed + ((batch_ix + 1) * env.num_envs),
+                start_seed + (batch_ix * env.num_envs), start_seed + ((batch_ix + 1) * env.num_envs)
             )
         rollout_data = rollout(
             env,
@@ -414,11 +413,7 @@ def eval_policy(
 
 
 def _compile_episode_data(
-    rollout_data: dict,
-    done_indices: Tensor,
-    start_episode_index: int,
-    start_data_index: int,
-    fps: float,
+    rollout_data: dict, done_indices: Tensor, start_episode_index: int, start_data_index: int, fps: float
 ) -> dict:
     """Convenience function for `eval_policy(return_episode_data=True)`
 
@@ -486,10 +481,7 @@ def eval_main(cfg: EvalPipelineConfig):
     )
     policy.eval()
 
-    with (
-        torch.no_grad(),
-        torch.autocast(device_type=device.type) if cfg.policy.use_amp else nullcontext(),
-    ):
+    with torch.no_grad(), torch.autocast(device_type=device.type) if cfg.policy.use_amp else nullcontext():
         info = eval_policy(
             env,
             policy,

lerobot/scripts/train.py

@@ -196,11 +196,7 @@ def train(cfg: TrainPipelineConfig):
     }
 
     train_tracker = MetricsTracker(
-        cfg.batch_size,
-        dataset.num_frames,
-        dataset.num_episodes,
-        train_metrics,
-        initial_step=step,
+        cfg.batch_size, dataset.num_frames, dataset.num_episodes, train_metrics, initial_step=step
     )
 
     logging.info("Start offline training on a fixed dataset")
@@ -271,11 +267,7 @@ def train(cfg: TrainPipelineConfig):
                 "eval_s": AverageMeter("eval_s", ":.3f"),
             }
             eval_tracker = MetricsTracker(
-                cfg.batch_size,
-                dataset.num_frames,
-                dataset.num_episodes,
-                eval_metrics,
-                initial_step=step,
+                cfg.batch_size, dataset.num_frames, dataset.num_episodes, eval_metrics, initial_step=step
             )
             eval_tracker.eval_s = eval_info["aggregated"].pop("eval_s")
             eval_tracker.avg_sum_reward = eval_info["aggregated"].pop("avg_sum_reward")

lerobot/scripts/visualize_dataset_html.py

@@ -81,11 +81,7 @@ def run_server(
     static_folder: Path,
     template_folder: Path,
 ):
-    app = Flask(
-        __name__,
-        static_folder=static_folder.resolve(),
-        template_folder=template_folder.resolve(),
-    )
+    app = Flask(__name__, static_folder=static_folder.resolve(), template_folder=template_folder.resolve())
     app.config["SEND_FILE_MAX_AGE_DEFAULT"] = 0  # specifying not to cache
 
     @app.route("/")
@@ -201,8 +197,7 @@ def run_server(
             ]
 
             response = requests.get(
-                f"https://huggingface.co/datasets/{repo_id}/resolve/main/meta/episodes.jsonl",
-                timeout=5,
+                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
@@ -287,8 +282,7 @@ def get_episode_data(dataset: LeRobotDataset | IterableNamespace, episode_index)
         repo_id = dataset.repo_id
 
         url = f"https://huggingface.co/datasets/{repo_id}/resolve/main/" + dataset.data_path.format(
-            episode_chunk=int(episode_index) // dataset.chunks_size,
-            episode_index=episode_index,
+            episode_chunk=int(episode_index) // dataset.chunks_size, episode_index=episode_index
         )
         df = pd.read_parquet(url)
         data = df[selected_columns]  # Select specific columns
@@ -337,8 +331,7 @@ 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",
-        timeout=5,
+        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()

pyproject.toml

@@ -71,7 +71,7 @@ dependencies = [
     "rerun-sdk>=0.21.0",
     "termcolor>=2.4.0",
     "torch>=2.2.1,<2.7",
-    "torchcodec>=0.2.1; sys_platform != 'win32' and (sys_platform != 'linux' or (platform_machine != 'aarch64' and platform_machine != 'arm64' and platform_machine != 'armv7l')) and (sys_platform != 'darwin' or platform_machine != 'x86_64')",
+    "torchcodec==0.2.1; sys_platform != 'win32' and (sys_platform != 'linux' or (platform_machine != 'aarch64' and platform_machine != 'arm64' and platform_machine != 'armv7l')) and (sys_platform != 'darwin' or platform_machine != 'x86_64')",
     "torchmetrics>=1.6.0",
     "torchvision>=0.21.0",
     "transformers>=4.47.0",

tests/policies/test_policies.py

@@ -59,33 +59,16 @@ def dummy_dataset_metadata(lerobot_dataset_metadata_factory, info_factory, tmp_p
         "action": {
             "dtype": "float32",
             "shape": (6,),
-            "names": [
-                "shoulder_pan",
-                "shoulder_lift",
-                "elbow_flex",
-                "wrist_flex",
-                "wrist_roll",
-                "gripper",
-            ],
+            "names": ["shoulder_pan", "shoulder_lift", "elbow_flex", "wrist_flex", "wrist_roll", "gripper"],
         },
         "observation.state": {
             "dtype": "float32",
             "shape": (6,),
-            "names": [
-                "shoulder_pan",
-                "shoulder_lift",
-                "elbow_flex",
-                "wrist_flex",
-                "wrist_roll",
-                "gripper",
-            ],
+            "names": ["shoulder_pan", "shoulder_lift", "elbow_flex", "wrist_flex", "wrist_roll", "gripper"],
         },
     }
     info = info_factory(
-        total_episodes=1,
-        total_frames=1,
-        camera_features=camera_features,
-        motor_features=motor_features,
+        total_episodes=1, total_frames=1, camera_features=camera_features, motor_features=motor_features
     )
     ds_meta = lerobot_dataset_metadata_factory(root=tmp_path / "init", info=info)
     return ds_meta
@@ -98,8 +81,7 @@ def test_get_policy_and_config_classes(policy_name: str):
     policy_cfg = make_policy_config(policy_name)
     assert policy_cls.name == policy_name
     assert issubclass(
-        policy_cfg.__class__,
-        inspect.signature(policy_cls.__init__).parameters["config"].annotation,
+        policy_cfg.__class__, inspect.signature(policy_cls.__init__).parameters["config"].annotation
     )
 
 
@@ -110,13 +92,7 @@ def test_get_policy_and_config_classes(policy_name: str):
         ("lerobot/pusht", "pusht", {}, "diffusion", {}),
         ("lerobot/pusht", "pusht", {}, "vqbet", {}),
         ("lerobot/pusht", "pusht", {}, "act", {}),
-        (
-            "lerobot/aloha_sim_insertion_human",
-            "aloha",
-            {"task": "AlohaInsertion-v0"},
-            "act",
-            {},
-        ),
+        ("lerobot/aloha_sim_insertion_human", "aloha", {"task": "AlohaInsertion-v0"}, "act", {}),
         (
             "lerobot/aloha_sim_insertion_scripted",
             "aloha",

tests/robots/test_control_robot.py

@@ -390,8 +390,7 @@ def test_record_with_event_exit_early(tmp_path, request, robot_type, mock):
 
 
 @pytest.mark.parametrize(
-    "robot_type, mock, num_image_writer_processes",
-    [("koch", True, 0), ("koch", True, 1)],
+    "robot_type, mock, num_image_writer_processes", [("koch", True, 0), ("koch", True, 1)]
 )
 @require_robot
 def test_record_with_event_stop_recording(tmp_path, request, robot_type, mock, num_image_writer_processes):

tests/robots/test_robots.py

@@ -40,10 +40,7 @@ import pytest
 import torch
 
 from lerobot.common.robot_devices.robots.utils import make_robot
-from lerobot.common.robot_devices.utils import (
-    RobotDeviceAlreadyConnectedError,
-    RobotDeviceNotConnectedError,
-)
+from lerobot.common.robot_devices.utils import RobotDeviceAlreadyConnectedError, RobotDeviceNotConnectedError
 from tests.utils import TEST_ROBOT_TYPES, mock_calibration_dir, require_robot
 
 

tests/utils.py

@@ -26,9 +26,7 @@ from lerobot import available_cameras, available_motors, available_robots
 from lerobot.common.robot_devices.cameras.utils import Camera
 from lerobot.common.robot_devices.cameras.utils import make_camera as make_camera_device
 from lerobot.common.robot_devices.motors.utils import MotorsBus
-from lerobot.common.robot_devices.motors.utils import (
-    make_motors_bus as make_motors_bus_device,
-)
+from lerobot.common.robot_devices.motors.utils import make_motors_bus as make_motors_bus_device
 from lerobot.common.utils.import_utils import is_package_available
 
 DEVICE = os.environ.get("LEROBOT_TEST_DEVICE", "cuda") if torch.cuda.is_available() else "cpu"
@@ -287,14 +285,7 @@ def mock_calibration_dir(calibration_dir):
         "start_pos": [1442, 843, 2166, 2849, 1988, 1835],
         "end_pos": [2440, 1869, -1106, -1848, -926, 3235],
         "calib_mode": ["DEGREE", "DEGREE", "DEGREE", "DEGREE", "DEGREE", "LINEAR"],
-        "motor_names": [
-            "shoulder_pan",
-            "shoulder_lift",
-            "elbow_flex",
-            "wrist_flex",
-            "wrist_roll",
-            "gripper",
-        ],
+        "motor_names": ["shoulder_pan", "shoulder_lift", "elbow_flex", "wrist_flex", "wrist_roll", "gripper"],
     }
     Path(str(calibration_dir)).mkdir(parents=True, exist_ok=True)
     with open(calibration_dir / "main_follower.json", "w") as f:

tests/utils/test_logging_utils.py

@@ -18,10 +18,7 @@ from lerobot.common.utils.logging_utils import AverageMeter, MetricsTracker
 
 @pytest.fixture
 def mock_metrics():
-    return {
-        "loss": AverageMeter("loss", ":.3f"),
-        "accuracy": AverageMeter("accuracy", ":.2f"),
-    }
+    return {"loss": AverageMeter("loss", ":.3f"), "accuracy": AverageMeter("accuracy", ":.2f")}
 
 
 def test_average_meter_initialization():
@@ -61,11 +58,7 @@ def test_average_meter_str():
 
 def test_metrics_tracker_initialization(mock_metrics):
     tracker = MetricsTracker(
-        batch_size=32,
-        num_frames=1000,
-        num_episodes=50,
-        metrics=mock_metrics,
-        initial_step=10,
+        batch_size=32, num_frames=1000, num_episodes=50, metrics=mock_metrics, initial_step=10
     )
     assert tracker.steps == 10
     assert tracker.samples == 10 * 32
@@ -77,11 +70,7 @@ def test_metrics_tracker_initialization(mock_metrics):
 
 def test_metrics_tracker_step(mock_metrics):
     tracker = MetricsTracker(
-        batch_size=32,
-        num_frames=1000,
-        num_episodes=50,
-        metrics=mock_metrics,
-        initial_step=5,
+        batch_size=32, num_frames=1000, num_episodes=50, metrics=mock_metrics, initial_step=5
     )
     tracker.step()
     assert tracker.steps == 6