chore(format)

* fix(utils): validate precise_sleep spin/margin args

Negative spin_threshold/sleep_margin make remaining arithmetic wrong
and can overshoot. Reject them early; cover the no-op path.

* test: drop flaky wall-clock assertion in no-op test

Per review: the 50ms wall-clock check can exceed its bound on a preempted
CI worker even when precise_sleep returns immediately. The direct calls
already exercise the non-positive no-op path, so the assertion is redundant.

* chore(tests): remove precise_sleep test negative values

---------

Co-authored-by: Bartok9 <danielrpike9@gmail.com>

AGENT_GUIDE.md

@@ -61,15 +61,19 @@ Full details in [`docs/source/so101.mdx`](./docs/source/so101.mdx) and [`docs/so
 **4.1 Install**
 
 ```bash
-pip install 'lerobot[feetech]'              # SO-100/SO-101 motor stack
-# pip install 'lerobot[all]'                # everything
-# pip install 'lerobot[aloha,pusht]'        # specific features
-# pip install 'lerobot[smolvla]'            # add SmolVLA deps
-git lfs install && git lfs pull
-hf auth login                               # required to push datasets/policies
-```
+# uv (recommended — see AGENTS.md and CLAUDE.md)
+uv sync --locked --extra feetech          # SO-100/SO-101 motor stack
+# uv sync --locked --extra all            # everything
+# uv sync --locked --extra smolvla        # add SmolVLA deps
 
-Contributors can alternatively use `uv sync --locked --extra feetech` (see `AGENTS.md`).
+# pip (alternative, e.g. when not working from source)
+# pip install 'lerobot[feetech]'
+# pip install 'lerobot[all]'
+# pip install 'lerobot[smolvla]'
+
+git lfs install && git lfs pull
+hf auth login                             # required to push datasets/policies
+```
 
 **4.2 Find USB ports** — run once per arm, unplug when prompted.
 

docker/Dockerfile.internal

@@ -68,17 +68,16 @@ ENV HOME=/home/user_lerobot \
 # issues with MuJoCo and OpenGL drivers.
 RUN uv venv --python python${PYTHON_VERSION}
 
-# Install Python dependencies for caching
+# Install third-party dependencies separately for layer caching
 COPY --chown=user_lerobot:user_lerobot setup.py pyproject.toml uv.lock README.md MANIFEST.in ./
-COPY --chown=user_lerobot:user_lerobot src/ src/
-
-RUN uv sync --locked --extra all --no-cache
+RUN uv sync --locked --extra all --no-install-project --no-cache
 
 RUN chmod +x /lerobot/.venv/lib/python${PYTHON_VERSION}/site-packages/triton/backends/nvidia/bin/ptxas
 
-# Copy the rest of the application source code
+# Copy the application source code and install the local project
 # Make sure to have the git-LFS files for testing
 COPY --chown=user_lerobot:user_lerobot . .
+RUN uv sync --locked --extra all --no-cache
 
 # Set the default command
 CMD ["/bin/bash"]

docker/Dockerfile.user

@@ -60,15 +60,14 @@ ENV HOME=/home/user_lerobot \
 # run other Python projects in the same container without dependency conflicts.
 RUN uv venv
 
-# Install Python dependencies for caching
+# Install third-party dependencies separately for layer caching
 COPY --chown=user_lerobot:user_lerobot setup.py pyproject.toml uv.lock README.md MANIFEST.in ./
-COPY --chown=user_lerobot:user_lerobot src/ src/
+RUN uv sync --locked --extra all --no-install-project --no-cache
 
-RUN uv sync --locked --extra all --no-cache
-
-# Copy the rest of the application code
+# Copy the application code and install the local project
 # Make sure to have the git-LFS files for testing
 COPY --chown=user_lerobot:user_lerobot . .
+RUN uv sync --locked --extra all --no-cache
 
 # Set the default command
 CMD ["/bin/bash"]

docs/source/backwardcomp.mdx

@@ -58,7 +58,7 @@ final_action = postprocessor(action)
 
 ## Hardware API redesign
 
-PR [#777](https://github.com/huggingface/lerobot/pull/777) improves the LeRobot calibration but is **not backward-compatible**. Below is a overview of what changed and how you can continue to work with datasets created before this pull request.
+PR [#777](https://github.com/huggingface/lerobot/pull/777) improves the LeRobot calibration but is **not backward-compatible**. Below is an overview of what changed and how you can continue to work with datasets created before this pull request.
 
 ### What changed?
 
@@ -129,8 +129,8 @@ python examples/backward_compatibility/replay.py \
 
 Policies output actions in the same format as the datasets (`torch.Tensors`). Therefore, the same transformations should be applied.
 
-To find these transformations, we recommend to first try and and replay an episode of the dataset your policy was trained on using the section above.
-Then, add these same transformations on your inference script (shown here in the `record.py` script):
+To find these transformations, we recommend first replaying an episode of the dataset your policy was trained on using the section above.
+Then, add these same transformations to your inference script (shown here in the `record.py` script):
 
 ```diff
 action_values = predict_action(

docs/source/cameras.mdx

@@ -164,8 +164,8 @@ includes the range reported by the sensor. Requesting an unsupported control als
 Omitted controls leave the sensor's existing automatic or manual setting unchanged. These options
 require `use_rgb=True`.
 
-On the RealSense D405, the color stream is provided by the Stereo Module, so changing manual
-exposure or gain also affects the depth stream.
+Manual color controls require a dedicated RGB module. Cameras without one, such as the RealSense
+D405, do not support them and raise an error at connection time.
 
 </hfoption>
 </hfoptions>

docs/source/feetech.mdx

@@ -40,10 +40,10 @@ This tutorial guides you through updating the firmware of Feetech motors using t
 For each motor you want to update:
 
 1. **Select the motor** from the list by clicking on it
-2. **Click on Upgrade tab**:
-3. **Click on Online button**:
-   - If an potential firmware update is found, it will be displayed in the box
-4. **Click on Upgrade button**:
+2. **Click the Upgrade tab**:
+3. **Click the Online button**:
+   - If a potential firmware update is found, it will be displayed in the box
+4. **Click the Upgrade button**:
    - The update progress will be displayed
 
 ## Step 6: Verify Update

docs/source/processors_robots_teleop.mdx

@@ -22,7 +22,7 @@ With processors, you choose the learning features you want to use for your polic
 ## Three pipelines
 
 We often compose three pipelines. Depending on your setup, some can be empty if action and observation spaces already match.
-Each of these pipelines handle different conversions between different action and observation spaces. Below is a quick explanation of each pipeline.
+Each of these pipelines handles different conversions between different action and observation spaces. Below is a quick explanation of each pipeline.
 
 1. Pipeline 1: Teleop action space → dataset action space (phone pose → EE targets)
 2. Pipeline 2: Dataset action space → robot command space (EE targets → joints)
@@ -74,15 +74,15 @@ In the phone to SO-100 follower examples we use the following adapters:
 - `robot_action_to_transition`: transforms the teleop action dict to a pipeline transition.
 - `transition_to_robot_action`: transforms the pipeline transition to a robot action dict.
 - `observation_to_transition`: transforms the robot observation dict to a pipeline transition.
-- `transition_to_observation`: transforms the pipeline transition to a observation dict.
+- `transition_to_observation`: transforms the pipeline transition to an observation dict.
 
-Checkout [src/lerobot/processor/converters.py](https://github.com/huggingface/lerobot/blob/main/src/lerobot/processor/converters.py) for more details.
+Check out [src/lerobot/processor/converters.py](https://github.com/huggingface/lerobot/blob/main/src/lerobot/processor/converters.py) for more details.
 
 ## Dataset feature contracts
 
 Dataset features are determined by the keys saved in the dataset. Each step can declare what features it modifies in a contract called `transform_features(...)`. Once you build a processor, the processor can then aggregate all of these features with `aggregate_pipeline_dataset_features()` and merge multiple feature dicts with `combine_feature_dicts(...)`.
 
-Below is and example of how we declare features with the `transform_features` method in the phone to SO-100 follower examples:
+Below is an example of how we declare features with the `transform_features` method in the phone to SO-100 follower examples:
 
 ```python
     def transform_features(

docs/source/rtc.mdx

@@ -57,7 +57,7 @@ policy_cfg.rtc_config = RTCConfig(
 policy = PI0Policy.from_pretrained("lerobot/pi0_base", policy_cfg=policy_cfg, device="cuda")
 
 # Now use predict_action_chunk with RTC parameters
-inference_delay = 4  # How many steps of inference latency, this values should be calculated based on the inference latency of the policy
+inference_delay = 4  # How many steps of inference latency, this value should be calculated based on the inference latency of the policy
 
 # Initialize the action queue
 action_queue = ActionQueue(policy_cfg.rtc_config)
@@ -100,7 +100,7 @@ Typical values: 8-12 steps
 RTCConfig(execution_horizon=10)
 ```
 
-**`max_guidance_weight`**: How strongly to enforce consistency with the previous chunk. This is a hyperparameter that can be tuned to balance the smoothness of the transitions and the reactivity of the policy. For 10 steps flow matching (SmolVLA, Pi0, Pi0.5), a value of 10.0 is a optimal value.
+**`max_guidance_weight`**: How strongly to enforce consistency with the previous chunk. This is a hyperparameter that can be tuned to balance the smoothness of the transitions and the reactivity of the policy. For 10 steps flow matching (SmolVLA, Pi0, Pi0.5), a value of 10.0 is an optimal value.
 
 **`prefix_attention_schedule`**: How to weight consistency across the overlap region.
 

docs/source/using_dataset_tools.mdx

@@ -50,11 +50,11 @@ lerobot-edit-dataset \
 Divide a dataset into multiple subsets.
 
 ```bash
-# Split by fractions (e.g. 80% train, 20% test, 20% val)
+# Split by fractions (e.g. 60% train, 20% val, 20% test)
 lerobot-edit-dataset \
     --repo_id lerobot/pusht \
     --operation.type split \
-    --operation.splits '{"train": 0.8, "test": 0.2, "val": 0.2}'
+    --operation.splits '{"train": 0.6, "val": 0.2, "test": 0.2}'
 
 # Split by specific episode indices
 lerobot-edit-dataset \

pyproject.toml

@@ -494,6 +494,19 @@ ignore_errors = true
 module = "lerobot.envs.*"
 ignore_errors = false
 
+[[tool.mypy.overrides]]
+module = "lerobot.annotations.*"
+ignore_errors = false
+disallow_untyped_defs = true
+disallow_incomplete_defs = true
+check_untyped_defs = true
+
+[[tool.mypy.overrides]]
+module = "lerobot.transforms.*"
+ignore_errors = false
+disallow_untyped_defs = true
+disallow_incomplete_defs = true
+check_untyped_defs = true
 
 # [[tool.mypy.overrides]]
 # module = "lerobot.utils.*"

src/lerobot/cameras/realsense/camera_realsense.py

@@ -365,11 +365,12 @@ class RealSenseCamera(Camera):
         return self._async_read(timeout_ms=10000, read_depth=read_depth)
 
     def _get_color_sensor(self) -> "rs.sensor":
-        """Returns the sensor that controls the color stream.
+        """Returns the dedicated "RGB Camera" sensor that controls the color stream.
 
-        Most RealSense cameras expose "RGB Camera" for color. The D405 has no
-        separate RGB module — its color stream comes from "Stereo Module".
-        We try RGB Camera first, then fall back to Stereo Module.
+        Manual color controls are only applied to a dedicated RGB module. Cameras
+        without one (e.g. the D405, whose color stream comes from the shared
+        "Stereo Module") are unsupported, so we never fall back to another sensor
+        to avoid altering the depth stream.
         """
         if self.rs_profile is None:
             raise RuntimeError(f"{self}: rs_profile must be initialized before use.")
@@ -377,12 +378,14 @@ class RealSenseCamera(Camera):
         device = self.rs_profile.get_device()
         sensors = {s.get_info(rs.camera_info.name): s for s in device.query_sensors()}
 
-        for name in ("RGB Camera", "Stereo Module"):
-            if name in sensors:
-                return sensors[name]
+        if "RGB Camera" in sensors:
+            return sensors["RGB Camera"]
 
         available = list(sensors.keys())
-        raise RuntimeError(f"{self}: no color sensor found. Available sensors: {available}")
+        raise RuntimeError(
+            f"{self}: manual color controls require a dedicated 'RGB Camera' module, which this camera does not have. ",
+            f"Available sensors: {available}.",
+        )
 
     def _set_sensor_option(self, sensor: "rs.sensor", option: "rs.option", value: float, label: str) -> None:
         """Sets a sensor option, re-raising range errors with actionable diagnostics."""

src/lerobot/datasets/aggregate.py

@@ -19,6 +19,7 @@ import copy
 import logging
 import shutil
 from pathlib import Path
+from typing import Any, NotRequired, TypedDict
 
 import datasets
 import pandas as pd
@@ -49,8 +50,32 @@ from .utils import (
 )
 from .video_utils import concatenate_video_files, get_video_duration_in_s
 
+logger = logging.getLogger(__name__)
 
-def merge_video_feature_info_for_aggregate(all_metadata: list[LeRobotDatasetMetadata]) -> dict[str, dict]:
+type FeatureDict = dict[str, dict[str, Any]]
+type ChunkFile = tuple[int, int]
+
+
+class IndexState(TypedDict):
+    chunk: int
+    file: int
+    src_to_dst: NotRequired[dict[ChunkFile, ChunkFile]]
+
+
+class VideoIndex(TypedDict):
+    chunk: int
+    file: int
+    latest_duration: float
+    episode_duration: float
+    src_to_offset: NotRequired[dict[ChunkFile, float]]
+    src_to_dst: NotRequired[dict[ChunkFile, ChunkFile]]
+    dst_file_durations: NotRequired[dict[ChunkFile, float]]
+
+
+type VideoIndexState = dict[str, VideoIndex]
+
+
+def merge_video_feature_info_for_aggregate(all_metadata: list[LeRobotDatasetMetadata]) -> FeatureDict:
     """Create a merged video feature info dictionary for aggregation. The video encoder info is merged field-by-field: each key is kept only when every source agrees; otherwise that key is set to ``null`` (or ``{}`` for ``video.extra_options``) and a warning is logged.
 
     Args:
@@ -59,14 +84,14 @@ def merge_video_feature_info_for_aggregate(all_metadata: list[LeRobotDatasetMeta
     Returns:
         dict: A dictionary of merged video feature info.
     """
-    merged_info = copy.deepcopy(all_metadata[0].features)
+    merged_info: FeatureDict = copy.deepcopy(all_metadata[0].features)
     video_keys = [k for k in merged_info if merged_info[k].get("dtype") == "video"]
 
     for vk in video_keys:
         video_infos = [m.features.get(vk, {}).get("info") or {} for m in all_metadata]
         base_video_info = video_infos[0]
 
-        merged_encoder_info: dict = {}
+        merged_encoder_info: dict[str, Any] = {}
         fallback_keys: list[str] = []
         for info_key in VIDEO_ENCODER_INFO_KEYS:
             values = [info.get(info_key, None) for info in video_infos]
@@ -80,7 +105,7 @@ def merge_video_feature_info_for_aggregate(all_metadata: list[LeRobotDatasetMeta
                 merged_encoder_info[info_key] = {} if info_key == "video.extra_options" else None
 
         if fallback_keys:
-            logging.warning(
+            logger.warning(
                 f"Merging heterogeneous or incomplete video encoder metadata for feature {vk}. "
                 f"Setting these keys to null: {fallback_keys}.",
             )
@@ -92,7 +117,7 @@ def merge_video_feature_info_for_aggregate(all_metadata: list[LeRobotDatasetMeta
     return merged_info
 
 
-def validate_all_metadata(all_metadata: list[LeRobotDatasetMetadata]):
+def validate_all_metadata(all_metadata: list[LeRobotDatasetMetadata]) -> tuple[int, str | None, FeatureDict]:
     """Validates that all dataset metadata have consistent properties.
 
     Ensures all datasets have the same fps, robot_type, and features to guarantee
@@ -129,7 +154,9 @@ def validate_all_metadata(all_metadata: list[LeRobotDatasetMetadata]):
     return fps, robot_type, features
 
 
-def update_data_df(df, src_meta, dst_meta):
+def update_data_df(
+    df: pd.DataFrame, src_meta: LeRobotDatasetMetadata, dst_meta: LeRobotDatasetMetadata
+) -> pd.DataFrame:
     """Updates a data DataFrame with new indices and task mappings for aggregation.
 
     Adjusts episode indices, frame indices, and task indices to account for
@@ -154,12 +181,12 @@ def update_data_df(df, src_meta, dst_meta):
 
 
 def update_meta_data(
-    df,
-    dst_meta,
-    meta_idx,
-    data_idx,
-    videos_idx,
-):
+    df: pd.DataFrame,
+    dst_meta: LeRobotDatasetMetadata,
+    meta_idx: IndexState,
+    data_idx: IndexState,
+    videos_idx: VideoIndexState,
+) -> pd.DataFrame:
     """Updates metadata DataFrame with new chunk, file, and timestamp indices.
 
     Adjusts all indices and timestamps to account for previously aggregated
@@ -289,7 +316,7 @@ def aggregate_datasets(
     chunk_size: int | None = None,
     concatenate_videos: bool = True,
     concatenate_data: bool = True,
-):
+) -> None:
     """Aggregates multiple LeRobot datasets into a single unified dataset.
 
     This is the main function that orchestrates the aggregation process by:
@@ -309,7 +336,7 @@ def aggregate_datasets(
         concatenate_videos: When False, keep one mp4 per source file instead of packing into shards.
         concatenate_data: When False, keep one parquet per source file instead of packing into shards.
     """
-    logging.info("Start aggregate_datasets")
+    logger.info("Start aggregate_datasets")
 
     if data_files_size_in_mb is None:
         data_files_size_in_mb = DEFAULT_DATA_FILE_SIZE_IN_MB
@@ -341,15 +368,15 @@ def aggregate_datasets(
         video_files_size_in_mb=video_files_size_in_mb,
     )
 
-    logging.info("Find all tasks")
+    logger.info("Find all tasks")
     unique_tasks = pd.concat([m.tasks for m in all_metadata]).index.unique()
     dst_meta.tasks = pd.DataFrame(
         {"task_index": range(len(unique_tasks))}, index=pd.Index(unique_tasks, name="task")
     )
 
-    meta_idx = {"chunk": 0, "file": 0}
-    data_idx = {"chunk": 0, "file": 0}
-    videos_idx = {
+    meta_idx: IndexState = {"chunk": 0, "file": 0}
+    data_idx: IndexState = {"chunk": 0, "file": 0}
+    videos_idx: VideoIndexState = {
         key: {"chunk": 0, "file": 0, "latest_duration": 0, "episode_duration": 0} for key in video_keys
     }
 
@@ -373,12 +400,17 @@ def aggregate_datasets(
         dst_meta.info.total_frames += src_meta.total_frames
 
     finalize_aggregation(dst_meta, all_metadata)
-    logging.info("Aggregation complete.")
+    logger.info("Aggregation complete.")
 
 
 def aggregate_videos(
-    src_meta, dst_meta, videos_idx, video_files_size_in_mb, chunk_size, concatenate_videos=True
-):
+    src_meta: LeRobotDatasetMetadata,
+    dst_meta: LeRobotDatasetMetadata,
+    videos_idx: VideoIndexState,
+    video_files_size_in_mb: float,
+    chunk_size: int,
+    concatenate_videos: bool = True,
+) -> VideoIndexState:
     """Aggregates video chunks from a source dataset into the destination dataset.
 
     Handles video file concatenation and rotation based on file size limits.
@@ -406,15 +438,16 @@ def aggregate_videos(
             videos_idx[key]["dst_file_durations"] = {}
 
     for key, video_idx in videos_idx.items():
-        unique_chunk_file_pairs = {
-            (chunk, file)
-            for chunk, file in zip(
-                src_meta.episodes[f"videos/{key}/chunk_index"],
-                src_meta.episodes[f"videos/{key}/file_index"],
-                strict=False,
-            )
-        }
-        unique_chunk_file_pairs = sorted(unique_chunk_file_pairs)
+        unique_chunk_file_pairs: list[ChunkFile] = sorted(
+            {
+                (chunk, file)
+                for chunk, file in zip(
+                    src_meta.episodes[f"videos/{key}/chunk_index"],
+                    src_meta.episodes[f"videos/{key}/file_index"],
+                    strict=False,
+                )
+            }
+        )
 
         chunk_idx = video_idx["chunk"]
         file_idx = video_idx["file"]
@@ -489,7 +522,14 @@ def aggregate_videos(
     return videos_idx
 
 
-def aggregate_data(src_meta, dst_meta, data_idx, data_files_size_in_mb, chunk_size, concatenate_data=True):
+def aggregate_data(
+    src_meta: LeRobotDatasetMetadata,
+    dst_meta: LeRobotDatasetMetadata,
+    data_idx: IndexState,
+    data_files_size_in_mb: float,
+    chunk_size: int,
+    concatenate_data: bool = True,
+) -> IndexState:
     """Aggregates data chunks from a source dataset into the destination dataset.
 
     Reads source data files, updates indices to match the aggregated dataset,
@@ -510,14 +550,16 @@ def aggregate_data(src_meta, dst_meta, data_idx, data_files_size_in_mb, chunk_si
     Returns:
         dict: Updated data_idx with current chunk and file indices.
     """
-    unique_chunk_file_ids = {
-        (c, f)
-        for c, f in zip(
-            src_meta.episodes["data/chunk_index"], src_meta.episodes["data/file_index"], strict=False
-        )
-    }
-
-    unique_chunk_file_ids = sorted(unique_chunk_file_ids)
+    unique_chunk_file_ids: list[ChunkFile] = sorted(
+        {
+            (c, f)
+            for c, f in zip(
+                src_meta.episodes["data/chunk_index"],
+                src_meta.episodes["data/file_index"],
+                strict=False,
+            )
+        }
+    )
     contains_images = len(dst_meta.image_keys) > 0
 
     # retrieve features schema for proper image typing in parquet
@@ -525,7 +567,7 @@ def aggregate_data(src_meta, dst_meta, data_idx, data_files_size_in_mb, chunk_si
 
     # Track source to destination file mapping for metadata update
     # This is critical for handling datasets that are already results of a merge
-    src_to_dst: dict[tuple[int, int], tuple[int, int]] = {}
+    src_to_dst: dict[ChunkFile, ChunkFile] = {}
 
     for src_chunk_idx, src_file_idx in unique_chunk_file_ids:
         src_path = src_meta.root / DEFAULT_DATA_PATH.format(
@@ -564,7 +606,13 @@ def aggregate_data(src_meta, dst_meta, data_idx, data_files_size_in_mb, chunk_si
     return data_idx
 
 
-def aggregate_metadata(src_meta, dst_meta, meta_idx, data_idx, videos_idx):
+def aggregate_metadata(
+    src_meta: LeRobotDatasetMetadata,
+    dst_meta: LeRobotDatasetMetadata,
+    meta_idx: IndexState,
+    data_idx: IndexState,
+    videos_idx: VideoIndexState,
+) -> IndexState:
     """Aggregates metadata from a source dataset into the destination dataset.
 
     Reads source metadata files, updates all indices and timestamps,
@@ -580,16 +628,16 @@ def aggregate_metadata(src_meta, dst_meta, meta_idx, data_idx, videos_idx):
     Returns:
         dict: Updated meta_idx with current chunk and file indices.
     """
-    chunk_file_ids = {
-        (c, f)
-        for c, f in zip(
-            src_meta.episodes["meta/episodes/chunk_index"],
-            src_meta.episodes["meta/episodes/file_index"],
-            strict=False,
-        )
-    }
-
-    chunk_file_ids = sorted(chunk_file_ids)
+    chunk_file_ids: list[ChunkFile] = sorted(
+        {
+            (c, f)
+            for c, f in zip(
+                src_meta.episodes["meta/episodes/chunk_index"],
+                src_meta.episodes["meta/episodes/file_index"],
+                strict=False,
+            )
+        }
+    )
     for chunk_idx, file_idx in chunk_file_ids:
         src_path = src_meta.root / DEFAULT_EPISODES_PATH.format(chunk_index=chunk_idx, file_index=file_idx)
         df = pd.read_parquet(src_path)
@@ -622,16 +670,16 @@ def aggregate_metadata(src_meta, dst_meta, meta_idx, data_idx, videos_idx):
 def append_or_create_parquet_file(
     df: pd.DataFrame,
     src_path: Path,
-    idx: dict[str, int],
+    idx: IndexState,
     max_mb: float,
     chunk_size: int,
     default_path: str,
     contains_images: bool = False,
-    aggr_root: Path = None,
+    aggr_root: Path | None = None,
     hf_features: datasets.Features | None = None,
     concatenate: bool = True,
     one_row_group_per_episode: bool = False,
-) -> tuple[dict[str, int], tuple[int, int]]:
+) -> tuple[IndexState, ChunkFile]:
     """Appends data to an existing parquet file or creates a new one based on size constraints.
 
     Manages file rotation when size limits are exceeded to prevent individual files
@@ -654,7 +702,13 @@ def append_or_create_parquet_file(
     Returns:
         tuple: (updated_idx, (dst_chunk, dst_file)) where updated_idx is the index dict
                and (dst_chunk, dst_file) is the actual destination file the data was written to.
+
+    Raises:
+        ValueError: If aggr_root is not provided.
     """
+    if aggr_root is None:
+        raise ValueError("aggr_root must be provided.")
+
     dst_chunk, dst_file = idx["chunk"], idx["file"]
     dst_path = aggr_root / default_path.format(chunk_index=dst_chunk, file_index=dst_file)
 
@@ -698,7 +752,9 @@ def append_or_create_parquet_file(
     return idx, (dst_chunk, dst_file)
 
 
-def finalize_aggregation(aggr_meta, all_metadata):
+def finalize_aggregation(
+    aggr_meta: LeRobotDatasetMetadata, all_metadata: list[LeRobotDatasetMetadata]
+) -> None:
     """Finalizes the dataset aggregation by writing summary files and statistics.
 
     Writes the tasks file, info file with total counts and splits, and
@@ -708,16 +764,16 @@ def finalize_aggregation(aggr_meta, all_metadata):
         aggr_meta: Aggregated dataset metadata.
         all_metadata: List of all source dataset metadata objects.
     """
-    logging.info("write tasks")
+    logger.info("write tasks")
     write_tasks(aggr_meta.tasks, aggr_meta.root)
 
-    logging.info("write info")
+    logger.info("write info")
     aggr_meta.info.total_tasks = len(aggr_meta.tasks)
     aggr_meta.info.total_episodes = sum(m.total_episodes for m in all_metadata)
     aggr_meta.info.total_frames = sum(m.total_frames for m in all_metadata)
     aggr_meta.info.splits = {"train": f"0:{sum(m.total_episodes for m in all_metadata)}"}
     write_info(aggr_meta.info, aggr_meta.root)
 
-    logging.info("write stats")
+    logger.info("write stats")
     aggr_meta.stats = aggregate_stats([m.stats for m in all_metadata])
     write_stats(aggr_meta.stats, aggr_meta.root)

src/lerobot/datasets/dataset_metadata.py

@@ -188,8 +188,8 @@ class LeRobotDatasetMetadata:
     def _load_metadata(self):
         self.info = load_info(self.root)
         check_version_compatibility(self.repo_id, self._version, CODEBASE_VERSION)
-        self.tasks = load_tasks(self.root)
-        self.episodes = load_episodes(self.root)
+        self.tasks = load_tasks(self.root) if self.total_tasks > 0 else None
+        self.episodes = load_episodes(self.root) if self.total_episodes > 0 else None
         self.stats = load_stats(self.root)
 
     def ensure_readable(self) -> None:

src/lerobot/envs/libero.py

@@ -384,7 +384,12 @@ class LiberoEnv(gym.Env):
 
     def close(self):
         if self._env is not None:
-            self._env.close()
+            try:
+                self._env.close()
+            finally:
+                # LIBERO deletes its inner env on close, so this wrapper must
+                # be recreated before the next reset.
+                self._env = None
 
 
 def _make_env_fns(

src/lerobot/envs/robotwin.py

@@ -384,7 +384,9 @@ class RoboTwinEnv(gym.Env):
 
         self._env: Any | None = None  # deferred — created on first reset() inside worker
         self._step_count: int = 0
-        self._black_frame = np.zeros((self.observation_height, self.observation_width, 3), dtype=np.uint8)
+        self._black_frame: np.ndarray = np.zeros(
+            (self.observation_height, self.observation_width, 3), dtype=np.uint8
+        )
 
         image_spaces = {
             cam: spaces.Box(

src/lerobot/envs/vlabench.py

@@ -373,7 +373,7 @@ class VLABenchEnv(gym.Env):
 
         if action.shape[0] != 7:
             # Unknown layout — fall back to zero-pad so the sim doesn't crash.
-            padded = np.zeros(ctrl_dim, dtype=np.float64)
+            padded: np.ndarray = np.zeros(ctrl_dim, dtype=np.float64)
             padded[: min(action.shape[0], ctrl_dim)] = action[:ctrl_dim]
             return padded
 

src/lerobot/policies/evo1/processor_evo1.py

@@ -302,6 +302,33 @@ def _pad_evo1_stats(
     return padded_stats
 
 
+def _refresh_evo1_normalization_steps(
+    config: Evo1Config,
+    preprocessor: PolicyProcessorPipeline,
+    postprocessor: PolicyProcessorPipeline,
+) -> None:
+    """Re-pad checkpoint-loaded (un)normalizer stats/features to EVO1's fixed widths.
+
+    Loading a checkpoint injects the raw dataset stats (unpadded to max_state_dim/max_action_dim)
+    into the (un)normalizer via the generic override path in make_pre_post_processors. Those stats
+    and their declared features must be re-padded/reshaped to EVO1's fixed widths, otherwise
+    normalization fails against the padded state/action tensors (e.g. state padded to 24 vs. 8-dim
+    LIBERO stats). Padding is a no-op when stats are already at the target width.
+    """
+    normalization_features = _evo1_normalization_features(config)
+    action_features = _evo1_action_features(config)
+    for step in preprocessor.steps:
+        if isinstance(step, NormalizerProcessorStep):
+            step.features = normalization_features
+            step.stats = _pad_evo1_stats(config, step.stats)
+            step.to(device=step.device, dtype=step.dtype)
+    for step in postprocessor.steps:
+        if isinstance(step, UnnormalizerProcessorStep):
+            step.features = action_features
+            step.stats = _pad_evo1_stats(config, step.stats)
+            step.to(device=step.device, dtype=step.dtype)
+
+
 def reconcile_evo1_processors(
     config: Evo1Config,
     preprocessor: PolicyProcessorPipeline,
@@ -309,16 +336,19 @@ def reconcile_evo1_processors(
 ) -> tuple[PolicyProcessorPipeline, PolicyProcessorPipeline]:
     """Reconcile checkpoint-loaded pipelines with the current EVO1 config.
 
-    Two things cannot be restored from a serialized pipeline alone: the EVO1 batch converter
-    (converters are plain functions and are never serialized), and eval-time CLI overrides of the
-    action postprocessing flags (`postprocess_action_dim`, `binarize_gripper`, `gripper_*`). This
-    restores the converter and rebuilds the action step from the current config so those overrides
-    take effect.
+    Three things cannot be restored from a serialized pipeline alone: the EVO1 batch converter
+    (converters are plain functions and are never serialized), eval-time CLI overrides of the
+    action postprocessing flags (`postprocess_action_dim`, `binarize_gripper`, `gripper_*`), and the
+    (un)normalizer stats/features when the generic override path injects raw, unpadded dataset
+    stats. This restores the converter, re-pads the normalization stats to EVO1's fixed widths, and
+    rebuilds the action step from the current config so those overrides take effect.
     """
     # Pipelines reloaded from a checkpoint come back with the default batch converter, which drops
     # non-observation extras (embodiment_id, state_mask, custom task fields) needed by EVO1.
     preprocessor.to_transition = evo1_batch_to_transition
 
+    _refresh_evo1_normalization_steps(config, preprocessor, postprocessor)
+
     action_step = Evo1ActionProcessorStep(
         action_dim=_evo1_action_dim(config),
         binarize_gripper=config.binarize_gripper,

src/lerobot/rl/buffer.py

@@ -18,7 +18,7 @@ import functools
 import threading
 from collections.abc import Callable, Sequence
 from contextlib import suppress
-from typing import TypedDict
+from typing import NotRequired, TypedDict
 
 import torch
 import torch.nn.functional as F  # noqa: N812
@@ -36,7 +36,7 @@ class BatchTransition(TypedDict):
     next_state: dict[str, torch.Tensor]
     done: torch.Tensor
     truncated: torch.Tensor
-    complementary_info: dict[str, torch.Tensor | float | int] | None = None
+    complementary_info: NotRequired[dict[str, torch.Tensor | float | int] | None]
 
 
 def random_crop_vectorized(images: torch.Tensor, output_size: tuple) -> torch.Tensor:

src/lerobot/robots/so_follower/config_so_follower.py

@@ -46,6 +46,12 @@ class SOFollowerConfig:
     position_i_coefficient: int = 0
     position_d_coefficient: int = 32
 
+    # Number of extra attempts when a `sync_read` of the motors fails. Feetech buses can occasionally
+    # return a corrupted status packet ("Incorrect status packet!"), especially when several joints move
+    # at once, which otherwise aborts the control loop. Retries are immediate (no sleep) and only happen on
+    # failure, so the steady-state read cost is unchanged.
+    num_read_retries: int = 2
+
 
 @RobotConfig.register_subclass("so101_follower")
 @RobotConfig.register_subclass("so100_follower")

src/lerobot/robots/so_follower/robot_kinematic_processor.py

@@ -510,10 +510,10 @@ class ForwardKinematicsJointsToEEAction(RobotActionProcessorStep):
         # We only use the ee pose in the dataset, so we don't need the joint positions
         for n in self.motor_names:
             features[PipelineFeatureType.ACTION].pop(f"{n}.pos", None)
-        # We specify the dataset features of this step that we want to be stored in the dataset
+        # Store end-effector features as actions in the dataset schema
         for k in ["x", "y", "z", "wx", "wy", "wz", "gripper_pos"]:
             features[PipelineFeatureType.ACTION][f"ee.{k}"] = PolicyFeature(
-                type=FeatureType.STATE, shape=(1,)
+                type=FeatureType.ACTION, shape=(1,)
             )
         return features
 

src/lerobot/robots/so_follower/so_follower.py

@@ -180,7 +180,7 @@ class SOFollower(Robot):
     def get_observation(self) -> RobotObservation:
         # Read arm position
         start = time.perf_counter()
-        obs_dict = self.bus.sync_read("Present_Position")
+        obs_dict = self.bus.sync_read("Present_Position", num_retry=self.config.num_read_retries)
         obs_dict = {f"{motor}.pos": val for motor, val in obs_dict.items()}
         dt_ms = (time.perf_counter() - start) * 1e3
         logger.debug(f"{self} read state: {dt_ms:.1f}ms")
@@ -221,7 +221,7 @@ class SOFollower(Robot):
         # Cap goal position when too far away from present position.
         # /!\ Slower fps expected due to reading from the follower.
         if self.config.max_relative_target is not None:
-            present_pos = self.bus.sync_read("Present_Position")
+            present_pos = self.bus.sync_read("Present_Position", num_retry=self.config.num_read_retries)
             goal_present_pos = {key: (g_pos, present_pos[key]) for key, g_pos in goal_pos.items()}
             goal_pos = ensure_safe_goal_position(goal_present_pos, self.config.max_relative_target)
 

src/lerobot/scripts/augment_dataset_quantile_stats.py

@@ -36,6 +36,7 @@ python src/lerobot/scripts/augment_dataset_quantile_stats.py \
 import argparse
 import concurrent.futures
 import logging
+import os
 from pathlib import Path
 
 import numpy as np
@@ -52,6 +53,7 @@ from lerobot.datasets import (
     get_feature_stats,
     write_stats,
 )
+from lerobot.datasets.compute_stats import sample_indices
 from lerobot.utils.utils import init_logging
 
 
@@ -77,12 +79,14 @@ def has_quantile_stats(stats: dict[str, dict] | None, quantile_list_keys: list[s
     return False
 
 
-def process_single_episode(dataset: LeRobotDataset, episode_idx: int) -> dict:
+def process_single_episode(dataset: LeRobotDataset, episode_idx: int, use_sampling: bool = True) -> dict:
     """Process a single episode and return its statistics.
 
     Args:
         dataset: The LeRobot dataset
         episode_idx: Index of the episode to process
+        use_sampling: If True, sub-sample image/video frames per episode to bound
+            memory. If False, use every frame (exact, higher memory).
 
     Returns:
         Dictionary containing episode statistics
@@ -92,16 +96,31 @@ def process_single_episode(dataset: LeRobotDataset, episode_idx: int) -> dict:
     start_idx = dataset.meta.episodes[episode_idx]["dataset_from_index"]
     end_idx = dataset.meta.episodes[episode_idx]["dataset_to_index"]
 
-    collected_data: dict[str, list] = {}
-    for idx in range(start_idx, end_idx):
-        item = dataset[idx]
-        for key, value in item.items():
-            if key not in dataset.features:
-                continue
+    episode_len = end_idx - start_idx
 
-            if key not in collected_data:
-                collected_data[key] = []
-            collected_data[key].append(value)
+    # Images/video are the memory hog, so sub-sample those frames per episode;
+    # numeric columns are cheap, so read them in full (exact).
+    image_keys = [k for k in dataset.features if dataset.features[k]["dtype"] in ("image", "video")]
+    numeric_keys = [
+        k for k in dataset.features if dataset.features[k]["dtype"] not in ("image", "video", "string")
+    ]
+
+    collected_data: dict[str, list] = {}
+
+    # Numeric features: every frame, read directly from the underlying table.
+    if numeric_keys:
+        numeric_cols = dataset.hf_dataset.select_columns(numeric_keys)[start_idx:end_idx]
+        for key in numeric_keys:
+            collected_data[key] = [torch.as_tensor(v) for v in numeric_cols[key]]
+
+    # Image/video features: decode only a sampled subset of frames.
+    if image_keys:
+        sampled_offsets = sample_indices(episode_len) if use_sampling else list(range(episode_len))
+        for offset in sampled_offsets:
+            item = dataset[start_idx + offset]
+            for key in image_keys:
+                if key in item:
+                    collected_data.setdefault(key, []).append(item[key])
 
     ep_stats = {}
     for key, data_list in collected_data.items():
@@ -131,11 +150,13 @@ def process_single_episode(dataset: LeRobotDataset, episode_idx: int) -> dict:
     return ep_stats
 
 
-def compute_quantile_stats_for_dataset(dataset: LeRobotDataset) -> dict[str, dict]:
+def compute_quantile_stats_for_dataset(dataset: LeRobotDataset, use_sampling: bool = True) -> dict[str, dict]:
     """Compute quantile statistics for all episodes in the dataset.
 
     Args:
         dataset: The LeRobot dataset to compute statistics for
+        use_sampling: If True, sub-sample image/video frames per episode to bound
+            memory. If False, use every frame (exact, higher memory).
 
     Returns:
         Dictionary containing aggregated statistics with quantiles
@@ -153,15 +174,15 @@ def compute_quantile_stats_for_dataset(dataset: LeRobotDataset) -> dict[str, dic
     if has_videos:
         logging.info("Dataset contains video keys - using sequential processing for thread safety")
         for episode_idx in tqdm(range(dataset.num_episodes), desc="Processing episodes"):
-            ep_stats = process_single_episode(dataset, episode_idx)
+            ep_stats = process_single_episode(dataset, episode_idx, use_sampling)
             episode_stats_list.append(ep_stats)
     else:
         logging.info("Dataset has no video keys - using parallel processing for better performance")
-        max_workers = min(dataset.num_episodes, 16)
+        max_workers = min(dataset.num_episodes, int(os.environ.get("LEROBOT_STATS_MAX_WORKERS", 16)))
 
         with concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) as executor:
             future_to_episode = {
-                executor.submit(process_single_episode, dataset, episode_idx): episode_idx
+                executor.submit(process_single_episode, dataset, episode_idx, use_sampling): episode_idx
                 for episode_idx in range(dataset.num_episodes)
             }
 
@@ -188,6 +209,7 @@ def augment_dataset_with_quantile_stats(
     repo_id: str,
     root: str | Path | None = None,
     overwrite: bool = False,
+    use_sampling: bool = True,
 ) -> None:
     """Augment a dataset with quantile statistics if they are missing.
 
@@ -195,6 +217,8 @@ def augment_dataset_with_quantile_stats(
         repo_id: Repository ID of the dataset
         root: Local root directory for the dataset
         overwrite: Overwrite existing quantile statistics if they already exist
+        use_sampling: If True, sub-sample image/video frames per episode to bound
+            memory. If False, use every frame (exact, higher memory).
     """
     logging.info(f"Loading dataset: {repo_id}")
     dataset = LeRobotDataset(
@@ -208,7 +232,7 @@ def augment_dataset_with_quantile_stats(
 
     logging.info("Dataset does not contain quantile statistics. Computing them now...")
 
-    new_stats = compute_quantile_stats_for_dataset(dataset)
+    new_stats = compute_quantile_stats_for_dataset(dataset, use_sampling=use_sampling)
 
     logging.info("Updating dataset metadata with new quantile statistics")
     dataset.meta.stats = new_stats
@@ -248,6 +272,14 @@ def main():
         action="store_true",
         help="Overwrite existing quantile statistics if they already exist",
     )
+    parser.add_argument(
+        "--no-sampling",
+        action="store_true",
+        help=(
+            "Compute stats over every frame (exact, higher memory). By default, "
+            "image/video frames are sub-sampled per episode to bound memory."
+        ),
+    )
 
     args = parser.parse_args()
     root = Path(args.root) if args.root else None
@@ -258,6 +290,7 @@ def main():
         repo_id=args.repo_id,
         root=root,
         overwrite=args.overwrite,
+        use_sampling=not args.no_sampling,
     )
 
 

src/lerobot/scripts/lerobot_eval.py

@@ -564,7 +564,7 @@ def eval_policy(
         if seeds:
             all_seeds.extend(seeds)
         else:
-            all_seeds.append(None)
+            all_seeds.extend([None] * env.num_envs)
 
         # FIXME: episode_data is either None or it doesn't exist
         if return_episode_data:

src/lerobot/scripts/lerobot_find_cameras.py

@@ -28,7 +28,6 @@ lerobot-find-cameras
 # NOTE(Steven): macOS cameras sometimes report different FPS at init time, not an issue here as we don't specify FPS when opening the cameras, but the information displayed might not be truthful.
 
 import argparse
-import concurrent.futures
 import logging
 import time
 from pathlib import Path
@@ -133,7 +132,7 @@ def save_image(
     camera_identifier: str | int,
     images_dir: Path,
     camera_type: str,
-):
+) -> None:
     """
     Saves a single image to disk using Pillow. Handles color conversion if necessary.
     """
@@ -152,7 +151,7 @@ def save_image(
         logger.error(f"Failed to save image for camera {camera_identifier} (type {camera_type}): {e}")
 
 
-def create_camera_instance(cam_meta: dict[str, Any]) -> dict[str, Any] | None:
+def create_camera_instance(cam_meta: dict[str, Any], *, warmup_s: int = 1) -> dict[str, Any] | None:
     """Create and connect to a camera instance based on metadata."""
     cam_type = cam_meta.get("type")
     cam_id = cam_meta.get("id")
@@ -165,12 +164,14 @@ def create_camera_instance(cam_meta: dict[str, Any]) -> dict[str, Any] | None:
             cv_config = OpenCVCameraConfig(
                 index_or_path=cam_id,
                 color_mode=ColorMode.RGB,
+                warmup_s=warmup_s,
             )
             instance = OpenCVCamera(cv_config)
         elif cam_type == "RealSense":
             rs_config = RealSenseCameraConfig(
                 serial_number_or_name=cam_id,
                 color_mode=ColorMode.RGB,
+                warmup_s=warmup_s,
             )
             instance = RealSenseCamera(rs_config)
         else:
@@ -188,9 +189,7 @@ def create_camera_instance(cam_meta: dict[str, Any]) -> dict[str, Any] | None:
         return None
 
 
-def process_camera_image(
-    cam_dict: dict[str, Any], output_dir: Path, current_time: float
-) -> concurrent.futures.Future | None:
+def process_camera_image(cam_dict: dict[str, Any], output_dir: Path, current_time: float) -> None:
     """Capture and process an image from a single camera."""
     cam = cam_dict["instance"]
     meta = cam_dict["meta"]
@@ -200,7 +199,7 @@ def process_camera_image(
     try:
         image_data = cam.read()
 
-        return save_image(
+        save_image(
             image_data,
             cam_id_str,
             output_dir,
@@ -215,21 +214,21 @@ def process_camera_image(
     return None
 
 
-def cleanup_cameras(cameras_to_use: list[dict[str, Any]]):
+def cleanup_camera(cam_dict: dict[str, Any]) -> None:
     """Disconnect all cameras."""
-    logger.info(f"Disconnecting {len(cameras_to_use)} cameras...")
-    for cam_dict in cameras_to_use:
-        try:
-            if cam_dict["instance"] and cam_dict["instance"].is_connected:
-                cam_dict["instance"].disconnect()
-        except Exception as e:
-            logger.error(f"Error disconnecting camera {cam_dict['meta'].get('id')}: {e}")
+    logger.info(f"Disconnecting camera with ID {cam_dict['meta'].get('id')}...")
+    try:
+        if cam_dict["instance"] and cam_dict["instance"].is_connected:
+            cam_dict["instance"].disconnect()
+    except Exception as e:
+        logger.error(f"Error disconnecting camera {cam_dict['meta'].get('id')}: {e}")
 
 
 def save_images_from_all_cameras(
     output_dir: Path,
     record_time_s: float = 2.0,
     camera_type: str | None = None,
+    warmup_s: int = 1,
 ):
     """
     Connects to detected cameras (optionally filtered by type) and saves images from each.
@@ -240,6 +239,7 @@ def save_images_from_all_cameras(
         record_time_s: Duration in seconds to record images.
         camera_type: Optional string to filter cameras ("realsense" or "opencv").
                             If None, uses all detected cameras.
+        warmup_s: Duration in seconds to warmup camera before recording images.
     """
     output_dir.mkdir(parents=True, exist_ok=True)
     logger.info(f"Saving images to {output_dir}")
@@ -249,40 +249,24 @@ def save_images_from_all_cameras(
         logger.warning("No cameras detected matching the criteria. Cannot save images.")
         return
 
-    cameras_to_use = []
-    for cam_meta in all_camera_metadata:
-        camera_instance = create_camera_instance(cam_meta)
-        if camera_instance:
-            cameras_to_use.append(camera_instance)
+    logger.info(
+        f"Starting image capture for {record_time_s} seconds from {len(all_camera_metadata)} cameras."
+    )
 
-    if not cameras_to_use:
-        logger.warning("No cameras could be connected. Aborting image save.")
-        return
-
-    logger.info(f"Starting image capture for {record_time_s} seconds from {len(cameras_to_use)} cameras.")
-    start_time = time.perf_counter()
-
-    with concurrent.futures.ThreadPoolExecutor(max_workers=len(cameras_to_use) * 2) as executor:
-        try:
+    try:
+        for cam_meta in all_camera_metadata:
+            cam_dict = create_camera_instance(cam_meta, warmup_s=warmup_s)
+            if cam_dict is None:
+                continue
+            start_time = time.perf_counter()
             while time.perf_counter() - start_time < record_time_s:
-                futures = []
                 current_capture_time = time.perf_counter()
-
-                for cam_dict in cameras_to_use:
-                    future = process_camera_image(cam_dict, output_dir, current_capture_time)
-                    if future:
-                        futures.append(future)
-
-                if futures:
-                    concurrent.futures.wait(futures)
-
-        except KeyboardInterrupt:
-            logger.info("Capture interrupted by user.")
-        finally:
-            print("\nFinalizing image saving...")
-            executor.shutdown(wait=True)
-            cleanup_cameras(cameras_to_use)
-            print(f"Image capture finished. Images saved to {output_dir}")
+                process_camera_image(cam_dict, output_dir, current_capture_time)
+            cleanup_camera(cam_dict)
+    except KeyboardInterrupt:
+        logger.info("Capture interrupted by user.")
+    finally:
+        print(f"Image capture finished. Images saved to {output_dir}")
 
 
 def main():
@@ -291,7 +275,6 @@ def main():
     parser = argparse.ArgumentParser(
         description="Unified camera utility script for listing cameras and capturing images."
     )
-
     parser.add_argument(
         "camera_type",
         type=str,
@@ -309,8 +292,14 @@ def main():
     parser.add_argument(
         "--record-time-s",
         type=float,
-        default=6.0,
-        help="Time duration to attempt capturing frames. Default: 6 seconds.",
+        default=2.0,
+        help="Time duration to attempt capturing frames. Default: 2 seconds.",
+    )
+    parser.add_argument(
+        "--warmup-s",
+        type=int,
+        default=1,
+        help="Time duration to warmup camera before attempting to capture frames. Default: 1 second.",
     )
     args = parser.parse_args()
     save_images_from_all_cameras(**vars(args))

src/lerobot/scripts/lerobot_train.py

@@ -22,7 +22,8 @@ import dataclasses
 import logging
 import sys
 import time
-from contextlib import nullcontext
+from collections.abc import Iterator
+from contextlib import contextmanager, nullcontext
 from pprint import pformat
 from typing import TYPE_CHECKING, Any
 
@@ -76,6 +77,20 @@ else:
 from .lerobot_eval import eval_policy_all
 
 
+@contextmanager
+def _make_eval_envs(cfg: TrainPipelineConfig) -> Iterator[dict[str, dict[int, Any]]]:
+    """Create evaluation environments for one run and always dispose of them."""
+    envs = make_env(
+        cfg.env,
+        n_envs=cfg.eval.batch_size,
+        use_async_envs=cfg.eval.use_async_envs,
+    )
+    try:
+        yield envs
+    finally:
+        close_envs(envs)
+
+
 def _dataloader_worker_kwargs(cfg: TrainPipelineConfig) -> dict[str, Any]:
     """Return worker-only DataLoader options, disabling them for single-process loading."""
     workers_enabled = cfg.num_workers > 0
@@ -280,14 +295,6 @@ def train(cfg: TrainPipelineConfig, accelerator: "Accelerator | None" = None):
     if not is_main_process:
         dataset, eval_dataset = make_train_eval_datasets(cfg)
 
-    # Create environment used for evaluating checkpoints during training on simulation data.
-    # On real-world data, no need to create an environment as evaluations are done outside train.py,
-    # using the eval.py instead, with gym_dora environment and dora-rs.
-    eval_env = None
-    if cfg.env_eval_freq > 0 and cfg.env is not None and is_main_process:
-        logging.info("Creating env")
-        eval_env = make_env(cfg.env, n_envs=cfg.eval.batch_size, use_async_envs=cfg.eval.use_async_envs)
-
     if cfg.is_reward_model_training:
         if is_main_process:
             logging.info("Creating reward model")
@@ -695,7 +702,7 @@ def train(cfg: TrainPipelineConfig, accelerator: "Accelerator | None" = None):
             if is_main_process:
                 step_id = get_step_identifier(step, cfg.steps)
                 logging.info(f"Eval policy at step {step}")
-                with torch.no_grad(), accelerator.autocast():
+                with _make_eval_envs(cfg) as eval_env, torch.no_grad(), accelerator.autocast():
                     eval_info = eval_policy_all(
                         envs=eval_env,  # dict[suite][task_id] -> vec_env
                         policy=accelerator.unwrap_model(policy),
@@ -743,9 +750,6 @@ def train(cfg: TrainPipelineConfig, accelerator: "Accelerator | None" = None):
     if is_main_process:
         progbar.close()
 
-    if eval_env:
-        close_envs(eval_env)
-
     is_fsdp = accelerator.distributed_type == DistributedType.FSDP
     model_state_dict = accelerator.get_state_dict(policy) if is_fsdp else None
     if is_main_process:

src/lerobot/teleoperators/phone/teleop_phone.py

@@ -171,7 +171,13 @@ class IOSPhone(BasePhone, Teleoperator):
         # HEBI provides orientation in w, x, y, z format.
         # Scipy's Rotation expects x, y, z, w.
         quat_xyzw = np.concatenate((ar_quat[1:], [ar_quat[0]]))  # wxyz to xyzw
-        rot = Rotation.from_quat(quat_xyzw)
+        # ARKit can emit zero/NaN quaternions before tracking is ready or on a
+        # dropped packet. Rotation.from_quat now rejects those; degrade the same
+        # way as a missing pose so teleop stays alive mid-session.
+        try:
+            rot = Rotation.from_quat(quat_xyzw)
+        except ValueError:
+            return False, None, None, None
         pos = ar_pos - rot.apply(self.config.camera_offset)
         return True, pos, rot, pose
 

src/lerobot/teleoperators/so_leader/config_so_leader.py

@@ -29,6 +29,12 @@ class SOLeaderConfig:
     # Whether to use degrees for angles
     use_degrees: bool = True
 
+    # Number of extra attempts when a `sync_read` of the motors fails. Feetech buses can occasionally
+    # return a corrupted status packet ("Incorrect status packet!"), especially when several joints move
+    # at once, which otherwise aborts the teleoperation loop. Retries are immediate (no sleep) and only
+    # happen on failure, so the steady-state read cost is unchanged.
+    num_read_retries: int = 2
+
 
 @TeleoperatorConfig.register_subclass("so101_leader")
 @TeleoperatorConfig.register_subclass("so100_leader")

src/lerobot/teleoperators/so_leader/so_leader.py

@@ -145,7 +145,7 @@ class SOLeader(Teleoperator):
     @check_if_not_connected
     def get_action(self) -> dict[str, float]:
         start = time.perf_counter()
-        action = self.bus.sync_read("Present_Position")
+        action = self.bus.sync_read("Present_Position", num_retry=self.config.num_read_retries)
         action = {f"{motor}.pos": val for motor, val in action.items()}
         dt_ms = (time.perf_counter() - start) * 1e3
         logger.debug(f"{self} read action: {dt_ms:.1f}ms")

src/lerobot/transforms/transforms.py

@@ -41,7 +41,7 @@ class RandomSubsetApply(Transform):
 
     def __init__(
         self,
-        transforms: Sequence[Callable],
+        transforms: Sequence[Callable[..., Any]],
         p: list[float] | None = None,
         n_subset: int | None = None,
         random_order: bool = False,
@@ -50,7 +50,7 @@ class RandomSubsetApply(Transform):
         if not isinstance(transforms, Sequence):
             raise TypeError("Argument transforms should be a sequence of callables")
         if p is None:
-            p = [1] * len(transforms)
+            p = [1.0] * len(transforms)
         elif len(p) != len(transforms):
             raise ValueError(
                 f"Length of p doesn't match the number of transforms: {len(p)} != {len(transforms)}"
@@ -69,7 +69,7 @@ class RandomSubsetApply(Transform):
         self.n_subset = n_subset
         self.random_order = random_order
 
-        self.selected_transforms = None
+        self.selected_transforms: list[Callable[..., Any]] = []
 
     def forward(self, *inputs: Any) -> Any:
         needs_unpacking = len(inputs) > 1
@@ -119,7 +119,7 @@ class SharpnessJitter(Transform):
         super().__init__()
         self.sharpness = self._check_input(sharpness)
 
-    def _check_input(self, sharpness):
+    def _check_input(self, sharpness: float | Sequence[float]) -> tuple[float, float]:
         if isinstance(sharpness, (int | float)):
             if sharpness < 0:
                 raise ValueError("If sharpness is a single number, it must be non negative.")
@@ -215,7 +215,7 @@ class ImageTransformsConfig:
     )
 
 
-def make_transform_from_config(cfg: ImageTransformConfig):
+def make_transform_from_config(cfg: ImageTransformConfig) -> Transform:
     if cfg.type == "SharpnessJitter":
         return SharpnessJitter(**cfg.kwargs)
 
@@ -236,8 +236,8 @@ class ImageTransforms(Transform):
         super().__init__()
         self._cfg = cfg
 
-        self.weights = []
-        self.transforms = {}
+        self.weights: list[float] = []
+        self.transforms: dict[str, Transform] = {}
         for tf_name, tf_cfg in cfg.tfs.items():
             if tf_cfg.weight <= 0.0:
                 continue

src/lerobot/utils/device_utils.py

@@ -37,16 +37,25 @@ def auto_select_torch_device() -> torch.device:
 
 # TODO(Steven): Remove log. log shouldn't be an argument, this should be handled by the logger level
 def get_safe_torch_device(try_device: str, log: bool = False) -> torch.device:
-    """Given a string, return a torch.device with checks on whether the device is available."""
+    """Given a string, return a torch.device with checks on whether the device is available.
+
+    Raises:
+        ValueError: If the requested device family is known but not available on
+            this machine (``AssertionError`` was previously used and is easy to
+            mistake for a programmer bug under ``python -O`` where asserts vanish).
+    """
     try_device = str(try_device)
     if try_device.startswith("cuda"):
-        assert torch.cuda.is_available()
+        if not torch.cuda.is_available():
+            raise ValueError(f"Requested device {try_device!r} but CUDA is not available.")
         device = torch.device(try_device)
     elif try_device == "mps":
-        assert torch.backends.mps.is_available()
+        if not torch.backends.mps.is_available():
+            raise ValueError("Requested device 'mps' but MPS is not available.")
         device = torch.device("mps")
     elif try_device == "xpu":
-        assert torch.xpu.is_available()
+        if not torch.xpu.is_available():
+            raise ValueError("Requested device 'xpu' but XPU is not available.")
         device = torch.device("xpu")
     elif try_device == "cpu":
         device = torch.device("cpu")

src/lerobot/utils/io_utils.py

@@ -32,21 +32,21 @@ def load_json(fpath: Path) -> Any:
     Returns:
         Any: The data loaded from the JSON file.
     """
-    with open(fpath) as f:
+    with open(fpath, encoding="utf-8") as f:
         return json.load(f)
 
 
-def write_json(data: dict, fpath: Path) -> None:
-    """Write data to a JSON file.
+def write_json(data: JsonLike, fpath: Path) -> None:
+    """Write JSON-serializable data to a file.
 
     Creates parent directories if they don't exist.
 
     Args:
-        data (dict): The dictionary to write.
+        data: JSON-serializable data to write.
         fpath (Path): The path to the output JSON file.
     """
     fpath.parent.mkdir(exist_ok=True, parents=True)
-    with open(fpath, "w") as f:
+    with open(fpath, "w", encoding="utf-8") as f:
         json.dump(data, f, indent=4, ensure_ascii=False)
 
 

src/lerobot/utils/robot_utils.py

@@ -30,6 +30,10 @@ def precise_sleep(seconds: float, spin_threshold: float = 0.010, sleep_margin: f
     """
     if seconds <= 0:
         return
+    if spin_threshold < 0:
+        raise ValueError(f"spin_threshold must be >= 0, got {spin_threshold}")
+    if sleep_margin < 0:
+        raise ValueError(f"sleep_margin must be >= 0, got {sleep_margin}")
 
     system = platform.system()
     # On macOS and Windows the scheduler / sleep granularity can make

src/lerobot/utils/rotation.py

@@ -29,10 +29,13 @@ class Rotation:
     def __init__(self, quat: np.ndarray) -> None:
         """Initialize rotation from quaternion [x, y, z, w]."""
         self._quat = np.asarray(quat, dtype=float)
-        # Normalize quaternion
+        if self._quat.shape != (4,):
+            raise ValueError(f"Quaternion must have shape (4,), got {self._quat.shape}")
+        # Normalize quaternion. Reject the zero vector — it has no orientation.
         norm = np.linalg.norm(self._quat)
-        if norm > 0:
-            self._quat = self._quat / norm
+        if norm <= 0.0 or not np.isfinite(norm):
+            raise ValueError(f"Quaternion must be a non-zero finite vector; got {self._quat} (norm={norm})")
+        self._quat = self._quat / norm
 
     @classmethod
     def from_rotvec(cls, rotvec: np.ndarray) -> "Rotation":

src/lerobot/utils/transition.py

@@ -14,7 +14,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from typing import TypedDict
+from typing import NotRequired, TypedDict
 
 import torch
 
@@ -28,7 +28,7 @@ class Transition(TypedDict):
     next_state: dict[str, torch.Tensor]
     done: bool
     truncated: bool
-    complementary_info: dict[str, torch.Tensor | float | int] | None = None
+    complementary_info: NotRequired[dict[str, torch.Tensor | float | int] | None]
 
 
 def move_transition_to_device(transition: Transition, device: str = "cpu") -> Transition:

src/lerobot/utils/utils.py

@@ -24,7 +24,6 @@ import sys
 import time
 from collections.abc import Iterator
 from copy import copy, deepcopy
-from datetime import datetime
 from pathlib import Path
 from statistics import mean
 from typing import TYPE_CHECKING, Any
@@ -61,14 +60,16 @@ def init_logging(
         accelerator: Optional Accelerator instance (for multi-GPU detection)
     """
 
-    def custom_format(record: logging.LogRecord) -> str:
-        dt = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
-        fnameline = f"{record.pathname}:{record.lineno}"
-        pid_str = f"[PID: {os.getpid()}] " if display_pid else ""
-        return f"{record.levelname} {pid_str}{dt} {fnameline[-15:]:>15} {record.getMessage()}"
+    class LeRobotFormatter(logging.Formatter):
+        def format(self, record: logging.LogRecord) -> str:
+            record.lerobot_location = f"{record.pathname}:{record.lineno}"[-15:]
+            record.lerobot_pid = f"[PID: {os.getpid()}] " if display_pid else ""
+            return super().format(record)
 
-    formatter = logging.Formatter()
-    formatter.format = custom_format
+    formatter = LeRobotFormatter(
+        "%(levelname)s %(lerobot_pid)s%(asctime)s %(lerobot_location)15s %(message)s",
+        datefmt="%Y-%m-%d %H:%M:%S",
+    )
 
     logger = logging.getLogger()
     logger.setLevel(logging.NOTSET)
@@ -133,10 +134,13 @@ def say(text: str, blocking: bool = False):
     else:
         raise RuntimeError("Unsupported operating system for text-to-speech.")
 
-    if blocking:
-        subprocess.run(cmd, check=True)
-    else:
-        subprocess.Popen(cmd, creationflags=subprocess.CREATE_NO_WINDOW if system == "Windows" else 0)
+    try:
+        if blocking:
+            subprocess.run(cmd, check=True, timeout=5)
+        else:
+            subprocess.Popen(cmd, creationflags=subprocess.CREATE_NO_WINDOW if system == "Windows" else 0)
+    except (FileNotFoundError, subprocess.TimeoutExpired) as e:
+        logging.warning("Text-to-speech command failed: %s | Error: %s", cmd, e)
 
 
 def log_say(text: str, play_sounds: bool = True, blocking: bool = False):

tests/cameras/test_realsense.py

@@ -322,15 +322,16 @@ def test_get_color_sensor_prefers_rgb_camera():
     assert camera._get_color_sensor() is rgb
 
 
-def test_get_color_sensor_falls_back_to_stereo_module():
-    """D405 has no separate RGB module; color comes from Stereo Module."""
+def test_get_color_sensor_raises_without_dedicated_rgb_module():
+    """D405 has no separate RGB module; we refuse to touch the shared Stereo Module."""
     config = RealSenseCameraConfig(serial_number_or_name="042")
     camera = RealSenseCamera(config)
 
     stereo = _make_mock_sensor("Stereo Module")
     _attach_mock_color_sensor(camera, stereo)
 
-    assert camera._get_color_sensor() is stereo
+    with pytest.raises(RuntimeError, match="dedicated 'RGB Camera' module"):
+        camera._get_color_sensor()
 
 
 def test_get_color_sensor_raises_with_available_sensors():

tests/datasets/test_augment_quantile_stats.py

@@ -0,0 +1,104 @@
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# 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 numpy as np
+import pytest
+
+pytest.importorskip("datasets", reason="datasets is required (install lerobot[dataset])")
+
+from lerobot.scripts.augment_dataset_quantile_stats import (
+    compute_quantile_stats_for_dataset,
+    has_quantile_stats,
+)
+
+
+def _numeric_keys(dataset):
+    return [k for k, v in dataset.features.items() if v["dtype"] not in ("image", "video", "string")]
+
+
+def _image_keys(dataset):
+    return [k for k, v in dataset.features.items() if v["dtype"] in ("image", "video")]
+
+
+def test_numeric_stats_are_unaffected_by_sampling(tmp_path, lerobot_dataset_factory):
+    """Sampling only touches image/video frames; numeric features are read in
+    full either way, so their stats must be identical with and without sampling."""
+    dataset = lerobot_dataset_factory(
+        root=tmp_path / "ds", total_episodes=2, total_frames=400, use_videos=False
+    )
+
+    exact = compute_quantile_stats_for_dataset(dataset, use_sampling=False)
+    sampled = compute_quantile_stats_for_dataset(dataset, use_sampling=True)
+
+    numeric_keys = _numeric_keys(dataset)
+    assert numeric_keys, "fixture should expose numeric features"
+    for key in numeric_keys:
+        if key not in exact:
+            continue
+        for stat in ("mean", "std", "q01", "q50", "q99"):
+            if stat in exact[key]:
+                np.testing.assert_allclose(
+                    sampled[key][stat],
+                    exact[key][stat],
+                    rtol=1e-6,
+                    atol=1e-6,
+                    err_msg=f"numeric feature '{key}' stat '{stat}' changed under sampling",
+                )
+
+
+def test_image_sampling_reduces_data_but_keeps_stats_close(tmp_path, lerobot_dataset_factory):
+    """For images, sampling should reduce the number of samples considered while
+    keeping the resulting statistics close to the exact ones."""
+    dataset = lerobot_dataset_factory(
+        root=tmp_path / "ds", total_episodes=2, total_frames=400, use_videos=False
+    )
+
+    exact = compute_quantile_stats_for_dataset(dataset, use_sampling=False)
+    sampled = compute_quantile_stats_for_dataset(dataset, use_sampling=True)
+
+    image_keys = _image_keys(dataset)
+    assert image_keys, "fixture should expose at least one image feature"
+    for key in image_keys:
+        # sampling actually looked at fewer pixels
+        assert sampled[key]["count"][0] < exact[key]["count"][0]
+        # but per-channel mean stays close
+        np.testing.assert_allclose(
+            sampled[key]["mean"],
+            exact[key]["mean"],
+            rtol=0.15,
+            err_msg=f"image feature '{key}' mean drifted too far under sampling",
+        )
+
+
+def test_short_episodes_use_all_frames(tmp_path, lerobot_dataset_factory):
+    """With episodes shorter than the sampling floor, sampling is a no-op and
+    must produce exactly the same stats as the exact path."""
+    dataset = lerobot_dataset_factory(
+        root=tmp_path / "ds", total_episodes=2, total_frames=40, use_videos=False
+    )
+
+    exact = compute_quantile_stats_for_dataset(dataset, use_sampling=False)
+    sampled = compute_quantile_stats_for_dataset(dataset, use_sampling=True)
+
+    for key in _image_keys(dataset):
+        assert sampled[key]["count"][0] == exact[key]["count"][0]
+
+
+def test_quantile_stats_present_after_compute(tmp_path, lerobot_dataset_factory):
+    """The computed stats should contain quantile keys for the dataset."""
+    dataset = lerobot_dataset_factory(
+        root=tmp_path / "ds", total_episodes=2, total_frames=200, use_videos=False
+    )
+    stats = compute_quantile_stats_for_dataset(dataset, use_sampling=True)
+    assert has_quantile_stats(stats)

tests/datasets/test_lerobot_dataset.py

@@ -482,6 +482,20 @@ def test_add_frame_works_in_write_mode(tmp_path):
 # ── Resume mode ──────────────────────────────────────────────────────
 
 
+def test_resume_freshly_created_empty_dataset(tmp_path):
+    """resume() accepts a local dataset created before any episode was recorded."""
+    root = tmp_path / "resume_empty_ds"
+    LeRobotDataset.create(repo_id=DUMMY_REPO_ID, fps=DEFAULT_FPS, features=SIMPLE_FEATURES, root=root)
+
+    resumed = LeRobotDataset.resume(repo_id=DUMMY_REPO_ID, root=root)
+
+    assert isinstance(resumed.writer, DatasetWriter)
+    assert resumed.meta.total_episodes == 0
+    assert resumed.meta.total_frames == 0
+    assert resumed.meta.tasks is None
+    assert resumed.meta.episodes is None
+
+
 def test_resume_creates_writer(tmp_path):
     """After resume(), writer is a DatasetWriter."""
     root = tmp_path / "resume_ds"

tests/motors/test_feetech.py

@@ -294,6 +294,19 @@ def test__sync_read(addr, length, ids_values, mock_motors, dummy_motors):
     assert read_values == ids_values
 
 
+def test__sync_read_retries_after_transient_failure(mock_motors, dummy_motors):
+    addr, length, ids_values = (10, 4, {1: 1337})
+    stub = mock_motors.build_sync_read_stub(addr, length, ids_values, num_invalid_try=1)
+    bus = FeetechMotorsBus(port=mock_motors.port, motors=dummy_motors)
+    bus.connect(handshake=False)
+
+    read_values, read_comm = bus._sync_read(addr, length, list(ids_values), num_retry=1)
+
+    assert read_comm == scs.COMM_SUCCESS
+    assert read_values == ids_values
+    assert mock_motors.stubs[stub].calls == 2
+
+
 @pytest.mark.parametrize("raise_on_error", (True, False))
 def test__sync_read_comm(raise_on_error, mock_motors, dummy_motors):
     addr, length, ids_values = (10, 4, {1: 1337})

tests/policies/evo1/test_evo1.py

@@ -496,6 +496,60 @@ def test_evo1_processor_save_load_round_trip_applies_config_overrides(tmp_path):
     assert "embodiment_id" in processed
 
 
+def test_reconcile_evo1_processors_repads_overridden_stats(tmp_path):
+    """Loading a checkpoint and injecting raw (unpadded) dataset stats must be re-padded.
+
+    Regression test: lerobot-train passes the raw dataset stats as normalizer/unnormalizer
+    overrides when resuming from a checkpoint (e.g. stage2 from a stage1 checkpoint). Those stats
+    are at the dataset dims (e.g. LIBERO state=8/action=7), but EVO1 pads state/action to
+    max_state_dim/max_action_dim before normalization, so reconcile_evo1_processors must re-pad the
+    stats or normalization crashes with a shape mismatch.
+    """
+    config = make_config()
+    preprocessor, postprocessor = make_evo1_pre_post_processors(config, dataset_stats=make_stats())
+    preprocessor.save_pretrained(tmp_path)
+    postprocessor.save_pretrained(tmp_path)
+
+    # Reload with the generic override path injecting raw, unpadded dataset stats.
+    raw_stats = make_stats()
+    loaded_pre = PolicyProcessorPipeline.from_pretrained(
+        tmp_path,
+        config_filename=f"{POLICY_PREPROCESSOR_DEFAULT_NAME}.json",
+        overrides={"normalizer_processor": {"stats": raw_stats}},
+        to_transition=batch_to_transition,
+        to_output=transition_to_batch,
+    )
+    loaded_post = PolicyProcessorPipeline.from_pretrained(
+        tmp_path,
+        config_filename=f"{POLICY_POSTPROCESSOR_DEFAULT_NAME}.json",
+        overrides={"unnormalizer_processor": {"stats": raw_stats}},
+        to_transition=policy_action_to_transition,
+        to_output=transition_to_policy_action,
+    )
+
+    # Sanity: the override really injected unpadded stats before reconciliation.
+    normalizer = next(step for step in loaded_pre.steps if isinstance(step, NormalizerProcessorStep))
+    assert normalizer._tensor_stats[OBS_STATE]["min"].shape == (STATE_DIM,)
+
+    loaded_pre, loaded_post = reconcile_evo1_processors(config, loaded_pre, loaded_post)
+
+    normalizer = next(step for step in loaded_pre.steps if isinstance(step, NormalizerProcessorStep))
+    unnormalizer = next(step for step in loaded_post.steps if isinstance(step, UnnormalizerProcessorStep))
+    assert normalizer._tensor_stats[OBS_STATE]["min"].shape == (MAX_STATE_DIM,)
+    assert normalizer._tensor_stats[ACTION]["min"].shape == (MAX_ACTION_DIM,)
+    assert unnormalizer._tensor_stats[ACTION]["min"].shape == (MAX_ACTION_DIM,)
+
+    # Normalizing a padded state must not raise (this is the exact runtime path that crashed).
+    processed = loaded_pre(
+        {
+            "task": "pick the block",
+            OBS_STATE: torch.zeros(STATE_DIM),
+            f"{OBS_IMAGES}.front": torch.rand(3, 16, 16),
+        }
+    )
+    assert processed[OBS_STATE].shape == (1, MAX_STATE_DIM)
+
+
 def test_evo1_policy_forward_and_inference_use_batched_embedding(monkeypatch):
     monkeypatch.setattr(modeling_evo1, "Evo1Model", DummyEvo1Model)
     policy = modeling_evo1.Evo1Policy(make_config())

tests/policies/test_factory_peft_revision.py

@@ -12,7 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import sys
 from types import SimpleNamespace
 from unittest.mock import MagicMock
 
@@ -47,18 +46,23 @@ def test_make_policy_keeps_peft_adapter_and_base_revisions_separate(monkeypatch)
     peft_config_from_pretrained = MagicMock(return_value=peft_config)
     adapted_policy = torch.nn.Linear(1, 1)
     peft_model_from_pretrained = MagicMock(return_value=adapted_policy)
-    monkeypatch.setitem(
-        sys.modules,
-        "peft",
-        SimpleNamespace(
-            PeftConfig=SimpleNamespace(from_pretrained=peft_config_from_pretrained),
-            PeftModel=SimpleNamespace(from_pretrained=peft_model_from_pretrained),
-        ),
+    require_package = MagicMock()
+    monkeypatch.setattr(policy_factory, "require_package", require_package)
+    monkeypatch.setattr(
+        policy_factory,
+        "PeftConfig",
+        SimpleNamespace(from_pretrained=peft_config_from_pretrained),
+    )
+    monkeypatch.setattr(
+        policy_factory,
+        "PeftModel",
+        SimpleNamespace(from_pretrained=peft_model_from_pretrained),
     )
 
     policy = policy_factory.make_policy(cfg, ds_meta=dataset_meta)
 
     assert policy is adapted_policy
+    require_package.assert_called_once_with("peft", extra="peft")
     peft_config_from_pretrained.assert_called_once_with(
         "user/adapter",
         revision="adapter-sha",

tests/robots/test_robot_kinematic_processor.py

@@ -0,0 +1,45 @@
+#!/usr/bin/env python
+
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# 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 pytest
+
+from lerobot.configs import FeatureType, PipelineFeatureType, PolicyFeature
+from lerobot.robots.so_follower.robot_kinematic_processor import (
+    ForwardKinematicsJointsToEEAction,
+    ForwardKinematicsJointsToEEObservation,
+)
+
+MOTOR_NAMES = ["shoulder_pan", "shoulder_lift", "elbow_flex", "wrist_flex", "wrist_roll", "gripper"]
+EE_KEYS = {f"ee.{k}" for k in ["x", "y", "z", "wx", "wy", "wz", "gripper_pos"]}
+
+
+def _joint_bucket(feature_type: FeatureType) -> dict[str, PolicyFeature]:
+    return {f"{n}.pos": PolicyFeature(type=feature_type, shape=(1,)) for n in MOTOR_NAMES}
+
+
+@pytest.mark.parametrize(
+    ("step_cls", "bucket", "feature_type"),
+    [
+        (ForwardKinematicsJointsToEEAction, PipelineFeatureType.ACTION, FeatureType.ACTION),
+        (ForwardKinematicsJointsToEEObservation, PipelineFeatureType.OBSERVATION, FeatureType.STATE),
+    ],
+)
+def test_fk_feature_schema(step_cls, bucket, feature_type):
+    features = {PipelineFeatureType.ACTION: {}, PipelineFeatureType.OBSERVATION: {}}
+    features[bucket] = _joint_bucket(feature_type)
+    out = step_cls(kinematics=None, motor_names=MOTOR_NAMES).transform_features(features)[bucket]
+    assert set(out) == EE_KEYS
+    assert {feature.type for feature in out.values()} == {feature_type}

tests/robots/test_so100_follower.py

@@ -49,7 +49,7 @@ def _make_bus_mock() -> MagicMock:
 
 
 @pytest.fixture
-def follower():
+def follower(tmp_path):
     bus_mock = _make_bus_mock()
 
     def _bus_side_effect(*_args, **kwargs):
@@ -71,7 +71,7 @@ def follower():
         ),
         patch.object(SO100Follower, "configure", lambda self: None),
     ):
-        cfg = SO100FollowerConfig(port="/dev/null")
+        cfg = SO100FollowerConfig(port="/dev/null", calibration_dir=tmp_path)
         robot = SO100Follower(cfg)
         yield robot
         if robot.is_connected:
@@ -99,6 +99,27 @@ def test_get_observation(follower):
         assert obs[f"{motor}.pos"] == idx
 
 
+def test_get_observation_uses_read_retries(follower):
+    # Feetech buses can intermittently fail a sync_read; the follower should forward the configured
+    # retry count so transient failures don't abort the control loop (see #3131).
+    follower.config.num_read_retries = 7
+    follower.connect()
+    follower.get_observation()
+
+    follower.bus.sync_read.assert_called_once_with("Present_Position", num_retry=7)
+
+
+def test_send_action_uses_read_retries(follower):
+    follower.config.max_relative_target = 10.0
+    follower.config.num_read_retries = 7
+    follower.connect()
+
+    action = {f"{motor}.pos": value * 10 for value, motor in enumerate(follower.bus.motors, 1)}
+    follower.send_action(action)
+
+    follower.bus.sync_read.assert_called_once_with("Present_Position", num_retry=7)
+
+
 def test_send_action(follower):
     follower.connect()
 

tests/utils/test_device_utils.py

@@ -0,0 +1,36 @@
+#!/usr/bin/env python
+# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# 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.
+
+from unittest.mock import patch
+
+import pytest
+import torch
+
+from lerobot.utils.device_utils import get_safe_torch_device, is_torch_device_available
+
+
+def test_cpu_always_available():
+    assert get_safe_torch_device("cpu") == torch.device("cpu")
+    assert is_torch_device_available("cpu")
+
+
+def test_missing_cuda_raises_valueerror():
+    with patch("torch.cuda.is_available", return_value=False), pytest.raises(ValueError, match="CUDA"):
+        get_safe_torch_device("cuda")
+
+
+def test_missing_mps_raises_valueerror():
+    with patch("torch.backends.mps.is_available", return_value=False), pytest.raises(ValueError, match="MPS"):
+        get_safe_torch_device("mps")

tests/utils/test_rotation.py

@@ -0,0 +1,46 @@
+#!/usr/bin/env python
+# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# 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 numpy as np
+import pytest
+
+from lerobot.utils.rotation import Rotation
+
+
+def test_zero_quaternion_rejected():
+    with pytest.raises(ValueError, match="non-zero"):
+        Rotation(np.zeros(4))
+
+
+def test_non_finite_quaternion_rejected():
+    with pytest.raises(ValueError, match="non-zero|finite"):
+        Rotation(np.array([np.nan, 0.0, 0.0, 1.0]))
+
+
+def test_wrong_shape_rejected():
+    with pytest.raises(ValueError, match="shape"):
+        Rotation(np.array([1.0, 0.0, 0.0]))
+
+
+def test_identity_roundtrip():
+    r = Rotation.from_rotvec(np.zeros(3))
+    assert np.allclose(r.as_rotvec(), 0.0)
+    assert np.allclose(r.as_matrix(), np.eye(3))
+
+
+def test_rotvec_roundtrip():
+    rotvec = np.array([0.1, -0.2, 0.3])
+    r = Rotation.from_rotvec(rotvec)
+    assert np.allclose(r.as_rotvec(), rotvec, atol=1e-6)