update image

Add GaussianNoise, MotionBlur, JPEGCompression, GaussianPatchBrightness,
RandomShadow, CoarseDropout, GammaCorrection, and PlanckianJitter.

Each transform addresses a real-world failure mode not covered by the
existing 6 defaults (sensor noise, motion blur, compression artifacts,
uneven lighting, cast shadows, partial occlusion, exposure variation,
color temperature shift).

All transforms are pure PyTorch, follow the make_params/transform
pattern, and integrate with ImageTransformConfig via a registry.

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-install-project --no-cache
-
-RUN uv sync --locked --extra all --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 application code and install the local project
-
-# Copy the rest of the application code
 # 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"]

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/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/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] = {}
+    episode_len = end_idx - start_idx
-    for idx in range(start_idx, end_idx):
-        item = dataset[idx]
-        for key, value in item.items():
-            if key not in dataset.features:
-                continue
 
-            if key not in collected_data:
+    # Images/video are the memory hog, so sub-sample those frames per episode;
-                collected_data[key] = []
+    # numeric columns are cheap, so read them in full (exact).
-            collected_data[key].append(value)
+    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_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/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/__init__.py

@@ -13,18 +13,34 @@
 # limitations under the License.
 
 from .transforms import (
+    CoarseDropout,
+    GammaCorrection,
+    GaussianNoise,
+    GaussianPatchBrightness,
     ImageTransformConfig,
     ImageTransforms,
     ImageTransformsConfig,
+    JPEGCompression,
+    MotionBlur,
+    PlanckianJitter,
+    RandomShadow,
     RandomSubsetApply,
     SharpnessJitter,
     make_transform_from_config,
 )
 
 __all__ = [
+    "CoarseDropout",
+    "GammaCorrection",
+    "GaussianNoise",
+    "GaussianPatchBrightness",
     "ImageTransformConfig",
     "ImageTransforms",
     "ImageTransformsConfig",
+    "JPEGCompression",
+    "MotionBlur",
+    "PlanckianJitter",
+    "RandomShadow",
     "RandomSubsetApply",
     "SharpnessJitter",
     "make_transform_from_config",

src/lerobot/transforms/transforms.py

@@ -14,11 +14,13 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import collections
+import math
 from collections.abc import Callable, Sequence
 from dataclasses import dataclass, field
 from typing import Any
 
 import torch
+from torchvision.io import decode_image, encode_jpeg
 from torchvision.transforms import v2
 from torchvision.transforms.v2 import (
     Transform,
@@ -144,6 +146,471 @@ class SharpnessJitter(Transform):
         return self._call_kernel(F.adjust_sharpness, inpt, sharpness_factor=sharpness_factor)
 
 
+class GaussianNoise(Transform):
+    """Add Gaussian noise to simulate camera sensor noise.
+
+    Models readout noise from ADC quantization, which increases in low-light conditions.
+    Common in real-robot setups where wrist cameras operate in suboptimal lighting.
+
+    Args:
+        std: Range (min, max) for noise standard deviation in pixel-value scale (0-255).
+    """
+
+    def __init__(self, std: float | Sequence[float] = (5.0, 25.0)) -> None:
+        super().__init__()
+        if isinstance(std, (int, float)):
+            self.std = (0.0, float(std))
+        elif isinstance(std, Sequence) and len(std) == 2:
+            self.std = (float(std[0]), float(std[1]))
+        else:
+            raise TypeError("std must be a number or a sequence with length 2.")
+        if not 0.0 <= self.std[0] <= self.std[1]:
+            raise ValueError(f"std must satisfy 0 <= min <= max, but got {self.std}.")
+
+    def make_params(self, flat_inputs: list[Any]) -> dict[str, Any]:
+        return {
+            "std": torch.empty(1).uniform_(self.std[0], self.std[1]).item(),
+            "seed": torch.randint(0, torch.iinfo(torch.int64).max, ()).item(),
+        }
+
+    def transform(self, inpt: Any, params: dict[str, Any]) -> Any:
+        if isinstance(inpt, torch.Tensor) and inpt.is_floating_point():
+            generator = torch.Generator(device=inpt.device).manual_seed(params["seed"])
+            noise = torch.randn(inpt.shape, device=inpt.device, dtype=inpt.dtype, generator=generator)
+            return (inpt + noise * (params["std"] / 255.0)).clamp(0.0, 1.0)
+        return inpt
+
+
+class MotionBlur(Transform):
+    """Apply directional motion blur to simulate fast robot or object movement.
+
+    Generates a 1D averaging kernel along a random direction, applied via depthwise convolution.
+
+    Args:
+        kernel_size: An odd kernel size or a range containing at least one odd kernel size.
+    """
+
+    def __init__(self, kernel_size: int | Sequence[int] = (3, 11)) -> None:
+        super().__init__()
+        if isinstance(kernel_size, int):
+            self.kernel_size = (kernel_size, kernel_size)
+        elif isinstance(kernel_size, Sequence) and len(kernel_size) == 2:
+            self.kernel_size = (int(kernel_size[0]), int(kernel_size[1]))
+        else:
+            raise TypeError("kernel_size must be an int or a sequence with length 2.")
+        if not 1 <= self.kernel_size[0] <= self.kernel_size[1]:
+            raise ValueError(f"kernel_size must satisfy 1 <= min <= max, but got {self.kernel_size}.")
+        self._first_odd_kernel_size = self.kernel_size[0] + (self.kernel_size[0] + 1) % 2
+        if self._first_odd_kernel_size > self.kernel_size[1]:
+            raise ValueError(f"kernel_size range must contain an odd value, but got {self.kernel_size}.")
+
+    def make_params(self, flat_inputs: list[Any]) -> dict[str, Any]:
+        num_odd_sizes = (self.kernel_size[1] - self._first_odd_kernel_size) // 2 + 1
+        size_index = int(torch.randint(0, num_odd_sizes, ()).item())
+        ks = self._first_odd_kernel_size + 2 * size_index
+        angle = torch.empty(1).uniform_(0, 360).item()
+        return {"kernel_size": ks, "angle": angle}
+
+    def transform(self, inpt: Any, params: dict[str, Any]) -> Any:
+        if not isinstance(inpt, torch.Tensor) or not inpt.is_floating_point():
+            return inpt
+        if inpt.ndim < 3:
+            raise ValueError(f"MotionBlur expects [..., C, H, W] input, but got shape {inpt.shape}.")
+
+        kernel_size = params["kernel_size"]
+        radius = kernel_size // 2
+        angle = math.radians(params["angle"])
+        positions = torch.linspace(-radius, radius, kernel_size, device=inpt.device)
+        x_coords = (positions * math.cos(angle)).round().to(torch.long) + radius
+        y_coords = (positions * math.sin(angle)).round().to(torch.long) + radius
+        kernel = torch.zeros((kernel_size, kernel_size), device=inpt.device, dtype=inpt.dtype)
+        kernel[y_coords, x_coords] = 1
+        kernel /= kernel.sum()
+
+        channels, height, width = inpt.shape[-3:]
+        flat_input = inpt.reshape(-1, channels, height, width)
+        depthwise_kernel = kernel.expand(channels, 1, kernel_size, kernel_size)
+        padded = torch.nn.functional.pad(flat_input, (radius,) * 4, mode="replicate")
+        output = torch.nn.functional.conv2d(padded, depthwise_kernel, groups=channels)
+        return output.reshape(inpt.shape).clamp(0.0, 1.0)
+
+
+class JPEGCompression(Transform):
+    """Simulate JPEG compression artifacts (block artifacts, color banding).
+
+    Models quality degradation from video compression in network-streamed camera feeds.
+
+    Args:
+        quality: Range (min, max) for JPEG quality factor (lower = more artifacts).
+    """
+
+    def __init__(self, quality: int | Sequence[int] = (15, 75)) -> None:
+        super().__init__()
+        if isinstance(quality, int):
+            self.quality = (quality, quality)
+        elif isinstance(quality, Sequence) and len(quality) == 2:
+            self.quality = (int(quality[0]), int(quality[1]))
+        else:
+            raise TypeError("quality must be an int or a sequence with length 2.")
+        if not 1 <= self.quality[0] <= self.quality[1] <= 100:
+            raise ValueError(f"quality must satisfy 1 <= min <= max <= 100, but got {self.quality}.")
+
+    def make_params(self, flat_inputs: list[Any]) -> dict[str, Any]:
+        return {"quality": int(torch.randint(self.quality[0], self.quality[1] + 1, (1,)).item())}
+
+    def transform(self, inpt: Any, params: dict[str, Any]) -> Any:
+        if not isinstance(inpt, torch.Tensor) or not inpt.is_floating_point():
+            return inpt
+        if inpt.ndim < 3:
+            raise ValueError(f"JPEGCompression expects [..., C, H, W] input, but got shape {inpt.shape}.")
+
+        channels, height, width = inpt.shape[-3:]
+        if channels not in (1, 3):
+            raise ValueError(f"JPEGCompression expects 1 or 3 channels, but got {channels}.")
+
+        flat_input = inpt.reshape(-1, channels, height, width)
+        flat_uint8 = (flat_input.clamp(0.0, 1.0) * 255).round().to(torch.uint8).cpu()
+        decoded_frames = [
+            decode_image(encode_jpeg(frame, quality=params["quality"])) for frame in flat_uint8.unbind()
+        ]
+        output = torch.stack(decoded_frames).to(device=inpt.device, dtype=inpt.dtype) / 255.0
+        return output.reshape(inpt.shape)
+
+
+class GaussianPatchBrightness(Transform):
+    """Apply spatially-varying brightness with Gaussian patches.
+
+    Simulates uneven overhead lighting, spotlights, and shadow patches commonly
+    encountered in real robot workspaces with multiple light sources.
+
+    Args:
+        num_patches: Range (min, max) for number of brightness patches.
+        sigma_range: Range for Gaussian sigma as fraction of image size.
+        factor_range: Range for brightness factor (< 1 darkens, > 1 brightens).
+    """
+
+    def __init__(
+        self,
+        num_patches: int | Sequence[int] = (1, 4),
+        sigma_range: Sequence[float] = (0.05, 0.25),
+        factor_range: Sequence[float] = (0.4, 1.6),
+    ) -> None:
+        super().__init__()
+        if isinstance(num_patches, int):
+            self.num_patches = (num_patches, num_patches)
+        elif isinstance(num_patches, Sequence) and len(num_patches) == 2:
+            self.num_patches = (int(num_patches[0]), int(num_patches[1]))
+        else:
+            raise TypeError("num_patches must be an int or a sequence with length 2.")
+        if not 1 <= self.num_patches[0] <= self.num_patches[1]:
+            raise ValueError(f"num_patches must satisfy 1 <= min <= max, but got {self.num_patches}.")
+        if not isinstance(sigma_range, Sequence) or len(sigma_range) != 2:
+            raise TypeError("sigma_range must be a sequence with length 2.")
+        self.sigma_range = (float(sigma_range[0]), float(sigma_range[1]))
+        if not 0.0 < self.sigma_range[0] <= self.sigma_range[1]:
+            raise ValueError(f"sigma_range must satisfy 0 < min <= max, but got {self.sigma_range}.")
+        if not isinstance(factor_range, Sequence) or len(factor_range) != 2:
+            raise TypeError("factor_range must be a sequence with length 2.")
+        self.factor_range = (float(factor_range[0]), float(factor_range[1]))
+        if not 0.0 <= self.factor_range[0] <= self.factor_range[1]:
+            raise ValueError(f"factor_range must satisfy 0 <= min <= max, but got {self.factor_range}.")
+
+    def make_params(self, flat_inputs: list[Any]) -> dict[str, Any]:
+        n = int(torch.randint(self.num_patches[0], self.num_patches[1] + 1, (1,)).item())
+        return {
+            "centers": torch.rand(n, 2).tolist(),
+            "sigmas": torch.empty(n).uniform_(self.sigma_range[0], self.sigma_range[1]).tolist(),
+            "factors": torch.empty(n).uniform_(self.factor_range[0], self.factor_range[1]).tolist(),
+        }
+
+    def transform(self, inpt: Any, params: dict[str, Any]) -> Any:
+        if not isinstance(inpt, torch.Tensor) or not inpt.is_floating_point():
+            return inpt
+        h, w = inpt.shape[-2:]
+        mask = torch.ones(h, w, device=inpt.device, dtype=inpt.dtype)
+        grid_y = torch.linspace(0, 1, h, device=inpt.device, dtype=inpt.dtype)
+        grid_x = torch.linspace(0, 1, w, device=inpt.device, dtype=inpt.dtype)
+        yy, xx = torch.meshgrid(grid_y, grid_x, indexing="ij")
+        for (cy, cx), sigma, factor in zip(
+            params["centers"], params["sigmas"], params["factors"], strict=True
+        ):
+            gauss = torch.exp(-((yy - cy) ** 2 + (xx - cx) ** 2) / (2 * sigma**2))
+            mask = mask * (1.0 + (factor - 1.0) * gauss)
+        broadcast_shape = (1,) * (inpt.ndim - 2) + (h, w)
+        return (inpt * mask.reshape(broadcast_shape)).clamp(0.0, 1.0)
+
+
+class RandomShadow(Transform):
+    """Add random vertical band shadow with smooth edges.
+
+    Simulates cast shadows from objects or people near the robot workspace.
+    Symmetric: randomly brightens or darkens to prevent BatchNorm stats shift.
+
+    Args:
+        opacity: Range (min, max) for shadow/highlight opacity.
+    """
+
+    def __init__(self, opacity: float | Sequence[float] = (0.3, 0.6)) -> None:
+        super().__init__()
+        if isinstance(opacity, (int, float)):
+            self.opacity = (float(opacity), float(opacity))
+        elif isinstance(opacity, Sequence) and len(opacity) == 2:
+            self.opacity = (float(opacity[0]), float(opacity[1]))
+        else:
+            raise TypeError("opacity must be a number or a sequence with length 2.")
+        if not 0.0 <= self.opacity[0] <= self.opacity[1] <= 1.0:
+            raise ValueError(f"opacity must satisfy 0 <= min <= max <= 1, but got {self.opacity}.")
+
+    def make_params(self, flat_inputs: list[Any]) -> dict[str, Any]:
+        return {
+            "opacity": torch.empty(1).uniform_(self.opacity[0], self.opacity[1]).item(),
+            "start": torch.rand(1).item(),
+            "width": torch.empty(1).uniform_(1 / 3, 2 / 3).item(),
+            "direction": -1.0 if torch.rand(1).item() < 0.5 else 1.0,
+        }
+
+    def transform(self, inpt: Any, params: dict[str, Any]) -> Any:
+        if not isinstance(inpt, torch.Tensor) or not inpt.is_floating_point():
+            return inpt
+        if inpt.ndim < 3:
+            raise ValueError(f"RandomShadow expects [..., C, H, W] input, but got shape {inpt.shape}.")
+
+        h, w = inpt.shape[-2:]
+        band_width = max(1, min(w, round(params["width"] * w)))
+        x_start = round(params["start"] * (w - band_width))
+        x_end = x_start + band_width
+        mask = torch.ones(h, w, device=inpt.device, dtype=inpt.dtype)
+        mask[:, x_start:x_end] = 1.0 + params["direction"] * params["opacity"]
+
+        smoothing_size = min(8, h, w)
+        if smoothing_size > 1:
+            batched_mask = mask[None, None]
+            small = torch.nn.functional.avg_pool2d(batched_mask, smoothing_size, stride=smoothing_size)
+            mask = torch.nn.functional.interpolate(small, size=(h, w), mode="bilinear", align_corners=False)[
+                0, 0
+            ]
+
+        broadcast_shape = (1,) * (inpt.ndim - 2) + (h, w)
+        return (inpt * mask.reshape(broadcast_shape)).clamp(0.0, 1.0)
+
+
+class CoarseDropout(Transform):
+    """Drop random rectangular patches to simulate partial occlusion.
+
+    Models objects, hands, or cables passing through the camera field of view
+    during robot manipulation.
+
+    Args:
+        max_holes: Maximum number of rectangular patches to drop.
+        max_height_frac: Maximum patch height as fraction of image height.
+        max_width_frac: Maximum patch width as fraction of image width.
+        fill_value: Value to fill dropped regions with.
+    """
+
+    def __init__(
+        self,
+        max_holes: int = 8,
+        max_height_frac: float = 0.07,
+        max_width_frac: float = 0.07,
+        fill_value: float = 0.0,
+    ) -> None:
+        super().__init__()
+        if not isinstance(max_holes, int):
+            raise TypeError("max_holes must be an int.")
+        if max_holes < 1:
+            raise ValueError(f"max_holes must be at least 1, but got {max_holes}.")
+        if not 0.0 < max_height_frac <= 1.0:
+            raise ValueError(f"max_height_frac must be in (0, 1], but got {max_height_frac}.")
+        if not 0.0 < max_width_frac <= 1.0:
+            raise ValueError(f"max_width_frac must be in (0, 1], but got {max_width_frac}.")
+        if not 0.0 <= fill_value <= 1.0:
+            raise ValueError(f"fill_value must be in [0, 1], but got {fill_value}.")
+        self.max_holes = max_holes
+        self.max_height_frac = max_height_frac
+        self.max_width_frac = max_width_frac
+        self.fill_value = fill_value
+
+    def make_params(self, flat_inputs: list[Any]) -> dict[str, Any]:
+        n = int(torch.randint(1, self.max_holes + 1, (1,)).item())
+        sizes = torch.rand(n, 2)
+        sizes[:, 0] *= self.max_height_frac
+        sizes[:, 1] *= self.max_width_frac
+        return {"sizes": sizes.tolist(), "positions": torch.rand(n, 2).tolist()}
+
+    def transform(self, inpt: Any, params: dict[str, Any]) -> Any:
+        if not isinstance(inpt, torch.Tensor) or not inpt.is_floating_point():
+            return inpt
+        if inpt.ndim < 3:
+            raise ValueError(f"CoarseDropout expects [..., C, H, W] input, but got shape {inpt.shape}.")
+
+        h, w = inpt.shape[-2:]
+        result = inpt.clone()
+        for (height_frac, width_frac), (y_frac, x_frac) in zip(
+            params["sizes"], params["positions"], strict=True
+        ):
+            hole_h = max(1, min(h, round(height_frac * h)))
+            hole_w = max(1, min(w, round(width_frac * w)))
+            y = round(y_frac * (h - hole_h))
+            x = round(x_frac * (w - hole_w))
+            result[..., y : y + hole_h, x : x + hole_w] = self.fill_value
+        return result
+
+
+class GammaCorrection(Transform):
+    """Apply random gamma correction to simulate exposure variation.
+
+    Models different camera auto-exposure settings and sensor response curves.
+    Uses log-symmetric sampling so brightening and darkening are equally likely,
+    preventing BatchNorm statistics shift.
+
+    Args:
+        gamma: Range (min, max) for gamma value. Values < 1 brighten, > 1 darken.
+    """
+
+    def __init__(self, gamma: float | Sequence[float] = (0.5, 2.0)) -> None:
+        super().__init__()
+        if isinstance(gamma, (int, float)):
+            gamma = float(gamma)
+            if gamma <= 0:
+                raise ValueError(f"gamma must be positive, but got {gamma}.")
+            self.gamma = (min(gamma, 1.0 / gamma), max(gamma, 1.0 / gamma))
+        elif isinstance(gamma, Sequence) and len(gamma) == 2:
+            self.gamma = (float(gamma[0]), float(gamma[1]))
+        else:
+            raise TypeError("gamma must be a number or a sequence with length 2.")
+        if not 0.0 < self.gamma[0] <= self.gamma[1]:
+            raise ValueError(f"gamma must satisfy 0 < min <= max, but got {self.gamma}.")
+
+    def make_params(self, flat_inputs: list[Any]) -> dict[str, Any]:
+        log_lo = math.log(self.gamma[0])
+        log_hi = math.log(self.gamma[1])
+        gamma = math.exp(torch.empty(1).uniform_(log_lo, log_hi).item())
+        return {"gamma": gamma}
+
+    def transform(self, inpt: Any, params: dict[str, Any]) -> Any:
+        if isinstance(inpt, torch.Tensor) and inpt.is_floating_point():
+            return inpt.pow(params["gamma"]).clamp(0.0, 1.0)
+        return inpt
+
+
+# From the paper authors' MIT-licensed reference implementation:
+# https://github.com/TheZino/PlanckianJitter
+_PLANCKIAN_BLACKBODY_COEFFICIENTS = (
+    (0.6743, 0.4029, 0.0013),
+    (0.6281, 0.4241, 0.1665),
+    (0.5919, 0.4372, 0.2513),
+    (0.5623, 0.4457, 0.3154),
+    (0.5376, 0.4515, 0.3672),
+    (0.5163, 0.4555, 0.4103),
+    (0.4979, 0.4584, 0.4468),
+    (0.4816, 0.4604, 0.4782),
+    (0.4672, 0.4619, 0.5053),
+    (0.4542, 0.4630, 0.5289),
+    (0.4426, 0.4638, 0.5497),
+    (0.4320, 0.4644, 0.5681),
+    (0.4223, 0.4648, 0.5844),
+    (0.4135, 0.4651, 0.5990),
+    (0.4054, 0.4653, 0.6121),
+    (0.3980, 0.4654, 0.6239),
+    (0.3911, 0.4655, 0.6346),
+    (0.3847, 0.4656, 0.6444),
+    (0.3787, 0.4656, 0.6532),
+    (0.3732, 0.4656, 0.6613),
+    (0.3680, 0.4655, 0.6688),
+    (0.3632, 0.4655, 0.6756),
+    (0.3586, 0.4655, 0.6820),
+    (0.3544, 0.4654, 0.6878),
+    (0.3503, 0.4653, 0.6933),
+)
+_PLANCKIAN_MIN_TEMPERATURE = 3_000
+_PLANCKIAN_MAX_TEMPERATURE = 15_000
+_PLANCKIAN_TEMPERATURE_STEP = 500
+
+
+class PlanckianJitter(Transform):
+    """Simulate color temperature shift along the Planckian locus.
+
+    Samples one black-body temperature and applies the corresponding correlated red
+    and blue channel scaling while preserving the green channel. Coefficients between
+    the tabulated 500 K intervals are linearly interpolated.
+
+    Reference: Zini et al., "Planckian Jitter", CVPR 2022 Workshop.
+
+    Args:
+        temperature: A fixed color temperature or range in Kelvin. Supported values
+            are between 3000 K and 15000 K.
+    """
+
+    def __init__(self, temperature: int | Sequence[int] = (3_000, 15_000)) -> None:
+        super().__init__()
+        if isinstance(temperature, int):
+            self.temperature = (temperature, temperature)
+        elif isinstance(temperature, Sequence) and len(temperature) == 2:
+            self.temperature = (int(temperature[0]), int(temperature[1]))
+        else:
+            raise TypeError("temperature must be an int or a sequence with length 2.")
+        if not (
+            _PLANCKIAN_MIN_TEMPERATURE
+            <= self.temperature[0]
+            <= self.temperature[1]
+            <= _PLANCKIAN_MAX_TEMPERATURE
+        ):
+            raise ValueError(
+                "temperature must satisfy "
+                f"{_PLANCKIAN_MIN_TEMPERATURE} <= min <= max <= {_PLANCKIAN_MAX_TEMPERATURE}, "
+                f"but got {self.temperature}."
+            )
+
+    def make_params(self, flat_inputs: list[Any]) -> dict[str, Any]:
+        temperature = int(torch.randint(self.temperature[0], self.temperature[1] + 1, ()).item())
+        return {"temperature": temperature}
+
+    def transform(self, inpt: Any, params: dict[str, Any]) -> Any:
+        if not isinstance(inpt, torch.Tensor) or not inpt.is_floating_point():
+            return inpt
+        if inpt.ndim < 3 or inpt.shape[-3] != 3:
+            raise ValueError(f"PlanckianJitter expects [..., 3, H, W] input, but got shape {inpt.shape}.")
+
+        table_position = (params["temperature"] - _PLANCKIAN_MIN_TEMPERATURE) / _PLANCKIAN_TEMPERATURE_STEP
+        left_index = math.floor(table_position)
+        right_index = min(left_index + 1, len(_PLANCKIAN_BLACKBODY_COEFFICIENTS) - 1)
+        interpolation_weight = table_position - left_index
+
+        left = torch.tensor(
+            _PLANCKIAN_BLACKBODY_COEFFICIENTS[left_index],
+            device=inpt.device,
+            dtype=inpt.dtype,
+        )
+        right = torch.tensor(
+            _PLANCKIAN_BLACKBODY_COEFFICIENTS[right_index],
+            device=inpt.device,
+            dtype=inpt.dtype,
+        )
+        coefficients = torch.lerp(left, right, interpolation_weight)
+        scale = torch.stack(
+            (
+                coefficients[0] / coefficients[1],
+                coefficients.new_tensor(1.0),
+                coefficients[2] / coefficients[1],
+            )
+        )
+        broadcast_shape = (1,) * (inpt.ndim - 3) + (3, 1, 1)
+        return (inpt * scale.reshape(broadcast_shape)).clamp(0.0, 1.0)
+
+
+_CUSTOM_TRANSFORMS: dict[str, type[Transform]] = {
+    "SharpnessJitter": SharpnessJitter,
+    "GaussianNoise": GaussianNoise,
+    "MotionBlur": MotionBlur,
+    "JPEGCompression": JPEGCompression,
+    "GaussianPatchBrightness": GaussianPatchBrightness,
+    "RandomShadow": RandomShadow,
+    "CoarseDropout": CoarseDropout,
+    "GammaCorrection": GammaCorrection,
+    "PlanckianJitter": PlanckianJitter,
+}
+
+
 @dataclass
 class ImageTransformConfig:
     """
@@ -216,16 +683,17 @@ class ImageTransformsConfig:
 
 
 def make_transform_from_config(cfg: ImageTransformConfig) -> Transform:
-    if cfg.type == "SharpnessJitter":
+    if cfg.type in _CUSTOM_TRANSFORMS:
-        return SharpnessJitter(**cfg.kwargs)
+        return _CUSTOM_TRANSFORMS[cfg.type](**cfg.kwargs)
 
     transform_cls = getattr(v2, cfg.type, None)
     if isinstance(transform_cls, type) and issubclass(transform_cls, Transform):
         return transform_cls(**cfg.kwargs)
 
+    valid_custom = ", ".join(sorted(_CUSTOM_TRANSFORMS.keys()))
     raise ValueError(
         f"Transform '{cfg.type}' is not valid. It must be a class in "
-        f"torchvision.transforms.v2 or 'SharpnessJitter'."
+        f"torchvision.transforms.v2 or one of: {valid_custom}."
     )
 
 

src/lerobot/utils/utils.py

@@ -133,10 +133,13 @@ def say(text: str, blocking: bool = False):
     else:
         raise RuntimeError("Unsupported operating system for text-to-speech.")
 
-    if blocking:
+    try:
-        subprocess.run(cmd, check=True)
+        if blocking:
-    else:
+            subprocess.run(cmd, check=True, timeout=5)
-        subprocess.Popen(cmd, creationflags=subprocess.CREATE_NO_WINDOW if system == "Windows" else 0)
+        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/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_image_transforms.py

@@ -28,9 +28,17 @@ from lerobot.scripts.lerobot_imgtransform_viz import (
     save_each_transform,
 )
 from lerobot.transforms import (
+    CoarseDropout,
+    GammaCorrection,
+    GaussianNoise,
+    GaussianPatchBrightness,
     ImageTransformConfig,
     ImageTransforms,
     ImageTransformsConfig,
+    JPEGCompression,
+    MotionBlur,
+    PlanckianJitter,
+    RandomShadow,
     RandomSubsetApply,
     SharpnessJitter,
     make_transform_from_config,
@@ -455,3 +463,153 @@ def test_save_each_transform(img_tensor_factory, tmp_path):
             assert (transform_dir / file_name).exists(), (
                 f"{file_name} was not found in {transform} directory."
             )
+
+
+# --- Tests for robotics-relevant augmentations ---
+
+ROBOTICS_TRANSFORMS = [
+    ("GaussianNoise", GaussianNoise, {"std": (5.0, 25.0)}),
+    ("MotionBlur", MotionBlur, {"kernel_size": (3, 11)}),
+    ("JPEGCompression", JPEGCompression, {"quality": (15, 75)}),
+    ("GaussianPatchBrightness", GaussianPatchBrightness, {}),
+    ("RandomShadow", RandomShadow, {"opacity": (0.3, 0.6)}),
+    ("CoarseDropout", CoarseDropout, {"max_holes": 8}),
+    ("GammaCorrection", GammaCorrection, {"gamma": (0.5, 2.0)}),
+    ("PlanckianJitter", PlanckianJitter, {"temperature": (3_000, 15_000)}),
+]
+
+
+@pytest.mark.parametrize("name,cls,kwargs", ROBOTICS_TRANSFORMS, ids=[t[0] for t in ROBOTICS_TRANSFORMS])
+def test_robotics_transform_shape_preserved(name, cls, kwargs, img_tensor_factory):
+    img = img_tensor_factory()
+    tf = cls(**kwargs)
+    out = tf(img)
+    assert out.shape == img.shape, f"{name} changed shape: {img.shape} -> {out.shape}"
+
+
+@pytest.mark.parametrize("name,cls,kwargs", ROBOTICS_TRANSFORMS, ids=[t[0] for t in ROBOTICS_TRANSFORMS])
+def test_robotics_transform_output_range(name, cls, kwargs, img_tensor_factory):
+    img = img_tensor_factory()
+    tf = cls(**kwargs)
+    out = tf(img)
+    assert out.min() >= -0.01, f"{name} min below range: {out.min():.4f}"
+    assert out.max() <= 1.01, f"{name} max above range: {out.max():.4f}"
+
+
+@pytest.mark.parametrize("name,cls,kwargs", ROBOTICS_TRANSFORMS, ids=[t[0] for t in ROBOTICS_TRANSFORMS])
+def test_robotics_transform_float_output(name, cls, kwargs, img_tensor_factory):
+    img = img_tensor_factory()
+    tf = cls(**kwargs)
+    out = tf(img)
+    assert out.is_floating_point(), f"{name} output dtype={out.dtype}"
+
+
+@pytest.mark.parametrize("name,cls,kwargs", ROBOTICS_TRANSFORMS, ids=[t[0] for t in ROBOTICS_TRANSFORMS])
+def test_robotics_transform_non_float_passthrough(name, cls, kwargs):
+    int_img = torch.randint(0, 255, (3, 32, 32), dtype=torch.uint8)
+    tf = cls(**kwargs)
+    out = tf(int_img)
+    assert torch.equal(out, int_img), f"{name} modified non-float input"
+
+
+@pytest.mark.parametrize("name,cls,kwargs", ROBOTICS_TRANSFORMS, ids=[t[0] for t in ROBOTICS_TRANSFORMS])
+def test_robotics_transform_via_config(name, cls, kwargs):
+    cfg = ImageTransformConfig(type=name, kwargs=kwargs)
+    tf = make_transform_from_config(cfg)
+    assert isinstance(tf, cls), f"Config produced {type(tf)}, expected {cls}"
+
+
+def test_make_transform_error_message_includes_custom():
+    """Error message should list all registered custom transforms."""
+    with pytest.raises(ValueError, match="GaussianNoise"):
+        make_transform_from_config(ImageTransformConfig(type="NonExistent"))
+
+
+@pytest.mark.parametrize("name,cls,kwargs", ROBOTICS_TRANSFORMS, ids=[t[0] for t in ROBOTICS_TRANSFORMS])
+@pytest.mark.parametrize("shape", [(4, 3, 32, 32), (2, 4, 3, 16, 16)])
+def test_robotics_transform_supports_temporal_batches(name, cls, kwargs, shape):
+    img = torch.rand(shape)
+    out = cls(**kwargs)(img)
+    assert out.shape == img.shape, f"{name} changed shape: {img.shape} -> {out.shape}"
+    assert out.min() >= 0
+    assert out.max() <= 1
+
+
+@pytest.mark.parametrize(
+    "cls,kwargs",
+    [
+        (GaussianNoise, {"std": (25.0, 25.0)}),
+        (MotionBlur, {"kernel_size": 5}),
+        (JPEGCompression, {"quality": 10}),
+        (
+            GaussianPatchBrightness,
+            {"num_patches": 1, "sigma_range": (0.2, 0.2), "factor_range": (0.5, 0.5)},
+        ),
+        (RandomShadow, {"opacity": 0.5}),
+        (CoarseDropout, {"max_holes": 1, "fill_value": 0.0}),
+        (GammaCorrection, {"gamma": (2.0, 2.0)}),
+        (PlanckianJitter, {"temperature": 3_000}),
+    ],
+)
+def test_robotics_transform_is_not_silent_noop(cls, kwargs):
+    img = torch.rand(3, 32, 32)
+    out = cls(**kwargs)(img)
+    assert not torch.equal(out, img)
+
+
+@pytest.mark.parametrize(
+    "transform",
+    [
+        GaussianNoise(std=25),
+        RandomShadow(opacity=0.5),
+        CoarseDropout(max_holes=4),
+    ],
+)
+def test_robotics_transform_random_params_are_reused(transform):
+    img = torch.rand(3, 32, 32)
+    params = transform.make_params([img])
+    torch.testing.assert_close(transform.transform(img, params), transform.transform(img, params))
+
+
+def test_motion_blur_kernel_size_stays_in_configured_range():
+    transform = MotionBlur(kernel_size=(4, 10))
+    sampled_sizes = {transform.make_params([])["kernel_size"] for _ in range(100)}
+    assert sampled_sizes <= {5, 7, 9}
+    assert sampled_sizes
+
+
+def test_gamma_correction_scalar_below_one_defines_symmetric_range():
+    transform = GammaCorrection(gamma=0.5)
+    assert transform.gamma == (0.5, 2.0)
+    assert transform(torch.rand(3, 8, 8)).shape == (3, 8, 8)
+
+
+def test_planckian_jitter_uses_correlated_temperature_coefficients():
+    img = torch.full((2, 3, 8, 8), 0.25)
+    out = PlanckianJitter(temperature=3_000)(img)
+    torch.testing.assert_close(out[:, 1], img[:, 1])
+    assert torch.all(out[:, 0] > out[:, 1])
+    assert torch.all(out[:, 2] < out[:, 1])
+
+
+def test_random_shadow_supports_small_images():
+    img = torch.rand(3, 7, 7)
+    assert RandomShadow()(img).shape == img.shape
+
+
+@pytest.mark.parametrize(
+    "cls,kwargs",
+    [
+        (GaussianNoise, {"std": (-1.0, 1.0)}),
+        (MotionBlur, {"kernel_size": 4}),
+        (JPEGCompression, {"quality": (0, 75)}),
+        (GaussianPatchBrightness, {"sigma_range": (0.0, 0.25)}),
+        (RandomShadow, {"opacity": (0.3, 1.1)}),
+        (CoarseDropout, {"max_holes": 0}),
+        (GammaCorrection, {"gamma": 0.0}),
+        (PlanckianJitter, {"temperature": (2_000, 6_500)}),
+    ],
+)
+def test_robotics_transform_rejects_invalid_config(cls, kwargs):
+    with pytest.raises(ValueError):
+        cls(**kwargs)

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/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(
+    require_package = MagicMock()
-        sys.modules,
+    monkeypatch.setattr(policy_factory, "require_package", require_package)
-        "peft",
+    monkeypatch.setattr(
-        SimpleNamespace(
+        policy_factory,
-            PeftConfig=SimpleNamespace(from_pretrained=peft_config_from_pretrained),
+        "PeftConfig",
-            PeftModel=SimpleNamespace(from_pretrained=peft_model_from_pretrained),
+        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_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()