feat(ci): re-enable stale issues countdown (#4279)

* docs(evo1): add LIBERO reproduction recipe

* docs(evo1): link verified LIBERO checkpoint

* docs(policies): address comments libero results

---------

Co-authored-by: Xingdong Zuo <18168681+zuoxingdong@users.noreply.github.com>

.github/workflows/benchmark_tests.yml

@@ -53,7 +53,7 @@ permissions:
   contents: read
 
 env:
-  UV_VERSION: "0.8.0"
+  UV_VERSION: "0.11.30"
   PYTHON_VERSION: "3.12"
 
 # Cancel in-flight runs for the same branch/PR.

.github/workflows/docker_publish.yml

@@ -27,7 +27,7 @@ on:
 
 # Sets up the environment variables
 env:
-  UV_VERSION: "0.8.0"
+  UV_VERSION: "0.11.30"
   PYTHON_VERSION: "3.12"
   DOCKER_IMAGE_NAME_CPU: huggingface/lerobot-cpu:latest
   DOCKER_IMAGE_NAME_GPU: huggingface/lerobot-gpu:latest

.github/workflows/fast_tests.yml

@@ -48,7 +48,7 @@ permissions:
 
 # Sets up the environment variables
 env:
-  UV_VERSION: "0.8.0"
+  UV_VERSION: "0.11.30"
   PYTHON_VERSION: "3.12"
 
 # Ensures that only the latest commit for a PR or branch is built, canceling older runs.

.github/workflows/full_tests.yml

@@ -37,7 +37,7 @@ permissions:
 
 # Sets up the environment variables
 env:
-  UV_VERSION: "0.8.0"
+  UV_VERSION: "0.11.30"
   PYTHON_VERSION: "3.12"
   DOCKER_IMAGE_NAME: huggingface/lerobot-gpu
 

.github/workflows/latest_deps_tests.yml

@@ -27,7 +27,7 @@ on:
 
 # Sets up the environment variables
 env:
-  UV_VERSION: "0.8.0"
+  UV_VERSION: "0.11.30"
   PYTHON_VERSION: "3.12"
   DOCKER_IMAGE_NAME: huggingface/lerobot-gpu:latest-deps
 

.github/workflows/release.yml

@@ -21,7 +21,7 @@ on:
 
 # Sets up the environment variables
 env:
-  UV_VERSION: "0.8.0"
+  UV_VERSION: "0.11.30"
   PYTHON_VERSION: "3.12"
 
 jobs:

.github/workflows/stale.yml

@@ -19,8 +19,8 @@ on:
   workflow_dispatch:
 
   # Runs at 02:00
-  # schedule:
-  #   - cron: "0 2 * * *"
+  schedule:
+    - cron: "0 2 * * *"
 
 env:
   CLOSE_ISSUE_MESSAGE: >
@@ -31,7 +31,7 @@ env:
     Feel free to reopen if is still relevant, or to ping a collaborator if you have any questions.
   WARN_ISSUE_MESSAGE: >
     This issue has been automatically marked as stale because it has not had
-    recent activity (1 year). It will be closed if no further activity occurs.
+    recent activity (1 year). It will be closed if no further activity occurs within 30 days.
     Any change, comment or update to this issue will reset this count.
     Thank you for your contributions.
   WARN_PR_MESSAGE: >
@@ -61,8 +61,8 @@ jobs:
           exempt-pr-labels: never-stale
           days-before-issue-stale: 365
           days-before-issue-close: 30
-          days-before-pr-stale: 365
-          days-before-pr-close: 30
+          days-before-pr-stale: -1
+          days-before-pr-close: -1
           delete-branch: true
           close-issue-message: ${{ env.CLOSE_ISSUE_MESSAGE }}
           close-pr-message: ${{ env.CLOSE_PR_MESSAGE }}

docs/source/evo1.mdx

@@ -23,18 +23,18 @@ The broader EVO1 project may include additional training scripts and dataset too
 2. Install EVO1 dependencies:
 
    ```bash
-   pip install -e ".[evo1]"
+   pip install -e ".[training,evo1]"
    ```
 
-   For LIBERO evaluation, install the LIBERO extra as well:
+   For LIBERO training and evaluation, install the LIBERO extra as well:
 
    ```bash
-   pip install -e ".[evo1,libero]"
+   pip install -e ".[training,evo1,libero]"
    ```
 
 3. Install a `flash-attn` wheel only if it is compatible with your Python, PyTorch, CUDA, and GPU stack. EVO1 falls back to standard attention when `flash_attn` is not available.
 
-EVO1 uses the native Hugging Face `transformers` InternVL implementation, so `policy.vlm_model_name` must point to a natively converted checkpoint such as `OpenGVLab/InternVL3-1B-hf` (note the `-hf` suffix). The first run may download the configured VLM checkpoint unless `policy.vlm_model_name` points to a local model directory.
+EVO1 uses the native Hugging Face `transformers` InternVL implementation, so `policy.vlm_model_name` must point to a natively converted checkpoint such as `OpenGVLab/InternVL3-1B-hf` (note the `-hf` suffix). The first run downloads the configured VLM checkpoint and later runs reuse it from the Hugging Face cache.
 
 ## Data Requirements
 
@@ -92,7 +92,7 @@ lerobot-train \
 
 ### Stage 2
 
-Stage 2 finetunes the VLM branches and action head. A common workflow starts from a Stage 1 checkpoint:
+Stage 2 loads the Stage 1 policy, but starts a fresh optimizer and scheduler:
 
 ```bash
 lerobot-train \
@@ -152,16 +152,154 @@ lerobot-rollout \
 
 ### LIBERO Evaluation
 
-> [!NOTE]
-> Benchmark results for a `lerobot`-hosted LIBERO checkpoint trained with this implementation
-> will be added once training completes.
+#### Reference result
 
-The official EVO1 LIBERO rollout protocol uses the raw LIBERO camera feature names
+> [!NOTE]
+> The released Stage-2 checkpoint passed clean-download and rollout verification:
+> [`zuoxingdong/evo1_libero`](https://huggingface.co/zuoxingdong/evo1_libero), revision
+> [`515921f4a2c1d3f3ad523721eafa26fdf2af315b`](https://huggingface.co/zuoxingdong/evo1_libero/commit/515921f4a2c1d3f3ad523721eafa26fdf2af315b).
+> The clean-download evaluation used LeRobot revision
+> [`e40b58a8dfa9e7b86918c374791599d070518d11`](https://github.com/huggingface/lerobot/commit/e40b58a8dfa9e7b86918c374791599d070518d11).
+
+The single-run Stage-2 checkpoint at step 70,000 produced:
+
+| Suite          | Successful episodes |  Episodes | Success rate |
+| -------------- | ------------------: | --------: | -----------: |
+| LIBERO Spatial |                 485 |       500 |        97.0% |
+| LIBERO Object  |                 496 |       500 |        99.2% |
+| LIBERO Goal    |                 483 |       500 |        96.6% |
+| LIBERO-10      |                 469 |       500 |        93.8% |
+| **Overall**    |           **1,933** | **2,000** |   **96.65%** |
+
+These results use one trained checkpoint and evaluation seed `1000`; they are not a multi-seed
+mean or confidence estimate.
+
+#### Reference training recipe
+
+The released checkpoint records the complete resolved Stage-2 configuration in
+[`train_config.json`](https://huggingface.co/zuoxingdong/evo1_libero/blob/515921f4a2c1d3f3ad523721eafa26fdf2af315b/train_config.json).
+The measured run used two H100 GPUs with two DDP processes and batch 64 per process, giving global batch 128. Both stages used the same topology. The base VLM came from revision
+`014c0583a0d4bedf29fbe2dbff4f865eb998e171` of `OpenGVLab/InternVL3-1B-hf`.
+The released artifact does not record the exact LeRobot training commit or its original dependency lock,
+so the commands below reproduce the recorded configuration and topology from a current checkout rather
+than reconstructing the software environment bit for bit.
+
+From a LeRobot source checkout, install the locked dependencies and download that exact VLM revision:
+
+```bash
+uv sync --locked --extra training --extra evo1 --extra libero
+VLM_DIR=$(uv run hf download OpenGVLab/InternVL3-1B-hf \
+  --revision=014c0583a0d4bedf29fbe2dbff4f865eb998e171)
+```
+
+Stage 1 freezes the VLM and trains the action head for 5,000 steps:
+
+```bash
+uv run accelerate launch --num_processes=2 -m lerobot.scripts.lerobot_train \
+  --dataset.repo_id=lerobot/libero \
+  --dataset.revision=a1aaacb7f6cd6ee5fb43120f673cebb0cfea7dd4 \
+  --dataset.video_backend=torchcodec \
+  --dataset.return_uint8=true \
+  --dataset.image_transforms.enable=true \
+  --dataset.use_imagenet_stats=true \
+  --dataset.eval_split=0.0 \
+  --policy.type=evo1 \
+  --policy.training_stage=stage1 \
+  --policy.apply_training_stage_defaults=true \
+  --policy.vlm_model_name="${VLM_DIR}" \
+  --policy.vlm_num_layers=14 \
+  --policy.vlm_dtype=bfloat16 \
+  --policy.device=cuda \
+  --policy.use_amp=true \
+  --policy.use_flash_attn=true \
+  --policy.enable_gradient_checkpointing=true \
+  --policy.gradient_checkpointing_use_reentrant=false \
+  --policy.image_resolution='[448,448]' \
+  --policy.chunk_size=50 \
+  --policy.n_action_steps=50 \
+  --policy.max_state_dim=24 \
+  --policy.max_action_dim=24 \
+  --policy.dropout=0.2 \
+  --policy.optimizer_lr=1e-5 \
+  --policy.optimizer_weight_decay=1e-3 \
+  --policy.optimizer_grad_clip_norm=1.0 \
+  --policy.scheduler_warmup_steps=1000 \
+  --policy.push_to_hub=false \
+  --use_policy_training_preset=true \
+  --batch_size=64 \
+  --steps=5000 \
+  --save_checkpoint=true \
+  --save_checkpoint_to_hub=false \
+  --save_freq=2500 \
+  --log_freq=10 \
+  --env_eval_freq=0 \
+  --num_workers=4 \
+  --prefetch_factor=2 \
+  --persistent_workers=true \
+  --seed=1000 \
+  --wandb.enable=false \
+  --output_dir=./outputs/evo1-libero-stage1-g128-5k
+```
+
+Stage 2 loads the Stage-1 policy but starts a fresh optimizer and scheduler. It trains for 80,000 steps;
+the reported checkpoint is the save at step 70,000:
+
+```bash
+uv run accelerate launch --num_processes=2 -m lerobot.scripts.lerobot_train \
+  --dataset.repo_id=lerobot/libero \
+  --dataset.revision=a1aaacb7f6cd6ee5fb43120f673cebb0cfea7dd4 \
+  --dataset.video_backend=torchcodec \
+  --dataset.return_uint8=true \
+  --dataset.image_transforms.enable=true \
+  --dataset.use_imagenet_stats=true \
+  --dataset.eval_split=0.0 \
+  --policy.path=./outputs/evo1-libero-stage1-g128-5k/checkpoints/005000/pretrained_model \
+  --policy.training_stage=stage2 \
+  --policy.apply_training_stage_defaults=true \
+  --policy.vlm_model_name="${VLM_DIR}" \
+  --policy.vlm_num_layers=14 \
+  --policy.vlm_dtype=float32 \
+  --policy.device=cuda \
+  --policy.use_amp=true \
+  --policy.use_flash_attn=true \
+  --policy.enable_gradient_checkpointing=true \
+  --policy.gradient_checkpointing_use_reentrant=false \
+  --policy.image_resolution='[448,448]' \
+  --policy.chunk_size=50 \
+  --policy.n_action_steps=50 \
+  --policy.max_state_dim=24 \
+  --policy.max_action_dim=24 \
+  --policy.dropout=0.2 \
+  --policy.optimizer_lr=1e-5 \
+  --policy.optimizer_weight_decay=1e-3 \
+  --policy.optimizer_grad_clip_norm=1.0 \
+  --policy.scheduler_warmup_steps=1000 \
+  --policy.push_to_hub=false \
+  --use_policy_training_preset=true \
+  --batch_size=64 \
+  --steps=80000 \
+  --resume=false \
+  --save_checkpoint=true \
+  --save_checkpoint_to_hub=false \
+  --save_freq=10000 \
+  --log_freq=10 \
+  --env_eval_freq=0 \
+  --num_workers=4 \
+  --prefetch_factor=2 \
+  --persistent_workers=true \
+  --seed=1000 \
+  --wandb.enable=false \
+  --output_dir=./outputs/evo1-libero-stage2-g128-80k
+```
+
+#### Author-format evaluation profile
+
+The author-format EVO1 LIBERO profile uses the raw LIBERO camera feature names
 (`observation.images.agentview_image` and `observation.images.robot0_eye_in_hand_image`), replans every
 14 actions, and binarizes the gripper command before stepping the simulator. The EVO1 policy postprocessor
 can crop the padded 24D action back to the 7D LIBERO action space and apply that gripper binarization. To
-evaluate a LIBERO checkpoint under the same one-episode-per-task setting, keep the raw camera names instead
-of the default `image`/`image2` mapping and set the LIBERO action postprocessing flags:
+evaluate an author-format checkpoint under the same one-episode-per-task setting, keep the raw camera names
+instead of the default `image`/`image2` mapping and set the LIBERO action postprocessing flags:
 
 ```bash
 lerobot-eval \
@@ -181,6 +319,75 @@ lerobot-eval \
   --eval.n_episodes=1
 ```
 
+#### Native `lerobot/libero` v3 profile
+
+Revision `a1aaacb7f6cd6ee5fb43120f673cebb0cfea7dd4` stores camera features as `image` and
+`image2`. This example evaluates all ten LIBERO Object tasks, launching each task in a fresh process:
+
+```bash
+export MUJOCO_GL=egl
+export PYOPENGL_PLATFORM=egl
+
+suite=libero_object
+horizon=280
+for task_id in {0..9}; do
+  lerobot-eval \
+    --policy.path=zuoxingdong/evo1_libero \
+    --policy.pretrained_revision=515921f4a2c1d3f3ad523721eafa26fdf2af315b \
+    --policy.vlm_model_name=OpenGVLab/InternVL3-1B-hf \
+    --policy.device=cuda \
+    --policy.use_amp=true \
+    --policy.vlm_dtype=bfloat16 \
+    --policy.use_flash_attn=false \
+    --policy.enable_gradient_checkpointing=false \
+    --policy.vlm_num_layers=14 \
+    --policy.image_resolution='[448,448]' \
+    --policy.max_text_length=1024 \
+    --policy.chunk_size=50 \
+    --policy.n_action_steps=14 \
+    --policy.max_state_dim=24 \
+    --policy.max_action_dim=24 \
+    --policy.num_inference_timesteps=32 \
+    --policy.postprocess_action_dim=7 \
+    --policy.binarize_gripper=true \
+    --policy.gripper_threshold=0.0 \
+    --policy.gripper_below_threshold_value=-1.0 \
+    --policy.gripper_above_threshold_value=1.0 \
+    --env.type=libero \
+    --env.task="${suite}" \
+    --env.task_ids="[${task_id}]" \
+    --env.camera_name=agentview_image,robot0_eye_in_hand_image \
+    --env.camera_name_mapping="{agentview_image: image, robot0_eye_in_hand_image: image2}" \
+    --env.control_mode=relative \
+    --env.obs_type=pixels_agent_pos \
+    --env.observation_width=448 \
+    --env.observation_height=448 \
+    --env.init_states=true \
+    --env.episode_length="${horizon}" \
+    --env.render_mode=rgb_array \
+    --env.max_parallel_tasks=1 \
+    --eval.n_episodes=50 \
+    --eval.batch_size=1 \
+    --eval.use_async_envs=false \
+    --eval.recording=false \
+    --seed=1000 \
+    --output_dir="./outputs/evo1-libero-stage2-70k-eval/${suite}/task-${task_id}" \
+    --job_name="evo1-libero-stage2-70k-${suite}-task-${task_id}"
+done
+```
+
+Run all ten task IDs for each suite with these horizons:
+
+| `env.task`       | `env.episode_length` |
+| ---------------- | -------------------: |
+| `libero_spatial` |                `280` |
+| `libero_object`  |                `280` |
+| `libero_goal`    |                `300` |
+| `libero_10`      |                `520` |
+
+Set `suite` and `horizon` for each row. This gives 500 episodes per suite and 2,000 episodes overall, while
+the loop's fresh process per task matches the measured RNG-reset topology.
+
 ## References
 
 - [EVO1 repository](https://github.com/MINT-SJTU/Evo-1)

docs/source/libero.mdx

@@ -92,6 +92,20 @@ LIBERO supports two control modes — `relative` (default) and `absolute`. Diffe
 --env.control_mode=relative   # or "absolute"
 ```
 
+### Reset performance
+
+By default, LeRobot preserves LIBERO's hard-reset behavior. With fixed initial
+states enabled, you can opt into soft resets to skip rebuilding the simulator
+model and renderer on every episode:
+
+```bash
+--env.init_states=true --env.hard_reset=false
+```
+
+Soft resets are faster but are not bit-identical to hard resets after the
+environment's settling steps, so camera observations and policy results may
+differ slightly. Use hard resets when reproducing benchmark results.
+
 ### Policy inputs and outputs
 
 **Observations:**

docs/source/libero_plus.mdx

@@ -134,6 +134,20 @@ LIBERO-plus supports two control modes — `relative` (default) and `absolute`.
 --env.control_mode=relative   # or "absolute"
 ```
 
+### Reset performance
+
+By default, LeRobot preserves LIBERO's hard-reset behavior. With fixed initial
+states enabled, you can opt into soft resets to skip rebuilding the simulator
+model and renderer on every episode:
+
+```bash
+--env.init_states=true --env.hard_reset=false
+```
+
+Soft resets are faster but are not bit-identical to hard resets after the
+environment's settling steps, so camera observations and policy results may
+differ slightly. Use hard resets when reproducing benchmark results.
+
 ### Policy inputs and outputs
 
 **Observations:**

pyproject.toml

@@ -87,7 +87,7 @@ dependencies = [
 
     # Build tools (required by opencv-python-headless on some platforms)
     "cmake>=3.29.0.1,<4.2.0",
-    "setuptools>=71.0.0,<81.0.0",
+    "setuptools>=71.0.0,<82.0.0",  # torch 2.11 requires setuptools<82; a higher cap makes the resolver downgrade torch
 ]
 
 # Optional dependencies
@@ -261,7 +261,7 @@ annotations = [
 # Development
 dev = ["pre-commit>=3.7.0,<5.0.0", "debugpy>=1.8.1,<1.9.0", "lerobot[grpcio-dep]", "grpcio-tools>=1.73.1,<2.0.0", "mypy>=1.19.1", "ruff>=0.14.1", "lerobot[notebook]"]
 notebook = ["jupyter>=1.0.0,<2.0.0", "ipykernel>=6.0.0,<7.0.0"]
-test = ["pytest>=8.1.0,<9.0.0", "pytest-timeout>=2.4.0,<3.0.0", "pytest-cov>=5.0.0,<8.0.0", "mock-serial>=0.0.1,<0.1.0 ; sys_platform != 'win32'"]
+test = ["pytest>=8.1.0,<10.0.0", "pytest-timeout>=2.4.0,<3.0.0", "pytest-cov>=5.0.0,<8.0.0", "mock-serial>=0.0.1,<0.1.0 ; sys_platform != 'win32'"]
 video_benchmark = ["scikit-image>=0.23.2,<0.26.0", "pandas>=2.2.2,<2.4.0"]
 
 # Simulation

src/lerobot/cameras/zmq/image_server.py

@@ -102,6 +102,7 @@ class ImageServer:
                 fps=self.fps,
                 width=shape[1],
                 height=shape[0],
+                fourcc=cfg.get("fourcc", "MJPG"),
                 color_mode=ColorMode.RGB,
             )
             camera = OpenCVCamera(cam_config)

src/lerobot/envs/configs.py

@@ -328,6 +328,7 @@ class LiberoEnv(EnvConfig):
     render_mode: str = "rgb_array"
     camera_name: str = "agentview_image,robot0_eye_in_hand_image"
     init_states: bool = True
+    hard_reset: bool = True
     camera_name_mapping: dict[str, str] | None = None
     observation_height: int = 360
     observation_width: int = 360
@@ -356,6 +357,8 @@ class LiberoEnv(EnvConfig):
     def __post_init__(self):
         if self.fps <= 0:
             raise ValueError(f"fps must be positive, got {self.fps}")
+        if not self.hard_reset and not self.init_states:
+            raise ValueError("hard_reset=False requires init_states=True")
 
         if self.obs_type == "pixels":
             self.features[LIBERO_KEY_PIXELS_AGENTVIEW] = PolicyFeature(
@@ -416,6 +419,7 @@ class LiberoEnv(EnvConfig):
             "observation_height": self.observation_height,
             "observation_width": self.observation_width,
             "control_freq": self.fps,
+            "hard_reset": self.hard_reset,
         }
         if self.task_ids is not None:
             kwargs["task_ids"] = self.task_ids

src/lerobot/envs/libero.py

@@ -128,10 +128,13 @@ class LiberoEnv(gym.Env):
         control_freq: int = 20,
         control_mode: str = "relative",
         is_libero_plus: bool = False,
+        hard_reset: bool = True,
     ):
         super().__init__()
         if control_freq <= 0:
             raise ValueError(f"control_freq must be positive, got {control_freq}")
+        if not hard_reset and not init_states:
+            raise ValueError("hard_reset=False requires init_states=True")
         self.task_id = task_id
         self.is_libero_plus = is_libero_plus
         self.obs_type = obs_type
@@ -158,6 +161,7 @@ class LiberoEnv(gym.Env):
         self.camera_name_mapping = camera_name_mapping
         self.num_steps_wait = num_steps_wait
         self.control_freq = control_freq
+        self.hard_reset = hard_reset
         self.episode_index = episode_index
         self.episode_length = episode_length
         # Load once and keep
@@ -265,6 +269,9 @@ class LiberoEnv(gym.Env):
             camera_heights=self.observation_height,
             camera_widths=self.observation_width,
             control_freq=self.control_freq,
+            # Soft resets skip LIBERO's model and renderer rebuild. They are opt-in
+            # because settle steps can make their observations differ from hard resets.
+            hard_reset=self.hard_reset,
         )
         env.reset()
         self._env = env
@@ -377,8 +384,9 @@ class LiberoEnv(gym.Env):
             }
         )
         observation = self._format_raw_obs(raw_obs)
-        if terminated:
-            self.reset()
+        # Return the terminal observation unchanged. The caller owns resetting after
+        # termination; vector envs created below use NEXT_STEP autoreset. Resetting here
+        # would therefore reset twice and skip an initial state.
         truncated = False
         return observation, reward, terminated, truncated, info
 
@@ -476,6 +484,7 @@ def create_libero_envs(
         print(f"Restricting to task_ids={task_ids_filter}")
 
     is_async = env_cls is gym.vector.AsyncVectorEnv
+    is_sync = env_cls is gym.vector.SyncVectorEnv
 
     out: dict[str, dict[int, Any]] = defaultdict(dict)
     for suite_name in suite_names:
@@ -512,6 +521,10 @@ def create_libero_envs(
                     cached_act_space = lazy.action_space
                     cached_metadata = lazy.metadata
                 out[suite_name][tid] = lazy
+            elif is_sync:
+                out[suite_name][tid] = gym.vector.SyncVectorEnv(
+                    fns, autoreset_mode=gym.vector.AutoresetMode.NEXT_STEP
+                )
             else:
                 out[suite_name][tid] = env_cls(fns)
             print(f"Built vec env | suite={suite_name} | task_id={tid} | n_envs={n_envs}")

src/lerobot/envs/utils.py

@@ -212,7 +212,12 @@ class _LazyAsyncVectorEnv:
 
     def _ensure(self) -> None:
         if self._env is None:
-            self._env = gym.vector.AsyncVectorEnv(self._env_fns, context="forkserver", shared_memory=True)
+            self._env = gym.vector.AsyncVectorEnv(
+                self._env_fns,
+                context="forkserver",
+                shared_memory=True,
+                autoreset_mode=gym.vector.AutoresetMode.NEXT_STEP,
+            )
 
     @property
     def unwrapped(self):

src/lerobot/robots/so_follower/robot_kinematic_processor.py

@@ -77,11 +77,13 @@ class EEReferenceAndDelta(RobotActionProcessorStep):
     _command_when_disabled: np.ndarray | None = field(default=None, init=False, repr=False)
 
     def action(self, action: RobotAction) -> RobotAction:
-        observation = self.transition.get(TransitionKey.OBSERVATION).copy()
+        raw_observation = self.transition.get(TransitionKey.OBSERVATION)
 
-        if observation is None:
+        if raw_observation is None:
             raise ValueError("Joints observation is require for computing robot kinematics")
 
+        observation = raw_observation.copy()
+
         if self.use_ik_solution and "IK_solution" in self.transition.get(TransitionKey.COMPLEMENTARY_DATA):
             q_raw = self.transition.get(TransitionKey.COMPLEMENTARY_DATA)["IK_solution"]
         else:
@@ -311,10 +313,12 @@ class InverseKinematicsEEToJoints(RobotActionProcessorStep):
                 "Missing required end-effector pose components: ee.x, ee.y, ee.z, ee.wx, ee.wy, ee.wz, ee.gripper_pos must all be present in action"
             )
 
-        observation = self.transition.get(TransitionKey.OBSERVATION).copy()
-        if observation is None:
+        raw_observation = self.transition.get(TransitionKey.OBSERVATION)
+        if raw_observation is None:
             raise ValueError("Joints observation is require for computing robot kinematics")
 
+        observation = raw_observation.copy()
+
         q_raw = np.array(
             [float(v) for k, v in observation.items() if isinstance(k, str) and k.endswith(".pos")],
             dtype=float,
@@ -391,13 +395,15 @@ class GripperVelocityToJoint(RobotActionProcessorStep):
     discrete_gripper: bool = False
 
     def action(self, action: RobotAction) -> RobotAction:
-        observation = self.transition.get(TransitionKey.OBSERVATION).copy()
+        raw_observation = self.transition.get(TransitionKey.OBSERVATION)
 
         gripper_vel = action.pop("ee.gripper_vel")
 
-        if observation is None:
+        if raw_observation is None:
             raise ValueError("Joints observation is require for computing robot kinematics")
 
+        observation = raw_observation.copy()
+
         q_raw = np.array(
             [float(v) for k, v in observation.items() if isinstance(k, str) and k.endswith(".pos")],
             dtype=float,
@@ -583,10 +589,12 @@ class InverseKinematicsRLStep(ProcessorStep):
                 "Missing required end-effector pose components: ee.x, ee.y, ee.z, ee.wx, ee.wy, ee.wz, ee.gripper_pos must all be present in action"
             )
 
-        observation = new_transition.get(TransitionKey.OBSERVATION).copy()
-        if observation is None:
+        raw_observation = new_transition.get(TransitionKey.OBSERVATION)
+        if raw_observation is None:
             raise ValueError("Joints observation is require for computing robot kinematics")
 
+        observation = raw_observation.copy()
+
         q_raw = np.array(
             [float(v) for k, v in observation.items() if isinstance(k, str) and k.endswith(".pos")],
             dtype=float,

src/lerobot/scripts/lerobot_edit_dataset.py

@@ -240,6 +240,7 @@ Using JSON config file:
 
 import abc
 import logging
+import os
 import shutil
 import sys
 from dataclasses import dataclass, field
@@ -384,24 +385,35 @@ def _resolve_io_paths(
     return output_repo_id, input_path, output_path
 
 
+def _is_in_place(input_path: Path, output_path: Path) -> bool:
+    """Whether both paths point to the same dataset directory.
+
+    Uses os.path.samefile (device+inode) which is robust to case-insensitive filesystems, hardlinks
+    and symlinks.
+    """
+    try:
+        return os.path.samefile(input_path, output_path)
+    except OSError:
+        return False
+
+
 def get_output_path(
     repo_id: str,
     new_repo_id: str | None,
     root: Path | str | None,
     new_root: Path | str | None,
-) -> tuple[str, Path]:
+) -> tuple[str, Path, Path | None]:
     output_repo_id, input_path, output_path = _resolve_io_paths(repo_id, new_repo_id, root, new_root)
 
-    # In case of in-place modification, create a backup of the original dataset (if it exists)
-    if output_path == input_path:
+    # In case of in-place modification, create a backup of the original dataset (if it exists).
+    backup_path: Path | None = None
+    if _is_in_place(input_path, output_path):
         backup_path = input_path.with_name(input_path.name + "_old")
-
-        if input_path.exists():
         if backup_path.exists():
             shutil.rmtree(backup_path)
         shutil.move(input_path, backup_path)
 
-    return output_repo_id, output_path
+    return output_repo_id, output_path, backup_path
 
 
 def handle_delete_episodes(cfg: EditDatasetConfig) -> None:
@@ -412,7 +424,7 @@ def handle_delete_episodes(cfg: EditDatasetConfig) -> None:
         raise ValueError("episode_indices must be specified for delete_episodes operation")
 
     dataset = LeRobotDataset(cfg.repo_id, root=cfg.root)
-    output_repo_id, output_dir = get_output_path(
+    output_repo_id, output_dir, backup_path = get_output_path(
         cfg.repo_id,
         new_repo_id=cfg.new_repo_id,
         root=cfg.root,
@@ -420,8 +432,8 @@ def handle_delete_episodes(cfg: EditDatasetConfig) -> None:
     )
 
     # In case of in-place modification, make the dataset point to the backup directory
-    if output_dir == dataset.root:
-        dataset.root = dataset.root.with_name(dataset.root.name + "_old")
+    if backup_path is not None:
+        dataset.root = backup_path
 
     logging.info(f"Deleting episodes {cfg.operation.episode_indices} from {cfg.repo_id}")
     new_dataset = delete_episodes(
@@ -525,7 +537,7 @@ def handle_remove_feature(cfg: EditDatasetConfig) -> None:
         raise ValueError("feature_names must be specified for remove_feature operation")
 
     dataset = LeRobotDataset(cfg.repo_id, root=cfg.root)
-    output_repo_id, output_dir = get_output_path(
+    output_repo_id, output_dir, backup_path = get_output_path(
         cfg.repo_id,
         new_repo_id=cfg.new_repo_id,
         root=cfg.root,
@@ -533,8 +545,8 @@ def handle_remove_feature(cfg: EditDatasetConfig) -> None:
     )
 
     # In case of in-place modification, make the dataset point to the backup directory
-    if output_dir == dataset.root:
-        dataset.root = dataset.root.with_name(dataset.root.name + "_old")
+    if backup_path is not None:
+        dataset.root = backup_path
 
     logging.info(f"Removing features {cfg.operation.feature_names} from {cfg.repo_id}")
     new_dataset = remove_feature(
@@ -671,7 +683,7 @@ def handle_recompute_stats(cfg: EditDatasetConfig) -> None:
         cfg.new_root,
         default_new_repo_id=f"{cfg.repo_id}_recomputed_stats",
     )
-    in_place = output_root == input_root
+    in_place = _is_in_place(input_root, output_root)
 
     if in_place and not cfg.operation.overwrite:
         raise ValueError(
@@ -731,7 +743,7 @@ def handle_reencode_videos(cfg: EditDatasetConfig) -> None:
         cfg.new_root,
         default_new_repo_id=f"{cfg.repo_id}_reencoded",
     )
-    in_place = output_root == input_root
+    in_place = _is_in_place(input_root, output_root)
 
     if in_place and not cfg.operation.overwrite:
         raise ValueError(

src/lerobot/scripts/lerobot_train.py

@@ -243,9 +243,17 @@ def train(cfg: TrainPipelineConfig, accelerator: "Accelerator | None" = None):
         # Accelerate auto-detects the device based on the available hardware and ignores the policy.device setting.
         # Force the device to be CPU when the active config's device is set to CPU (works for both policy and reward model training).
         force_cpu = cfg.trainable_config.device == "cpu"
-        # Drive Accelerate's autocast from policy.dtype (bf16/fp16 activate it; float32/absent -> launcher default).
+        # Drive Accelerate's autocast from policy.dtype (bf16/fp16 activate it; float32 -> full precision).
+        has_policy_dtype = hasattr(cfg.trainable_config, "dtype")
         policy_dtype = getattr(cfg.trainable_config, "dtype", None)
         mixed_precision = {"bfloat16": "bf16", "float16": "fp16", "float32": "no"}.get(policy_dtype)
+        # Policies without a `dtype` field fall back to `use_amp`, which would otherwise be
+        # silently ignored here while lerobot-eval honors it. Follow torch.autocast's default
+        # for the configured device so training and evaluation use the same precision.
+        if not has_policy_dtype and getattr(cfg.trainable_config, "use_amp", False):
+            device_type = torch.device(cfg.trainable_config.device).type
+            autocast_dtype = torch.get_autocast_dtype(device_type)
+            mixed_precision = {torch.bfloat16: "bf16", torch.float16: "fp16"}[autocast_dtype]
         accelerator = Accelerator(
             step_scheduler_with_optimizer=False,
             mixed_precision=mixed_precision,

tests/robots/test_robot_kinematic_processor.py

@@ -17,9 +17,14 @@
 import pytest
 
 from lerobot.configs import FeatureType, PipelineFeatureType, PolicyFeature
+from lerobot.processor.converters import create_transition
 from lerobot.robots.so_follower.robot_kinematic_processor import (
+    EEReferenceAndDelta,
     ForwardKinematicsJointsToEEAction,
     ForwardKinematicsJointsToEEObservation,
+    GripperVelocityToJoint,
+    InverseKinematicsEEToJoints,
+    InverseKinematicsRLStep,
 )
 
 MOTOR_NAMES = ["shoulder_pan", "shoulder_lift", "elbow_flex", "wrist_flex", "wrist_roll", "gripper"]
@@ -43,3 +48,38 @@ def test_fk_feature_schema(step_cls, 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}
+
+
+EE_ACTION = dict.fromkeys(EE_KEYS, 0.0)
+
+
+@pytest.mark.parametrize(
+    ("step", "action"),
+    [
+        (
+            EEReferenceAndDelta(kinematics=None, end_effector_step_sizes={}, motor_names=MOTOR_NAMES),
+            dict(EE_ACTION),
+        ),
+        (InverseKinematicsEEToJoints(kinematics=None, motor_names=MOTOR_NAMES), dict(EE_ACTION)),
+        (GripperVelocityToJoint(), {**EE_ACTION, "ee.gripper_vel": 0.0}),
+        (InverseKinematicsRLStep(kinematics=None, motor_names=MOTOR_NAMES), dict(EE_ACTION)),
+    ],
+    ids=[
+        "ee_reference_and_delta",
+        "inverse_kinematics_ee_to_joints",
+        "gripper_velocity_to_joint",
+        "inverse_kinematics_rl_step",
+    ],
+)
+def test_missing_observation_raises_value_error(step, action):
+    """A transition without an observation must surface the documented ValueError.
+
+    `RobotProcessorPipeline.process_action` builds its transition with
+    `create_transition(action=...)`, which sets `TransitionKey.OBSERVATION` to None.
+    These steps used to call `.copy()` on that before the None check, so the guard
+    below them was unreachable and an AttributeError escaped instead.
+    """
+    transition = create_transition(action=action)
+
+    with pytest.raises(ValueError, match="Joints observation"):
+        step(transition)

uv.lock

@@ -1,5 +1,5 @@
 version = 1
-revision = 2
+revision = 3
 requires-python = ">=3.12"
 resolution-markers = [
     "(python_full_version >= '3.15' and platform_machine == 'AMD64' and sys_platform == 'linux') or (python_full_version >= '3.15' and platform_machine == 'x86_64' and sys_platform == 'linux')",
@@ -402,10 +402,10 @@ name = "bddl"
 version = "1.0.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "jupytext", marker = "sys_platform == 'linux'" },
-    { name = "networkx", marker = "sys_platform == 'linux'" },
-    { name = "numpy", marker = "sys_platform == 'linux'" },
-    { name = "pytest", marker = "sys_platform == 'linux'" },
+    { name = "jupytext" },
+    { name = "networkx" },
+    { name = "numpy" },
+    { name = "pytest" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/5c/37/0211f82891a9f14efcfd2b2096f8d9e4351398ad637fdd1ee59cfc580b0e/bddl-1.0.1.tar.gz", hash = "sha256:1fa4e6e5050b93888ff6fd8455c39bfb29d3864ce06b4c37c0f781f513a2ae26", size = 164809, upload-time = "2022-03-08T01:48:23.564Z" }
 
@@ -1010,7 +1010,7 @@ name = "cuda-bindings"
 version = "12.9.7"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "cuda-pathfinder", marker = "sys_platform == 'linux'" },
+    { name = "cuda-pathfinder" },
 ]
 wheels = [
     { url = "https://files.pythonhosted.org/packages/32/45/557d4ed1fa54f0c7db8aee083229f624990d69f7d00f55477eed5c7e169a/cuda_bindings-12.9.7-cp312-cp312-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:0666d3c082ef8f4b2d670950589373550e9f3bf564d635dd883f24a0b40402ff", size = 7071026, upload-time = "2026-05-27T18:44:13.356Z" },
@@ -1043,37 +1043,37 @@ wheels = [
 
 [package.optional-dependencies]
 cublas = [
-    { name = "nvidia-cublas-cu12", marker = "sys_platform == 'linux'" },
+    { name = "nvidia-cublas-cu12" },
 ]
 cudart = [
-    { name = "nvidia-cuda-runtime-cu12", marker = "sys_platform == 'linux'" },
+    { name = "nvidia-cuda-runtime-cu12" },
 ]
 cufft = [
-    { name = "nvidia-cufft-cu12", marker = "sys_platform == 'linux'" },
+    { name = "nvidia-cufft-cu12" },
 ]
 cufile = [
-    { name = "nvidia-cufile-cu12", marker = "sys_platform == 'linux'" },
+    { name = "nvidia-cufile-cu12" },
 ]
 cupti = [
-    { name = "nvidia-cuda-cupti-cu12", marker = "sys_platform == 'linux'" },
+    { name = "nvidia-cuda-cupti-cu12" },
 ]
 curand = [
-    { name = "nvidia-curand-cu12", marker = "sys_platform == 'linux'" },
+    { name = "nvidia-curand-cu12" },
 ]
 cusolver = [
-    { name = "nvidia-cusolver-cu12", marker = "sys_platform == 'linux'" },
+    { name = "nvidia-cusolver-cu12" },
 ]
 cusparse = [
-    { name = "nvidia-cusparse-cu12", marker = "sys_platform == 'linux'" },
+    { name = "nvidia-cusparse-cu12" },
 ]
 nvjitlink = [
-    { name = "nvidia-nvjitlink-cu12", marker = "sys_platform == 'linux'" },
+    { name = "nvidia-nvjitlink-cu12" },
 ]
 nvrtc = [
-    { name = "nvidia-cuda-nvrtc-cu12", marker = "sys_platform == 'linux'" },
+    { name = "nvidia-cuda-nvrtc-cu12" },
 ]
 nvtx = [
-    { name = "nvidia-nvtx-cu12", marker = "sys_platform == 'linux'" },
+    { name = "nvidia-nvtx-cu12" },
 ]
 
 [[package]]
@@ -1145,7 +1145,7 @@ name = "decord"
 version = "0.6.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "numpy", marker = "(platform_machine != 'arm64' and sys_platform == 'darwin') or (platform_machine == 'AMD64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux')" },
+    { name = "numpy" },
 ]
 wheels = [
     { url = "https://files.pythonhosted.org/packages/11/79/936af42edf90a7bd4e41a6cac89c913d4b47fa48a26b042d5129a9242ee3/decord-0.6.0-py3-none-manylinux2010_x86_64.whl", hash = "sha256:51997f20be8958e23b7c4061ba45d0efcd86bffd5fe81c695d0befee0d442976", size = 13602299, upload-time = "2021-06-14T21:30:55.486Z" },
@@ -1283,10 +1283,10 @@ resolution-markers = [
     "python_full_version == '3.14.*' and sys_platform == 'win32'",
 ]
 dependencies = [
-    { name = "absl-py", marker = "python_full_version >= '3.14'" },
-    { name = "attrs", marker = "python_full_version >= '3.14'" },
-    { name = "numpy", marker = "python_full_version >= '3.14'" },
-    { name = "wrapt", marker = "python_full_version >= '3.14'" },
+    { name = "absl-py" },
+    { name = "attrs" },
+    { name = "numpy" },
+    { name = "wrapt" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/a6/83/ce29720ccf934c6cfa9b9c95ebbe96558386e66886626066632b5e44afed/dm_tree-0.1.9.tar.gz", hash = "sha256:a4c7db3d3935a5a2d5e4b383fc26c6b0cd6f78c6d4605d3e7b518800ecd5342b", size = 35623, upload-time = "2025-01-30T20:45:37.13Z" }
 wheels = [
@@ -1324,10 +1324,10 @@ resolution-markers = [
     "python_full_version < '3.13' and sys_platform == 'win32'",
 ]
 dependencies = [
-    { name = "absl-py", marker = "python_full_version < '3.14'" },
-    { name = "attrs", marker = "python_full_version < '3.14'" },
-    { name = "numpy", marker = "python_full_version < '3.14'" },
-    { name = "wrapt", marker = "python_full_version < '3.14'" },
+    { name = "absl-py" },
+    { name = "attrs" },
+    { name = "numpy" },
+    { name = "wrapt" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/5a/66/a3ec619d22b6baffa5ab853e8dc6ec9d0c837127948af59bb15b988d7312/dm_tree-0.1.10.tar.gz", hash = "sha256:22f37b599e01cc3402a17f79c257a802aebd8d326de05b54657650845956208a", size = 35748, upload-time = "2026-03-31T17:35:39.03Z" }
 wheels = [
@@ -1911,7 +1911,7 @@ name = "h5py"
 version = "3.16.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "numpy", marker = "sys_platform == 'linux'" },
+    { name = "numpy" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/db/33/acd0ce6863b6c0d7735007df01815403f5589a21ff8c2e1ee2587a38f548/h5py-3.16.0.tar.gz", hash = "sha256:a0dbaad796840ccaa67a4c144a0d0c8080073c34c76d5a6941d6818678ef2738", size = 446526, upload-time = "2026-03-06T13:49:08.07Z" }
 wheels = [
@@ -1955,23 +1955,23 @@ name = "hf-libero"
 version = "0.1.4"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "bddl", marker = "sys_platform == 'linux'" },
-    { name = "cloudpickle", marker = "sys_platform == 'linux'" },
-    { name = "easydict", marker = "sys_platform == 'linux'" },
-    { name = "einops", marker = "sys_platform == 'linux'" },
-    { name = "future", marker = "sys_platform == 'linux'" },
-    { name = "gymnasium", marker = "sys_platform == 'linux'" },
-    { name = "hf-egl-probe", marker = "sys_platform == 'linux'" },
-    { name = "hydra-core", marker = "sys_platform == 'linux'" },
-    { name = "matplotlib", marker = "sys_platform == 'linux'" },
-    { name = "mujoco", marker = "sys_platform == 'linux'" },
-    { name = "numpy", marker = "sys_platform == 'linux'" },
-    { name = "opencv-python", marker = "sys_platform == 'linux'" },
-    { name = "robomimic", marker = "sys_platform == 'linux'" },
-    { name = "robosuite", marker = "sys_platform == 'linux'" },
-    { name = "thop", marker = "sys_platform == 'linux'" },
-    { name = "transformers", marker = "sys_platform == 'linux'" },
-    { name = "wandb", marker = "sys_platform == 'linux'" },
+    { name = "bddl" },
+    { name = "cloudpickle" },
+    { name = "easydict" },
+    { name = "einops" },
+    { name = "future" },
+    { name = "gymnasium" },
+    { name = "hf-egl-probe" },
+    { name = "hydra-core" },
+    { name = "matplotlib" },
+    { name = "mujoco" },
+    { name = "numpy" },
+    { name = "opencv-python" },
+    { name = "robomimic" },
+    { name = "robosuite" },
+    { name = "thop" },
+    { name = "transformers" },
+    { name = "wandb" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/af/aa/4e9eb8715e0bff9cb6553db563a35d253393097d446f82bd53575e8b253d/hf_libero-0.1.4.tar.gz", hash = "sha256:c058d67ad5a2b589529c14d614282ef4cca3a7763dafa134f58a6c9039657e34", size = 2961319, upload-time = "2026-06-10T09:56:13.994Z" }
 wheels = [
@@ -2122,9 +2122,9 @@ name = "hydra-core"
 version = "1.3.4"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "antlr4-python3-runtime", marker = "sys_platform == 'linux'" },
-    { name = "omegaconf", marker = "sys_platform == 'linux'" },
-    { name = "packaging", marker = "sys_platform == 'linux'" },
+    { name = "antlr4-python3-runtime" },
+    { name = "omegaconf" },
+    { name = "packaging" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/10/dd/220f0e91743136725352497e98540772a01fc7c3ab96ff16c3c74424e984/hydra_core-1.3.4.tar.gz", hash = "sha256:ad0f7b05a0242255a8984d5a4ed2f6847f7b783ed727368a2c0155ec52d6c34c", size = 3263348, upload-time = "2026-07-04T16:25:38.891Z" }
 wheels = [
@@ -2677,11 +2677,11 @@ name = "jupytext"
 version = "1.19.5"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "markdown-it-py", marker = "sys_platform == 'linux'" },
-    { name = "mdit-py-plugins", marker = "sys_platform == 'linux'" },
-    { name = "nbformat", marker = "sys_platform == 'linux'" },
-    { name = "packaging", marker = "sys_platform == 'linux'" },
-    { name = "pyyaml", marker = "sys_platform == 'linux'" },
+    { name = "markdown-it-py" },
+    { name = "mdit-py-plugins" },
+    { name = "nbformat" },
+    { name = "packaging" },
+    { name = "pyyaml" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/a1/ca/473f8ebb101553fb2ea6ab1d34324d6677844c968947ac050c759d539f2c/jupytext-1.19.5.tar.gz", hash = "sha256:605026446d605aa54fd7f7fc69df6ae51c7a46053d4cebf05afdc64d66de3df0", size = 4600916, upload-time = "2026-07-21T22:00:29.198Z" }
 wheels = [
@@ -3471,7 +3471,7 @@ requires-dist = [
     { name = "pyrealsense2", marker = "sys_platform != 'darwin' and extra == 'intelrealsense'", specifier = ">=2.55.1.6486,<2.57.0" },
     { name = "pyrealsense2-macosx", marker = "sys_platform == 'darwin' and extra == 'intelrealsense'", specifier = ">=2.54,<2.57.0" },
     { name = "pyserial", marker = "extra == 'pyserial-dep'", specifier = ">=3.5,<4.0" },
-    { name = "pytest", marker = "extra == 'test'", specifier = ">=8.1.0,<9.0.0" },
+    { name = "pytest", marker = "extra == 'test'", specifier = ">=8.1.0,<10.0.0" },
     { name = "pytest-cov", marker = "extra == 'test'", specifier = ">=5.0.0,<8.0.0" },
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@@ -6682,9 +6682,9 @@ resolution-markers = [
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 ]
 dependencies = [
-    { name = "numpy", marker = "sys_platform != 'linux'" },
-    { name = "pillow", marker = "sys_platform != 'linux'" },
-    { name = "torch", version = "2.11.0", source = { registry = "https://pypi.org/simple" }, marker = "sys_platform != 'linux'" },
+    { name = "numpy" },
+    { name = "pillow" },
+    { name = "torch", version = "2.11.0", source = { registry = "https://pypi.org/simple" } },
 ]
 wheels = [
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@@ -6718,9 +6718,9 @@ resolution-markers = [
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 dependencies = [
-    { name = "numpy", marker = "sys_platform == 'linux'" },
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-    { name = "torch", version = "2.11.0+cu128", source = { registry = "https://download.pytorch.org/whl/cu128" }, marker = "sys_platform == 'linux'" },
+    { name = "numpy" },
+    { name = "pillow" },
+    { name = "torch", version = "2.11.0+cu128", source = { registry = "https://download.pytorch.org/whl/cu128" } },
 ]
 wheels = [
     { url = "https://download-r2.pytorch.org/whl/cu128/torchvision-0.26.0%2Bcu128-cp312-cp312-manylinux_2_28_aarch64.whl", hash = "sha256:63e35234aed13b6edda37056f417b5c281249669db631e706811917af36b21d7", upload-time = "2026-04-09T23:21:35Z" },
@@ -7222,7 +7222,7 @@ name = "werkzeug"
 version = "3.1.8"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "markupsafe", marker = "sys_platform == 'linux'" },
+    { name = "markupsafe" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/dd/b2/381be8cfdee792dd117872481b6e378f85c957dd7c5bca38897b08f765fd/werkzeug-3.1.8.tar.gz", hash = "sha256:9bad61a4268dac112f1c5cd4630a56ede601b6ed420300677a869083d70a4c44", size = 875852, upload-time = "2026-04-02T18:49:14.268Z" }
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