improve script, time saving subtask array

Signed-off-by: Jade Choghari <chogharijade@gmail.com>

.github/workflows/full_tests.yml

@@ -101,11 +101,9 @@ jobs:
     runs-on:
       group: aws-general-8-plus
     if: |
-      github.repository == 'huggingface/lerobot' && (
-        (github.event_name == 'pull_request_review' && github.event.review.state == 'approved' && github.event.pull_request.head.repo.fork == false) ||
-        github.event_name == 'push' ||
-        github.event_name == 'workflow_dispatch'
-      )
+      (github.event_name == 'pull_request_review' && github.event.review.state == 'approved' && github.event.pull_request.head.repo.fork == false) ||
+      github.event_name == 'push' ||
+      github.event_name == 'workflow_dispatch'
     outputs:
       image_tag: ${{ steps.set_tag.outputs.image_tag }}
     env:

.github/workflows/unbound_deps_tests.yml

@@ -91,7 +91,6 @@ jobs:
     name: Build and Push Docker
     runs-on:
       group: aws-general-8-plus
-    if: github.repository == 'huggingface/lerobot'
     outputs:
       image_tag: ${{ env.DOCKER_IMAGE_NAME }}
     env:

docs/source/_toctree.yml

@@ -27,6 +27,8 @@
     title: Porting Large Datasets
   - local: using_dataset_tools
     title: Using the Dataset Tools
+  - local: annotation_tools
+    title: Using the Annotation Tools
   - local: dataset_subtask
     title: Using Subtasks in the Dataset
   title: "Datasets"

docs/source/annotation_tools.mdx

@@ -0,0 +1,425 @@
+# Dataset Annotation Tools
+
+This guide explains how to use the automatic annotation tools to add skill labels and synthetic dialogue to your LeRobot datasets.
+
+## Overview
+
+The annotation pipeline consists of two main components:
+
+1. **Subtask Annotation** (`subtask_annotate.py`): Automatically segments robot demonstrations into atomic skills using Vision-Language Models (VLMs)
+2. **High-Level Annotation** (`high_level_annotate.py`): Generates synthetic user prompts and robot utterances for hierarchical policy training
+
+These tools enable you to transform raw robot demonstration data into richly annotated datasets suitable for training hierarchical policies.
+
+## Installation Requirements
+
+Before using the annotation tools, ensure you have the required dependencies:
+
+```bash
+pip install transformers qwen-vl-utils opencv-python rich pandas pyarrow
+```
+
+You'll also need FFmpeg for video processing:
+
+```bash
+# Ubuntu/Debian
+sudo apt-get install ffmpeg
+
+# macOS
+brew install ffmpeg
+```
+
+## Part 1: Subtask Annotation
+
+### What It Does
+
+The subtask annotator segments each episode into short atomic manipulation skills (1-3 seconds each). For example, a "pick and place" episode might be segmented into:
+- "reach towards object" (0.0s - 1.2s)
+- "grasp object" (1.2s - 2.1s)
+- "lift object" (2.1s - 3.5s)
+- "move to target" (3.5s - 5.0s)
+- "release object" (5.0s - 6.2s)
+
+### Usage
+
+#### Basic Example
+
+```bash
+python src/lerobot/policies/pi05_full/annotate/subtask_annotate.py \
+    --repo-id your-username/your-dataset \
+    --video-key observation.images.base \
+    --output-dir /path/to/output
+```
+
+#### With Local Dataset
+
+```bash
+python src/lerobot/policies/pi05_full/annotate/subtask_annotate.py \
+    --data-dir /path/to/local/dataset \
+    --video-key observation.images.base \
+    --output-dir /path/to/output
+```
+
+#### Advanced Options
+
+```bash
+python src/lerobot/policies/pi05_full/annotate/subtask_annotate.py \
+    --repo-id your-username/your-dataset \
+    --video-key observation.images.base \
+    --model Qwen/Qwen2-VL-7B-Instruct \
+    --batch-size 16 \
+    --output-dir /path/to/output \
+    --push-to-hub
+```
+
+### Parameters
+
+| Parameter | Description | Default |
+|-----------|-------------|---------|
+| `--repo-id` | HuggingFace Hub dataset ID | Required (or use --data-dir) |
+| `--data-dir` | Path to local dataset | Required (or use --repo-id) |
+| `--video-key` | Video observation key | Required |
+| `--model` | VLM model to use | `Qwen/Qwen2-VL-7B-Instruct` |
+| `--device` | Device to run model on | `cuda` |
+| `--dtype` | Model dtype | `bfloat16` |
+| `--batch-size` | Episodes per batch | `8` |
+| `--episodes` | Specific episodes to annotate | All episodes |
+| `--output-dir` | Output directory | Auto-generated |
+| `--push-to-hub` | Push to HuggingFace Hub | `False` |
+
+### Supported Models
+
+- **Qwen2-VL**: `Qwen/Qwen2-VL-2B-Instruct`, `Qwen/Qwen2-VL-7B-Instruct`, `Qwen/Qwen2-VL-72B-Instruct`
+- **Qwen3-VL**: `Qwen/Qwen3-VL-30B-A3B-Instruct`
+
+### Output Files
+
+The subtask annotation creates the following files in your dataset:
+
+1. **`meta/subtasks.parquet`**: DataFrame with unique subtask names
+   ```python
+   # Structure:
+   # Index: subtask name (string)
+   # Column: subtask_index (int64)
+   ```
+
+2. **`meta/skills.json`**: Raw skill annotations with timestamps
+   ```json
+   {
+     "coarse_description": "Pick and place the object",
+     "skill_to_subtask_index": {
+       "reach towards object": 0,
+       "grasp object": 1,
+       ...
+     },
+     "episodes": {
+       "0": {
+         "episode_index": 0,
+         "description": "Pick and place the object",
+         "skills": [
+           {"name": "reach towards object", "start": 0.0, "end": 1.2},
+           {"name": "grasp object", "start": 1.2, "end": 2.1},
+           ...
+         ]
+       }
+     }
+   }
+   ```
+
+3. **`subtask_index` feature**: Added to each frame in the dataset
+   - Type: `int64`
+   - Shape: `(1,)`
+   - Maps each frame to its corresponding subtask
+
+### Accessing Subtask Annotations
+
+```python
+from lerobot.datasets.lerobot_dataset import LeRobotDataset
+
+# Load annotated dataset
+dataset = LeRobotDataset(repo_id="your/dataset_with_subtasks")
+
+# Get a frame
+frame = dataset[100]
+
+# Get the subtask for this frame
+subtask_idx = frame["subtask_index"].item()
+subtask_name = dataset.meta.subtasks.iloc[subtask_idx].name
+
+print(f"Frame 100 is performing: {subtask_name}")
+
+# Load all subtasks
+subtasks_df = dataset.meta.subtasks
+print(subtasks_df)
+```
+
+## Part 2: High-Level Annotation
+
+### What It Does
+
+The high-level annotator generates synthetic dialogue for hierarchical policy training. For each skill, it creates:
+- **User Prompt** (`ℓ_t`): A natural language request from the user
+- **Robot Utterance** (`u_t`): A natural language response from the robot
+
+This enables training policies that can understand and respond to human instructions in natural dialogue.
+
+### Prerequisites
+
+**Important**: You must run subtask annotation first! High-level annotation requires the `skills.json` file generated by subtask annotation.
+
+### Usage
+
+#### Image Mode (Default)
+
+Samples frames at regular intervals and passes images to the VLM:
+
+```bash
+python src/lerobot/policies/pi05_full/annotate/high_level_annotate.py \
+    --repo-id your/dataset_with_subtasks \
+    --model Qwen/Qwen2-VL-7B-Instruct \
+    --image-key observation.images.base \
+    --output-dir /path/to/output
+```
+
+#### Video Mode
+
+Passes entire episode videos to the VLM for better temporal understanding:
+
+```bash
+python src/lerobot/policies/pi05_full/annotate/high_level_annotate.py \
+    --repo-id your/dataset_with_subtasks \
+    --model Qwen/Qwen2-VL-7B-Instruct \
+    --video-mode \
+    --video-key observation.images.base \
+    --video-batch-size 4 \
+    --output-dir /path/to/output
+```
+
+### Parameters
+
+| Parameter | Description | Default |
+|-----------|-------------|---------|
+| `--repo-id` | HuggingFace Hub dataset ID | Required (or use --data-dir) |
+| `--data-dir` | Path to local dataset | Required (or use --repo-id) |
+| `--model` | VLM model to use | `Qwen/Qwen2-VL-7B-Instruct` |
+| `--image-key` | Image observation key (image mode) | First camera key |
+| `--video-mode` | Use video instead of images | `False` |
+| `--video-key` | Video observation key (video mode) | Auto-detected |
+| `--video-batch-size` | Episodes per batch (video mode) | `1` |
+| `--sample-interval` | Sampling interval in seconds | `1.0` |
+| `--temperature` | Sampling temperature | `0.7` |
+| `--output-dir` | Output directory | Auto-generated |
+| `--push-to-hub` | Push to HuggingFace Hub | `False` |
+
+### Output Files
+
+The high-level annotation creates:
+
+1. **`meta/tasks_high_level.parquet`**: DataFrame with high-level tasks
+   ```python
+   # Structure:
+   # Index: task string (concatenated user_prompt | robot_utterance)
+   # Columns:
+   #   - task_index: int64
+   #   - user_prompt: string
+   #   - robot_utterance: string
+   #   - skill: string (associated subtask)
+   #   - scenario_type: string
+   #   - response_type: string
+   ```
+
+2. **`meta/syn_annotations.jsonl`**: Debug annotations (JSONL format)
+   ```json
+   {"episode_id": 0, "timestamp": 1.5, "skill_current": "grasp object", "user_prompt": "Can you pick that up?", "robot_utterance": "Sure, I'll grasp it now", ...}
+   ```
+
+3. **`task_index_high_level` feature**: Added to each frame
+   - Type: `int64`
+   - Shape: `(1,)`
+   - Maps each frame to its high-level task
+
+### Dialogue Types Generated
+
+The system generates diverse interaction types:
+
+**Scenario Types:**
+- `specific_object`: "Pick up the red block"
+- `negative_task`: "Don't touch the blue one"
+- `situated_correction`: "Actually, move to the other box instead"
+- `implicit_request`: "I need something red for the tower"
+- `constraint_based`: "Make sure to handle it gently"
+
+**Response Types:**
+- `confirmation`: "OK, I'll pick it up"
+- `clarification`: "Just to confirm, you want me to pick up the red block?"
+- `acknowledgment`: "Got it, picking up the red block"
+- `constraint_acknowledgment`: "Sure, I'll pick it up gently"
+
+### Accessing High-Level Annotations
+
+```python
+from lerobot.datasets.lerobot_dataset import LeRobotDataset
+import pandas as pd
+
+# Load annotated dataset
+dataset = LeRobotDataset(repo_id="your/dataset_with_high_level_tasks")
+
+# Get a frame
+frame = dataset[100]
+
+# Get the high-level task
+task_idx = frame["task_index_high_level"].item()
+
+# Load tasks metadata
+tasks_df = pd.read_parquet(dataset.root / "meta" / "tasks_high_level.parquet")
+task_row = tasks_df[tasks_df["task_index"] == task_idx].iloc[0]
+
+print(f"User: {task_row['user_prompt']}")
+print(f"Robot: {task_row['robot_utterance']}")
+print(f"Skill: {task_row['skill']}")
+
+# Use in a DataLoader
+import torch
+from torch.utils.data import DataLoader
+
+dataloader = DataLoader(dataset, batch_size=32, shuffle=True)
+batch = next(iter(dataloader))
+
+print(f"Task indices: {batch['task_index_high_level']}")
+print(f"User prompts: {batch['user_prompt'][0]}")
+print(f"Robot utterances: {batch['robot_utterance'][0]}")
+```
+
+## Complete Pipeline Example
+
+Here's how to run both annotation stages:
+
+```bash
+#!/bin/bash
+
+REPO_ID="your-username/your-dataset"
+MODEL="Qwen/Qwen2-VL-7B-Instruct"
+OUTPUT_DIR="/path/to/output"
+
+# Step 1: Subtask Annotation
+python src/lerobot/policies/pi05_full/annotate/subtask_annotate.py \
+    --repo-id "$REPO_ID" \
+    --video-key observation.images.base \
+    --model "$MODEL" \
+    --batch-size 8 \
+    --output-dir "${OUTPUT_DIR}/subtasks"
+
+# Step 2: High-Level Annotation (Image Mode)
+python src/lerobot/policies/pi05_full/annotate/high_level_annotate.py \
+    --data-dir "${OUTPUT_DIR}/subtasks" \
+    --model "$MODEL" \
+    --image-key observation.images.base \
+    --sample-interval 1.0 \
+    --output-dir "${OUTPUT_DIR}/final"
+
+# Or Step 2: High-Level Annotation (Video Mode - Recommended)
+python src/lerobot/policies/pi05_full/annotate/high_level_annotate.py \
+    --data-dir "${OUTPUT_DIR}/subtasks" \
+    --model "$MODEL" \
+    --video-mode \
+    --video-key observation.images.base \
+    --video-batch-size 4 \
+    --output-dir "${OUTPUT_DIR}/final"
+```
+
+## Performance Tips
+
+### For Faster Processing
+
+1. **Increase batch size**: Use `--batch-size 16` or higher (subtask annotation)
+2. **Increase video batch size**: Use `--video-batch-size 8` (high-level annotation in video mode)
+3. **Larger sampling interval**: Use `--sample-interval 5.0` for testing (samples every 5 seconds instead of 1)
+4. **Use smaller models**: `Qwen/Qwen2-VL-2B-Instruct` is faster than `Qwen2-VL-7B-Instruct`
+5. **Process specific episodes**: Use `--episodes 0 1 2 3` to annotate only a subset
+
+### For Better Quality
+
+1. **Use larger models**: `Qwen/Qwen3-VL-30B-A3B-Instruct` or `Qwen/Qwen2-VL-72B-Instruct`
+2. **Use video mode**: Provides better temporal context
+3. **Smaller sampling intervals**: `--sample-interval 0.5` for dense annotations
+4. **Adjust temperature**: Use `--temperature 0.9` for more diverse dialogue
+
+## Memory Requirements
+
+| Model | GPU Memory | Recommended Batch Size |
+|-------|------------|------------------------|
+| Qwen2-VL-2B | ~8 GB | 16-32 |
+| Qwen2-VL-7B | ~16 GB | 8-16 |
+| Qwen2-VL-72B | ~80 GB | 1-2 |
+| Qwen3-VL-30B | ~40 GB | 4-8 |
+
+## Troubleshooting
+
+### "FFmpeg not found"
+```bash
+# Install FFmpeg
+sudo apt-get install ffmpeg  # Ubuntu/Debian
+brew install ffmpeg          # macOS
+```
+
+### "CUDA out of memory"
+- Reduce batch size: `--batch-size 1` or `--video-batch-size 1`
+- Use smaller model: `Qwen/Qwen2-VL-2B-Instruct`
+- Use CPU: `--device cpu` (much slower)
+
+### "No skills.json found"
+Run subtask annotation first before high-level annotation.
+
+### "Video key not found"
+List available keys:
+```python
+from lerobot.datasets.lerobot_dataset import LeRobotDataset
+dataset = LeRobotDataset(repo_id="your/dataset")
+print("Video keys:", dataset.meta.video_keys)
+print("Camera keys:", dataset.meta.camera_keys)
+```
+
+## Dataset Structure After Annotation
+
+```
+your_dataset_with_high_level_tasks/
+├── meta/
+│   ├── info.json                      # Original metadata
+│   ├── tasks.parquet                  # Original tasks (preserved)
+│   ├── subtasks.parquet              # NEW: Subtask names and indices
+│   ├── skills.json                    # NEW: Raw skill annotations with timestamps
+│   ├── tasks_high_level.parquet      # NEW: High-level tasks with dialogue
+│   └── syn_annotations.jsonl         # NEW: Debug annotations
+├── data/
+│   └── chunk-000/
+│       ├── observation.images.base.mp4
+│       ├── action.safetensors
+│       ├── subtask_index.safetensors # NEW: Subtask per frame
+│       └── task_index_high_level.safetensors # NEW: High-level task per frame
+└── videos/
+    └── ...
+```
+
+## Citation
+
+If you use these annotation tools in your research, please cite:
+
+```bibtex
+@article{lerobot2024,
+  title={LeRobot: State-of-the-art Machine Learning for Real-World Robotics},
+  author={LeRobot Contributors},
+  year={2024},
+  url={https://github.com/huggingface/lerobot}
+}
+```
+
+## Next Steps
+
+After annotation, you can:
+1. Train hierarchical policies using the subtask and high-level annotations
+2. Use the synthetic dialogue for instruction-following policy training
+3. Analyze skill distributions and dialogue patterns
+4. Share your annotated dataset on HuggingFace Hub with `--push-to-hub`
+
+For training examples, see the [training documentation](../training/).
+

docs/source/installation.mdx

@@ -1,15 +1,13 @@
 # Installation
 
-This guide uses conda (via miniforge) to manage environments. If you prefer another environment manager (e.g. `uv`, `venv`), ensure you have Python >=3.10 and ffmpeg installed with the `libsvtav1` encoder, then skip ahead to [Install LeRobot](#step-3-install-lerobot-).
-
-## Step 1: Install [`miniforge`](https://conda-forge.org/download/)
+## Install [`miniforge`](https://conda-forge.org/download/)
 
 ```bash
 wget "https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-$(uname)-$(uname -m).sh"
 bash Miniforge3-$(uname)-$(uname -m).sh
 ```
 
-## Step 2: Environment Setup
+## Environment Setup
 
 Create a virtual environment with Python 3.10, using conda:
 
@@ -40,7 +38,7 @@ conda install ffmpeg -c conda-forge
 >
 > - _[On Linux only]_ If you want to bring your own ffmpeg: Install [ffmpeg build dependencies](https://trac.ffmpeg.org/wiki/CompilationGuide/Ubuntu#GettheDependencies) and [compile ffmpeg from source with libsvtav1](https://trac.ffmpeg.org/wiki/CompilationGuide/Ubuntu#libsvtav1), and make sure you use the corresponding ffmpeg binary to your install with `which ffmpeg`.
 
-## Step 3: Install LeRobot 🤗
+## Install LeRobot 🤗
 
 ### From Source
 

pyproject.toml

@@ -360,9 +360,9 @@ ignore_errors = false
 module = "lerobot.cameras.*"
 ignore_errors = false
 
-[[tool.mypy.overrides]]
-module = "lerobot.motors.*"
-ignore_errors = false
+# [[tool.mypy.overrides]]
+# module = "lerobot.motors.*"
+# ignore_errors = false
 
 # [[tool.mypy.overrides]]
 # module = "lerobot.robots.*"

src/lerobot/cameras/__init__.py

@@ -13,5 +13,5 @@
 # limitations under the License.
 
 from .camera import Camera
-from .configs import CameraConfig, ColorMode, Cv2Backends, Cv2Rotation
+from .configs import CameraConfig, ColorMode, Cv2Rotation
 from .utils import make_cameras_from_configs

src/lerobot/cameras/configs.py

@@ -25,10 +25,6 @@ class ColorMode(str, Enum):
     RGB = "rgb"
     BGR = "bgr"
 
-    @classmethod
-    def _missing_(cls, value: object) -> None:
-        raise ValueError(f"`color_mode` is expected to be in {list(cls)}, but {value} is provided.")
-
 
 class Cv2Rotation(int, Enum):
     NO_ROTATION = 0
@@ -36,25 +32,6 @@ class Cv2Rotation(int, Enum):
     ROTATE_180 = 180
     ROTATE_270 = -90
 
-    @classmethod
-    def _missing_(cls, value: object) -> None:
-        raise ValueError(f"`rotation` is expected to be in {list(cls)}, but {value} is provided.")
-
-
-# Subset from https://docs.opencv.org/3.4/d4/d15/group__videoio__flags__base.html
-class Cv2Backends(int, Enum):
-    ANY = 0
-    V4L2 = 200
-    DSHOW = 700
-    PVAPI = 800
-    ANDROID = 1000
-    AVFOUNDATION = 1200
-    MSMF = 1400
-
-    @classmethod
-    def _missing_(cls, value: object) -> None:
-        raise ValueError(f"`backend` is expected to be in {list(cls)}, but {value} is provided.")
-
 
 @dataclass(kw_only=True)
 class CameraConfig(draccus.ChoiceRegistry, abc.ABC):  # type: ignore  # TODO: add type stubs for draccus

src/lerobot/cameras/opencv/camera_opencv.py

@@ -32,11 +32,10 @@ if platform.system() == "Windows" and "OPENCV_VIDEOIO_MSMF_ENABLE_HW_TRANSFORMS"
     os.environ["OPENCV_VIDEOIO_MSMF_ENABLE_HW_TRANSFORMS"] = "0"
 import cv2  # type: ignore  # TODO: add type stubs for OpenCV
 
-from lerobot.utils.decorators import check_if_already_connected, check_if_not_connected
-from lerobot.utils.errors import DeviceNotConnectedError
+from lerobot.utils.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError
 
 from ..camera import Camera
-from ..utils import get_cv2_rotation
+from ..utils import get_cv2_backend, get_cv2_rotation
 from .configuration_opencv import ColorMode, OpenCVCameraConfig
 
 # NOTE(Steven): The maximum opencv device index depends on your operating system. For instance,
@@ -118,7 +117,7 @@ class OpenCVCamera(Camera):
         self.new_frame_event: Event = Event()
 
         self.rotation: int | None = get_cv2_rotation(config.rotation)
-        self.backend: int = config.backend
+        self.backend: int = get_cv2_backend()
 
         if self.height and self.width:
             self.capture_width, self.capture_height = self.width, self.height
@@ -133,7 +132,6 @@ class OpenCVCamera(Camera):
         """Checks if the camera is currently connected and opened."""
         return isinstance(self.videocapture, cv2.VideoCapture) and self.videocapture.isOpened()
 
-    @check_if_already_connected
     def connect(self, warmup: bool = True) -> None:
         """
         Connects to the OpenCV camera specified in the configuration.
@@ -150,6 +148,8 @@ class OpenCVCamera(Camera):
             ConnectionError: If the specified camera index/path is not found or fails to open.
             RuntimeError: If the camera opens but fails to apply requested settings.
         """
+        if self.is_connected:
+            raise DeviceAlreadyConnectedError(f"{self} is already connected.")
 
         # Use 1 thread for OpenCV operations to avoid potential conflicts or
         # blocking in multi-threaded applications, especially during data collection.
@@ -178,7 +178,6 @@ class OpenCVCamera(Camera):
 
         logger.info(f"{self} connected.")
 
-    @check_if_not_connected
     def _configure_capture_settings(self) -> None:
         """
         Applies the specified FOURCC, FPS, width, and height settings to the connected camera.
@@ -198,6 +197,8 @@ class OpenCVCamera(Camera):
                           to the requested value.
             DeviceNotConnectedError: If the camera is not connected.
         """
+        if not self.is_connected:
+            raise DeviceNotConnectedError(f"Cannot configure settings for {self} as it is not connected.")
 
         # Set FOURCC first (if specified) as it can affect available FPS/resolution options
         if self.config.fourcc is not None:
@@ -347,7 +348,6 @@ class OpenCVCamera(Camera):
 
         return frame
 
-    @check_if_not_connected
     def read(self, color_mode: ColorMode | None = None) -> NDArray[Any]:
         """
         Reads a single frame synchronously from the camera.
@@ -374,6 +374,9 @@ class OpenCVCamera(Camera):
                 f"{self} read() color_mode parameter is deprecated and will be removed in future versions."
             )
 
+        if not self.is_connected:
+            raise DeviceNotConnectedError(f"{self} is not connected.")
+
         if self.thread is None or not self.thread.is_alive():
             raise RuntimeError(f"{self} read thread is not running.")
 
@@ -487,7 +490,6 @@ class OpenCVCamera(Camera):
             self.latest_timestamp = None
             self.new_frame_event.clear()
 
-    @check_if_not_connected
     def async_read(self, timeout_ms: float = 200) -> NDArray[Any]:
         """
         Reads the latest available frame asynchronously.
@@ -510,6 +512,8 @@ class OpenCVCamera(Camera):
             TimeoutError: If no frame becomes available within the specified timeout.
             RuntimeError: If an unexpected error occurs.
         """
+        if not self.is_connected:
+            raise DeviceNotConnectedError(f"{self} is not connected.")
 
         if self.thread is None or not self.thread.is_alive():
             raise RuntimeError(f"{self} read thread is not running.")
@@ -529,7 +533,6 @@ class OpenCVCamera(Camera):
 
         return frame
 
-    @check_if_not_connected
     def read_latest(self, max_age_ms: int = 1000) -> NDArray[Any]:
         """Return the most recent frame captured immediately (Peeking).
 
@@ -545,6 +548,8 @@ class OpenCVCamera(Camera):
             DeviceNotConnectedError: If the camera is not connected.
             RuntimeError: If the camera is connected but has not captured any frames yet.
         """
+        if not self.is_connected:
+            raise DeviceNotConnectedError(f"{self} is not connected.")
 
         if self.thread is None or not self.thread.is_alive():
             raise RuntimeError(f"{self} read thread is not running.")

src/lerobot/cameras/opencv/configuration_opencv.py

@@ -15,9 +15,9 @@
 from dataclasses import dataclass
 from pathlib import Path
 
-from ..configs import CameraConfig, ColorMode, Cv2Backends, Cv2Rotation
+from ..configs import CameraConfig, ColorMode, Cv2Rotation
 
-__all__ = ["OpenCVCameraConfig", "ColorMode", "Cv2Rotation", "Cv2Backends"]
+__all__ = ["OpenCVCameraConfig", "ColorMode", "Cv2Rotation"]
 
 
 @CameraConfig.register_subclass("opencv")
@@ -50,7 +50,6 @@ class OpenCVCameraConfig(CameraConfig):
         rotation: Image rotation setting (0°, 90°, 180°, or 270°). Defaults to no rotation.
         warmup_s: Time reading frames before returning from connect (in seconds)
         fourcc: FOURCC code for video format (e.g., "MJPG", "YUYV", "I420"). Defaults to None (auto-detect).
-        backend: OpenCV backend identifier (https://docs.opencv.org/3.4/d4/d15/group__videoio__flags__base.html). Defaults to ANY.
 
     Note:
         - Only 3-channel color output (RGB/BGR) is currently supported.
@@ -63,12 +62,22 @@ class OpenCVCameraConfig(CameraConfig):
     rotation: Cv2Rotation = Cv2Rotation.NO_ROTATION
     warmup_s: int = 1
     fourcc: str | None = None
-    backend: Cv2Backends = Cv2Backends.ANY
 
     def __post_init__(self) -> None:
-        self.color_mode = ColorMode(self.color_mode)
-        self.rotation = Cv2Rotation(self.rotation)
-        self.backend = Cv2Backends(self.backend)
+        if self.color_mode not in (ColorMode.RGB, ColorMode.BGR):
+            raise ValueError(
+                f"`color_mode` is expected to be {ColorMode.RGB.value} or {ColorMode.BGR.value}, but {self.color_mode} is provided."
+            )
+
+        if self.rotation not in (
+            Cv2Rotation.NO_ROTATION,
+            Cv2Rotation.ROTATE_90,
+            Cv2Rotation.ROTATE_180,
+            Cv2Rotation.ROTATE_270,
+        ):
+            raise ValueError(
+                f"`rotation` is expected to be in {(Cv2Rotation.NO_ROTATION, Cv2Rotation.ROTATE_90, Cv2Rotation.ROTATE_180, Cv2Rotation.ROTATE_270)}, but {self.rotation} is provided."
+            )
 
         if self.fourcc is not None and (not isinstance(self.fourcc, str) or len(self.fourcc) != 4):
             raise ValueError(

src/lerobot/cameras/reachy2_camera/configuration_reachy2_camera.py

@@ -74,4 +74,7 @@ class Reachy2CameraConfig(CameraConfig):
                 f"`image_type` is expected to be 'left' or 'right' for teleop camera, and 'rgb' or 'depth' for depth camera, but {self.image_type} is provided."
             )
 
-        self.color_mode = ColorMode(self.color_mode)
+        if self.color_mode not in ["rgb", "bgr"]:
+            raise ValueError(
+                f"`color_mode` is expected to be 'rgb' or 'bgr', but {self.color_mode} is provided."
+            )

src/lerobot/cameras/reachy2_camera/reachy2_camera.py

@@ -32,7 +32,6 @@ if platform.system() == "Windows" and "OPENCV_VIDEOIO_MSMF_ENABLE_HW_TRANSFORMS"
 import cv2  # type: ignore  # TODO: add type stubs for OpenCV
 import numpy as np  # type: ignore  # TODO: add type stubs for numpy
 
-from lerobot.utils.decorators import check_if_not_connected
 from lerobot.utils.import_utils import _reachy2_sdk_available
 
 if TYPE_CHECKING or _reachy2_sdk_available:
@@ -124,7 +123,6 @@ class Reachy2Camera(Camera):
         """
         raise NotImplementedError("Camera detection is not implemented for Reachy2 cameras.")
 
-    @check_if_not_connected
     def read(self, color_mode: ColorMode | None = None) -> NDArray[Any]:
         """
         Reads a single frame synchronously from the camera.
@@ -138,6 +136,9 @@ class Reachy2Camera(Camera):
         """
         start_time = time.perf_counter()
 
+        if not self.is_connected:
+            raise DeviceNotConnectedError(f"{self} is not connected.")
+
         if self.cam_manager is None:
             raise DeviceNotConnectedError(f"{self} is not connected.")
 
@@ -183,7 +184,6 @@ class Reachy2Camera(Camera):
 
         return frame
 
-    @check_if_not_connected
     def async_read(self, timeout_ms: float = 200) -> NDArray[Any]:
         """
         Same as read()
@@ -197,10 +197,11 @@ class Reachy2Camera(Camera):
             TimeoutError: If no frame becomes available within the specified timeout.
             RuntimeError: If an unexpected error occurs.
         """
+        if not self.is_connected:
+            raise DeviceNotConnectedError(f"{self} is not connected.")
 
         return self.read()
 
-    @check_if_not_connected
     def read_latest(self, max_age_ms: int = 1000) -> NDArray[Any]:
         """Return the most recent frame captured immediately (Peeking).
 
@@ -218,6 +219,8 @@ class Reachy2Camera(Camera):
             DeviceNotConnectedError: If the camera is not connected.
             RuntimeError: If the camera is connected but has not captured any frames yet.
         """
+        if not self.is_connected:
+            raise DeviceNotConnectedError(f"{self} is not connected.")
 
         if self.latest_frame is None or self.latest_timestamp is None:
             raise RuntimeError(f"{self} has not captured any frames yet.")
@@ -230,7 +233,6 @@ class Reachy2Camera(Camera):
 
         return self.latest_frame
 
-    @check_if_not_connected
     def disconnect(self) -> None:
         """
         Stops the background read thread (if running).
@@ -238,6 +240,8 @@ class Reachy2Camera(Camera):
         Raises:
             DeviceNotConnectedError: If the camera is already disconnected.
         """
+        if not self.is_connected:
+            raise DeviceNotConnectedError(f"{self} not connected.")
 
         if self.cam_manager is not None:
             self.cam_manager.disconnect()

src/lerobot/cameras/realsense/camera_realsense.py

@@ -30,8 +30,7 @@ try:
 except Exception as e:
     logging.info(f"Could not import realsense: {e}")
 
-from lerobot.utils.decorators import check_if_already_connected, check_if_not_connected
-from lerobot.utils.errors import DeviceNotConnectedError
+from lerobot.utils.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError
 
 from ..camera import Camera
 from ..configs import ColorMode
@@ -153,7 +152,6 @@ class RealSenseCamera(Camera):
         """Checks if the camera pipeline is started and streams are active."""
         return self.rs_pipeline is not None and self.rs_profile is not None
 
-    @check_if_already_connected
     def connect(self, warmup: bool = True) -> None:
         """
         Connects to the RealSense camera specified in the configuration.
@@ -171,6 +169,8 @@ class RealSenseCamera(Camera):
             ConnectionError: If the camera is found but fails to start the pipeline or no RealSense devices are detected at all.
             RuntimeError: If the pipeline starts but fails to apply requested settings.
         """
+        if self.is_connected:
+            raise DeviceAlreadyConnectedError(f"{self} is already connected.")
 
         self.rs_pipeline = rs.pipeline()
         rs_config = rs.config()
@@ -290,7 +290,6 @@ class RealSenseCamera(Camera):
             if self.use_depth:
                 rs_config.enable_stream(rs.stream.depth)
 
-    @check_if_not_connected
     def _configure_capture_settings(self) -> None:
         """Sets fps, width, and height from device stream if not already configured.
 
@@ -300,6 +299,8 @@ class RealSenseCamera(Camera):
         Raises:
             DeviceNotConnectedError: If device is not connected.
         """
+        if not self.is_connected:
+            raise DeviceNotConnectedError(f"Cannot validate settings for {self} as it is not connected.")
 
         if self.rs_profile is None:
             raise RuntimeError(f"{self}: rs_profile must be initialized before use.")
@@ -319,7 +320,6 @@ class RealSenseCamera(Camera):
                 self.width, self.height = actual_width, actual_height
                 self.capture_width, self.capture_height = actual_width, actual_height
 
-    @check_if_not_connected
     def read_depth(self, timeout_ms: int = 200) -> NDArray[Any]:
         """
         Reads a single frame (depth) synchronously from the camera.
@@ -345,6 +345,9 @@ class RealSenseCamera(Camera):
                 f"Failed to capture depth frame '.read_depth()'. Depth stream is not enabled for {self}."
             )
 
+        if not self.is_connected:
+            raise DeviceNotConnectedError(f"{self} is not connected.")
+
         if self.thread is None or not self.thread.is_alive():
             raise RuntimeError(f"{self} read thread is not running.")
 
@@ -371,7 +374,6 @@ class RealSenseCamera(Camera):
 
         return frame
 
-    @check_if_not_connected
     def read(self, color_mode: ColorMode | None = None, timeout_ms: int = 0) -> NDArray[Any]:
         """
         Reads a single frame (color) synchronously from the camera.
@@ -401,6 +403,9 @@ class RealSenseCamera(Camera):
                 f"{self} read() timeout_ms parameter is deprecated and will be removed in future versions."
             )
 
+        if not self.is_connected:
+            raise DeviceNotConnectedError(f"{self} is not connected.")
+
         if self.thread is None or not self.thread.is_alive():
             raise RuntimeError(f"{self} read thread is not running.")
 
@@ -529,7 +534,6 @@ class RealSenseCamera(Camera):
             self.new_frame_event.clear()
 
     # NOTE(Steven): Missing implementation for depth for now
-    @check_if_not_connected
     def async_read(self, timeout_ms: float = 200) -> NDArray[Any]:
         """
         Reads the latest available frame data (color) asynchronously.
@@ -552,6 +556,8 @@ class RealSenseCamera(Camera):
             TimeoutError: If no frame data becomes available within the specified timeout.
             RuntimeError: If the background thread died unexpectedly or another error occurs.
         """
+        if not self.is_connected:
+            raise DeviceNotConnectedError(f"{self} is not connected.")
 
         if self.thread is None or not self.thread.is_alive():
             raise RuntimeError(f"{self} read thread is not running.")
@@ -572,7 +578,6 @@ class RealSenseCamera(Camera):
         return frame
 
     # NOTE(Steven): Missing implementation for depth for now
-    @check_if_not_connected
     def read_latest(self, max_age_ms: int = 1000) -> NDArray[Any]:
         """Return the most recent (color) frame captured immediately (Peeking).
 
@@ -588,6 +593,8 @@ class RealSenseCamera(Camera):
             DeviceNotConnectedError: If the camera is not connected.
             RuntimeError: If the camera is connected but has not captured any frames yet.
         """
+        if not self.is_connected:
+            raise DeviceNotConnectedError(f"{self} is not connected.")
 
         if self.thread is None or not self.thread.is_alive():
             raise RuntimeError(f"{self} read thread is not running.")

src/lerobot/cameras/realsense/configuration_realsense.py

@@ -60,8 +60,20 @@ class RealSenseCameraConfig(CameraConfig):
     warmup_s: int = 1
 
     def __post_init__(self) -> None:
-        self.color_mode = ColorMode(self.color_mode)
-        self.rotation = Cv2Rotation(self.rotation)
+        if self.color_mode not in (ColorMode.RGB, ColorMode.BGR):
+            raise ValueError(
+                f"`color_mode` is expected to be {ColorMode.RGB.value} or {ColorMode.BGR.value}, but {self.color_mode} is provided."
+            )
+
+        if self.rotation not in (
+            Cv2Rotation.NO_ROTATION,
+            Cv2Rotation.ROTATE_90,
+            Cv2Rotation.ROTATE_180,
+            Cv2Rotation.ROTATE_270,
+        ):
+            raise ValueError(
+                f"`rotation` is expected to be in {(Cv2Rotation.NO_ROTATION, Cv2Rotation.ROTATE_90, Cv2Rotation.ROTATE_180, Cv2Rotation.ROTATE_270)}, but {self.rotation} is provided."
+            )
 
         values = (self.fps, self.width, self.height)
         if any(v is not None for v in values) and any(v is None for v in values):

src/lerobot/cameras/utils.py

@@ -14,6 +14,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import platform
 from typing import cast
 
 from lerobot.utils.import_utils import make_device_from_device_class
@@ -67,3 +68,14 @@ def get_cv2_rotation(rotation: Cv2Rotation) -> int | None:
         return int(cv2.ROTATE_90_COUNTERCLOCKWISE)
     else:
         return None
+
+
+def get_cv2_backend() -> int:
+    import cv2
+
+    if platform.system() == "Windows":
+        return int(cv2.CAP_MSMF)  # Use MSMF for Windows instead of AVFOUNDATION
+    # elif platform.system() == "Darwin":  # macOS
+    #     return cv2.CAP_AVFOUNDATION
+    else:  # Linux and others
+        return int(cv2.CAP_ANY)

src/lerobot/cameras/zmq/camera_zmq.py

@@ -34,8 +34,7 @@ import cv2
 import numpy as np
 from numpy.typing import NDArray
 
-from lerobot.utils.decorators import check_if_already_connected, check_if_not_connected
-from lerobot.utils.errors import DeviceNotConnectedError
+from lerobot.utils.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError
 
 from ..camera import Camera
 from ..configs import ColorMode
@@ -105,7 +104,6 @@ class ZMQCamera(Camera):
         """Checks if the ZMQ socket is initialized and connected."""
         return self._connected and self.context is not None and self.socket is not None
 
-    @check_if_already_connected
     def connect(self, warmup: bool = True) -> None:
         """Connect to ZMQ camera server.
 
@@ -113,6 +111,8 @@ class ZMQCamera(Camera):
             warmup (bool): If True, waits for the camera to provide at least one
                            valid frame before returning. Defaults to True.
         """
+        if self.is_connected:
+            raise DeviceAlreadyConnectedError(f"{self} is already connected.")
 
         logger.info(f"Connecting to {self}...")
 
@@ -211,7 +211,6 @@ class ZMQCamera(Camera):
 
         return frame
 
-    @check_if_not_connected
     def read(self, color_mode: ColorMode | None = None) -> NDArray[Any]:
         """
         Reads a single frame synchronously from the camera.
@@ -229,6 +228,9 @@ class ZMQCamera(Camera):
                 f"{self} read() color_mode parameter is deprecated and will be removed in future versions."
             )
 
+        if not self.is_connected:
+            raise DeviceNotConnectedError(f"{self} is not connected.")
+
         if self.thread is None or not self.thread.is_alive():
             raise RuntimeError(f"{self} read thread is not running.")
 
@@ -299,7 +301,6 @@ class ZMQCamera(Camera):
             self.latest_timestamp = None
             self.new_frame_event.clear()
 
-    @check_if_not_connected
     def async_read(self, timeout_ms: float = 200) -> NDArray[Any]:
         """
         Reads the latest available frame asynchronously.
@@ -316,6 +317,8 @@ class ZMQCamera(Camera):
             TimeoutError: If no frame data becomes available within the specified timeout.
             RuntimeError: If the background thread is not running.
         """
+        if not self.is_connected:
+            raise DeviceNotConnectedError(f"{self} is not connected.")
 
         if self.thread is None or not self.thread.is_alive():
             raise RuntimeError(f"{self} read thread is not running.")
@@ -332,7 +335,6 @@ class ZMQCamera(Camera):
 
         return frame
 
-    @check_if_not_connected
     def read_latest(self, max_age_ms: int = 1000) -> NDArray[Any]:
         """Return the most recent frame captured immediately (Peeking).
 
@@ -348,6 +350,8 @@ class ZMQCamera(Camera):
             DeviceNotConnectedError: If the camera is not connected.
             RuntimeError: If the camera is connected but has not captured any frames yet.
         """
+        if not self.is_connected:
+            raise DeviceNotConnectedError(f"{self} is not connected.")
 
         if self.thread is None or not self.thread.is_alive():
             raise RuntimeError(f"{self} read thread is not running.")

src/lerobot/cameras/zmq/configuration_zmq.py

@@ -32,7 +32,10 @@ class ZMQCameraConfig(CameraConfig):
     warmup_s: int = 1
 
     def __post_init__(self) -> None:
-        self.color_mode = ColorMode(self.color_mode)
+        if self.color_mode not in (ColorMode.RGB, ColorMode.BGR):
+            raise ValueError(
+                f"`color_mode` is expected to be {ColorMode.RGB.value} or {ColorMode.BGR.value}, but {self.color_mode} is provided."
+            )
 
         if self.timeout_ms <= 0:
             raise ValueError(f"`timeout_ms` must be positive, but {self.timeout_ms} is provided.")

src/lerobot/data_processing/annotations/annotate_libero.sh

@@ -0,0 +1,50 @@
+#!/bin/bash
+
+# Example script to run synthetic data generation with Qwen VLM
+# This generates user prompts and robot utterances for hierarchical policy training
+
+# Configuration
+REPO_ID="lerobot/libero_10"
+MODEL="Qwen/Qwen3-VL-30B-A3B-Instruct"
+# or: MODEL="Qwen/Qwen2-VL-7B-Instruct"
+
+
+OUTPUT_DIR="/fsx/jade_choghari/outputs/libero-10-annotate-high"
+
+BATCH_SIZE=16
+TEMPERATURE=0.9
+SAMPLE_INTERVAL=5.0  # generate dialogue every 1 second (all episodes processed)
+
+# Run subtask annotation
+# python /admin/home/jade_choghari/lerobot/src/lerobot/policies/pi05_full/annotate/subtask_annotate.py \
+#     --repo-id "$REPO_ID" \
+#     --video-key observation.images.image \
+#     --output-dir "$OUTPUT_DIR" \
+#     --skip-existing \
+#     --output-repo-id "jadechoghari/libero10-annotate" \
+#     --batch-size "$BATCH_SIZE" \
+# run synthetic data generation (all episodes processed)
+# python examples/dataset/annotate_pgen.py \
+#     --repo-id "$REPO_ID" \
+#     --model "$MODEL" \
+#     --output-dir "$OUTPUT_DIR" \
+#     --temperature "$TEMPERATURE" \
+#     --batch-size "$BATCH_SIZE" \
+#     --sample-interval "$SAMPLE_INTERVAL" \
+#     --image-key observation.images.base \
+#     --num-image-views-per-sample 1
+
+# for faster testing, increase sample interval:
+# --sample-interval 5.0  # Samples every 5 seconds (much faster)
+
+# to push to hub after generation:
+# add --push-to-hub flag
+
+# efficient batch processing: 4 episodes at once
+python src/lerobot/data_processing/annotations/high_level_annotate.py \
+    --data-dir "/fsx/jade_choghari/outputs/libero-10-annotate" \
+    --output-dir "$OUTPUT_DIR" \
+    --video-mode \
+    --video-key observation.images.image \
+    --video-batch-size "$BATCH_SIZE" \
+    --sample-interval 5.0

src/lerobot/data_processing/annotations/load_lerobot_high.py

@@ -0,0 +1,52 @@
+import torch
+from huggingface_hub import HfApi
+
+import lerobot
+from lerobot.datasets.lerobot_dataset import LeRobotDataset, LeRobotDatasetMetadata
+from lerobot.policies.factory import make_pre_post_processors
+from lerobot.configs.policies import PreTrainedConfig
+
+# /fsx/jade_choghari/data/libero_10_subtasks_kw_converted
+dataset = LeRobotDataset(repo_id="lerobot/libero_10_image_subtask")
+
+dataloader = torch.utils.data.DataLoader(
+        dataset,
+        num_workers=0,
+        batch_size=2,
+        shuffle=True,
+)
+
+cfg = PreTrainedConfig.from_pretrained(
+    pretrained_name_or_path="/fsx/jade_choghari/models/pi05-base",
+)
+cfg.dtype = "bfloat16"
+
+pre_processor, post_processor = make_pre_post_processors(
+    policy_cfg=cfg,
+    pretrained_path="/fsx/jade_choghari/models/pi05-base",
+)
+batch = next(iter(dataloader))
+breakpoint()
+batch1 = pre_processor(batch)
+breakpoint()
+print(batch.keys())
+# print(batch['task_index_high_level'].shape)
+# print(batch['task_index_high_level'])
+# print(batch['user_prompt'][0])
+# print(batch['robot_utterance'][0])
+# print(batch['task'][0])
+
+valid_episode_list = []
+for episode_idx in range(len(dataset.meta.episodes)):
+        subtask_index = dataset[episode_idx]["subtask_index"]
+        valid_episode_list.append(episode_idx)
+
+print(len(valid_episode_list))
+
+# read this parquet /fsx/jade_choghari/outputs/pgen_annotations1/meta/tasks.parquett
+# import pandas as pd
+# tasks_df = pd.read_parquet('/fsx/jade_choghari/outputs/pgen_annotations1/meta/tasks.parquet')
+
+# # print all
+# print(tasks_df.columns)
+# breakpoint()

src/lerobot/data_processing/annotations/run_pgen.sh

@@ -0,0 +1,74 @@
+#!/bin/bash
+
+# Example script to run synthetic data generation with Qwen VLM
+# This generates user prompts and robot utterances for hierarchical policy training
+
+# Configuration
+REPO_ID="jadechoghari/piper-demo-20260205_103303"
+# MODEL="Qwen/Qwen3-VL-30B-A3B-Thinking"
+MODEL="Qwen/Qwen3.5-27B"
+# or: MODEL="Qwen/Qwen2-VL-7B-Instruct"
+
+
+OUTPUT_DIR="/fsx/jade_choghari/outputs/collect-data-pgen_new"
+
+BATCH_SIZE=2
+TEMPERATURE=0.9
+SAMPLE_INTERVAL=5.0  # generate dialogue every 1 second (all episodes processed)
+
+# Run subtask annotation.
+# To use closed-vocabulary labels, add a line: --subtask-labels "label1" "label2" ...
+# Example (add backslash after "$MODEL" and uncomment the next line):
+#   --model "$MODEL" \
+#   --subtask-labels "pick_up_yellow_nut_bar" "pick_up_cake" "pick_up_biscuit_pack" "pick_up_soda_can"
+python /home/lerobot/src/lerobot/data_processing/annotations/subtask_annotate.py \
+    --repo-id "$REPO_ID" \
+    --video-key observation.images.top \
+    --output-dir "$OUTPUT_DIR" \
+    --output-repo-id "jadechoghari/piper-demo-annotated1" \
+    --push-to-hub \
+    --no-timer-overlay \
+    --model "$MODEL" \
+    --subtask-labels "pick_up_yellow_nut_bar" "pick_up_cake" "pick_up_biscuit_pack" "pick_up_soda_can" \
+    --batch-size 2
+
+# Run subtask annotation (image-window: frames as images for better accuracy)
+# python /admin/home/jade_choghari/lerobot/src/lerobot/data_processing/annotations/subtask_annotate_image.py \
+#     --repo-id "$REPO_ID" \
+#     --camera-key observation.images.wrist \
+#     --output-dir "$OUTPUT_DIR" \
+#     --output-repo-id "jadechoghari/piper-demo-annotated1-image" \
+#     --push-to-hub \
+#     --model "$MODEL" \
+#     --window-size 184 \
+#     --max-frames-per-window 16 \
+#     --subtask-labels "pick_up_yellow_nut_bar" "pick_up_cake" "pick_up_biscuit_pack" "pick_up_soda_can" \
+#     --batch-size 2
+
+    
+# run synthetic data generation (all episodes processed)
+# python examples/dataset/annotate_pgen.py \
+#     --repo-id "$REPO_ID" \
+#     --model "$MODEL" \
+#     --output-dir "$OUTPUT_DIR" \
+#     --temperature "$TEMPERATURE" \
+#     --batch-size "$BATCH_SIZE" \
+#     --sample-interval "$SAMPLE_INTERVAL" \
+#     --image-key observation.images.base \
+#     --num-image-views-per-sample 1
+
+# for faster testing, increase sample interval:
+# --sample-interval 5.0  # Samples every 5 seconds (much faster)
+
+# to push to hub after generation:
+# add --push-to-hub flag
+
+# efficient batch processing: 4 episodes at once
+# python examples/dataset/annotate_pgen.py \
+#     --repo-id "$REPO_ID" \
+#     --model "$MODEL" \
+#     --output-dir "$OUTPUT_DIR" \
+#     --video-mode \
+#     --video-key observation.images.up \
+#     --video-batch-size "$BATCH_SIZE" \
+#     --sample-interval 1.0

src/lerobot/data_processing/annotations/subtask_annotate_image.py

@@ -0,0 +1,561 @@
+#!/usr/bin/env python
+
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+Image-window subtask annotation for LeRobot datasets using Qwen VLMs.
+
+This script assigns a subtask to each window of consecutive frames by sending
+those frames as images to the VLM (instead of a video) for better accuracy.
+Supports Qwen2-VL and Qwen3-VL (same models as subtask_annotate.py).
+
+Pipeline:
+1. Load a LeRobot dataset (local or Hub).
+2. For each episode, slide a window over frame indices.
+3. For each window, load the corresponding images (from image_key or decoded video_key).
+4. Send the window of images to Qwen2-VL with the same skill prompt; get one subtask name.
+5. Assign that subtask to all frames in the window.
+6. Write subtasks.parquet and add subtask_index via add_features (same as subtask_annotate).
+
+Usage:
+  python -m lerobot.data_processing.annotations.subtask_annotate_image \\
+    --data-dir /path/to/dataset --camera-key observation.images.base \\
+    --window-size 8 --stride 8 --output-dir ./output
+"""
+
+from __future__ import annotations
+
+import argparse
+import random
+import textwrap
+from pathlib import Path
+
+import numpy as np
+import PIL.Image
+import torch
+from rich.console import Console
+
+from lerobot.datasets.lerobot_dataset import LeRobotDataset
+
+# Reuse data structures and save/load from the video-based annotator
+from lerobot.data_processing.annotations.subtask_annotate import (
+    EpisodeSkills,
+    Skill,
+    load_skill_annotations,
+    save_skill_annotations,
+)
+
+
+def create_window_skill_prompt(
+    coarse_goal: str | None = None,
+    subtask_labels: list[str] | None = None,
+) -> str:
+    """Prompt for labeling a single window of frames with one atomic skill.
+    If subtask_labels are provided, the model must choose exactly one from that list.
+    """
+    goal_context = f'The overall goal is: "{coarse_goal}".\n\n' if coarse_goal else ""
+    if subtask_labels:
+        labels_list = ", ".join(f'"{l}"' for l in subtask_labels)
+        label_instruction = (
+            f"You must choose exactly ONE skill from this list: [{labels_list}]. "
+            "Do not create new labels. Reply with only that label.\n\n"
+        )
+    else:
+        label_instruction = ""
+    return textwrap.dedent(f"""\
+        # Role
+        You are a Robotics Vision System that labels short clips from robot manipulation demonstrations.
+
+        # Task
+        {goal_context}{label_instruction}The following images are consecutive frames from a single short clip of a robot demonstration.
+        What single atomic manipulation skill is being performed in this clip?
+
+        # Requirements
+        - Reply with ONLY one short skill name (e.g. "pick up object", "move arm left", "release gripper").
+        - No explanation, no timestamps, no JSON. Just the skill name.
+        """).strip()
+
+
+def _run_image_segmenter(
+    self,
+    images: list[PIL.Image.Image],
+    coarse_goal: str | None,
+    subtask_labels: list[str] | None = None,
+) -> str:
+    """Shared inference for Qwen2-VL and Qwen3-VL image window labeling."""
+    prompt = create_window_skill_prompt(coarse_goal, subtask_labels)
+    content = []
+    for img in images:
+        content.append({"type": "image", "image": img})
+    content.append({"type": "text", "text": "What single atomic skill is shown in these frames? Reply with only the skill name."})
+
+    messages = [
+        {"role": "system", "content": [{"type": "text", "text": prompt}]},
+        {"role": "user", "content": content},
+    ]
+    text = self.processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+    image_inputs, video_inputs = self.process_vision_info(messages)
+    inputs = self.processor(
+        text=[text],
+        images=image_inputs,
+        videos=video_inputs,
+        padding=True,
+        return_tensors="pt",
+    ).to(self.device)
+
+    with torch.no_grad():
+        generated_ids = self.model.generate(**inputs, max_new_tokens=128, do_sample=False)
+
+    response = self.processor.batch_decode(
+        [out[len(inp) :] for inp, out in zip(inputs.input_ids, generated_ids)],
+        skip_special_tokens=True,
+    )[0].strip()
+    skill_name = response.split("\n")[0].strip().strip('."')
+    return skill_name if skill_name else "unknown"
+
+
+def _run_image_segmenter_batch(
+    self,
+    batch_images: list[list[PIL.Image.Image]],
+    coarse_goal: str | None,
+    subtask_labels: list[str] | None = None,
+) -> list[str]:
+    """Run VLM on multiple windows at once; returns one skill name per window."""
+    if not batch_images:
+        return []
+    prompt = create_window_skill_prompt(coarse_goal, subtask_labels)
+    all_texts = []
+    all_image_inputs = []
+    all_video_inputs = []
+    for images in batch_images:
+        content = []
+        for img in images:
+            content.append({"type": "image", "image": img})
+        content.append({"type": "text", "text": "What single atomic skill is shown in these frames? Reply with only the skill name."})
+        messages = [
+            {"role": "system", "content": [{"type": "text", "text": prompt}]},
+            {"role": "user", "content": content},
+        ]
+        text = self.processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+        image_inputs, video_inputs = self.process_vision_info(messages)
+        all_texts.append(text)
+        if image_inputs is not None:
+            all_image_inputs.extend(image_inputs if isinstance(image_inputs, list) else [image_inputs])
+        if video_inputs is not None:
+            all_video_inputs.extend(video_inputs if isinstance(video_inputs, list) else [video_inputs])
+    inputs = self.processor(
+        text=all_texts,
+        images=all_image_inputs if all_image_inputs else None,
+        videos=all_video_inputs if all_video_inputs else None,
+        padding=True,
+        return_tensors="pt",
+    ).to(self.device)
+    with torch.no_grad():
+        generated_ids = self.model.generate(**inputs, max_new_tokens=128, do_sample=False)
+    responses = self.processor.batch_decode(
+        [out[len(inp) :] for inp, out in zip(inputs.input_ids, generated_ids)],
+        skip_special_tokens=True,
+    )
+    return [
+        (r.split("\n")[0].strip().strip('."') or "unknown")
+        for r in responses
+    ]
+
+
+class Qwen2VLImageSegmenter:
+    """Uses Qwen2-VL to assign one skill name to a window of images (same model as subtask_annotate)."""
+
+    def __init__(self, model_name: str, device: str = "cuda", torch_dtype: torch.dtype = torch.bfloat16):
+        from qwen_vl_utils import process_vision_info
+        from transformers import AutoProcessor, Qwen2VLForConditionalGeneration
+
+        self.console = Console()
+        self.device = device
+        self.process_vision_info = process_vision_info
+        self.console.print(f"[cyan]Loading Qwen2-VL for image-window labeling: {model_name}...[/cyan]")
+        self.model = Qwen2VLForConditionalGeneration.from_pretrained(
+            model_name, torch_dtype=torch_dtype, device_map=device, trust_remote_code=True
+        )
+        self.processor = AutoProcessor.from_pretrained(model_name, trust_remote_code=True)
+        self.console.print(f"[green]✓ Model loaded on {device}[/green]")
+
+    def segment_skill_from_images(
+        self,
+        images: list[PIL.Image.Image],
+        coarse_goal: str | None = None,
+        subtask_labels: list[str] | None = None,
+    ) -> str:
+        """Return a single skill name for the given window of images."""
+        return _run_image_segmenter(self, images, coarse_goal, subtask_labels)
+
+    def segment_skill_from_images_batch(
+        self,
+        batch_images: list[list[PIL.Image.Image]],
+        coarse_goal: str | None = None,
+        subtask_labels: list[str] | None = None,
+    ) -> list[str]:
+        """Return one skill name per window; processes multiple windows in one forward pass."""
+        return _run_image_segmenter_batch(self, batch_images, coarse_goal, subtask_labels)
+
+
+class Qwen3VLImageSegmenter:
+    """Uses Qwen3-VL (MoE) to assign one skill name to a window of images."""
+
+    def __init__(self, model_name: str, device: str = "cuda", torch_dtype: torch.dtype = torch.bfloat16):
+        from qwen_vl_utils import process_vision_info
+        from transformers import AutoProcessor, Qwen3VLMoeForConditionalGeneration
+
+        self.console = Console()
+        self.device = device
+        self.process_vision_info = process_vision_info
+        self.console.print(f"[cyan]Loading Qwen3-VL for image-window labeling: {model_name}...[/cyan]")
+        self.model = Qwen3VLMoeForConditionalGeneration.from_pretrained(
+            model_name, torch_dtype=torch_dtype, device_map=device, trust_remote_code=True
+        )
+        self.processor = AutoProcessor.from_pretrained(model_name, trust_remote_code=True)
+        self.console.print(f"[green]✓ Model loaded on {device}[/green]")
+
+    def segment_skill_from_images(
+        self,
+        images: list[PIL.Image.Image],
+        coarse_goal: str | None = None,
+        subtask_labels: list[str] | None = None,
+    ) -> str:
+        """Return a single skill name for the given window of images."""
+        return _run_image_segmenter(self, images, coarse_goal, subtask_labels)
+
+    def segment_skill_from_images_batch(
+        self,
+        batch_images: list[list[PIL.Image.Image]],
+        coarse_goal: str | None = None,
+        subtask_labels: list[str] | None = None,
+    ) -> list[str]:
+        """Return one skill name per window; processes multiple windows in one forward pass."""
+        return _run_image_segmenter_batch(self, batch_images, coarse_goal, subtask_labels)
+
+
+def get_image_segmenter(
+    model_name: str,
+    device: str = "cuda",
+    torch_dtype: torch.dtype = torch.bfloat16,
+):
+    """Return the appropriate image-window segmenter for the model (Qwen2-VL or Qwen3-VL)."""
+    model_lower = model_name.lower()
+    if "qwen3" in model_lower:
+        return Qwen3VLImageSegmenter(model_name, device, torch_dtype)
+    return Qwen2VLImageSegmenter(model_name, device, torch_dtype)
+
+
+def frame_to_pil(frame_value) -> PIL.Image.Image:
+    """Convert a single frame from dataset (tensor or PIL or path) to PIL.Image."""
+    if isinstance(frame_value, PIL.Image.Image):
+        return frame_value
+    if isinstance(frame_value, (str, Path)):
+        return PIL.Image.open(frame_value).convert("RGB")
+    if hasattr(frame_value, "numpy"):
+        arr = frame_value.numpy()
+    else:
+        arr = np.asarray(frame_value)
+    if arr.ndim == 3 and arr.shape[0] in (1, 3, 4):
+        arr = np.transpose(arr, (1, 2, 0))
+    if arr.dtype == np.float32 or arr.dtype == np.float64:
+        arr = (np.clip(arr, 0, 1) * 255).astype(np.uint8)
+    elif arr.dtype != np.uint8:
+        arr = np.clip(arr, 0, 255).astype(np.uint8)
+    if arr.shape[-1] == 1:
+        arr = np.repeat(arr, 3, axis=-1)
+    return PIL.Image.fromarray(arr)
+
+
+def _sample_window_indices(window_length: int, max_frames: int) -> list[int]:
+    """Return indices into a window of length window_length, at most max_frames, in order.
+    If window_length <= max_frames, returns range(window_length).
+    Otherwise returns sorted random sample of max_frames indices (temporal order preserved).
+    """
+    if max_frames <= 0 or window_length <= max_frames:
+        return list(range(window_length))
+    return sorted(random.sample(range(window_length), max_frames))
+
+
+class SkillAnnotatorImage:
+    """Annotates episodes by sliding a window over frames and labeling each window with the VLM."""
+
+    def __init__(
+        self,
+        segmenter: Qwen2VLImageSegmenter | Qwen3VLImageSegmenter,
+        window_size: int = 8,
+        stride: int | None = None,
+        batch_size: int = 1,
+        max_frames_per_window: int | None = None,
+        console: Console | None = None,
+    ):
+        self.segmenter = segmenter
+        self.window_size = window_size
+        self.stride = stride if stride is not None else window_size
+        self.batch_size = max(1, batch_size)
+        self.max_frames_per_window = max_frames_per_window
+        self.console = console or Console()
+
+    def annotate_dataset(
+        self,
+        dataset: LeRobotDataset,
+        camera_key: str,
+        episodes: list[int] | None = None,
+        skip_existing: bool = False,
+        subtask_labels: list[str] | None = None,
+    ) -> dict[int, EpisodeSkills]:
+        """Annotate episodes using image windows. camera_key can be an image_key or video_key."""
+        episode_indices = episodes or list(range(dataset.meta.total_episodes))
+        coarse_goal = self._get_coarse_goal(dataset)
+        annotations: dict[int, EpisodeSkills] = {}
+
+        if skip_existing:
+            existing = load_skill_annotations(dataset.root)
+            if existing and existing.get("episodes"):
+                existing_eps = {int(k) for k in existing["episodes"] if existing["episodes"][k].get("skills")}
+                episode_indices = [i for i in episode_indices if i not in existing_eps]
+
+        for ep_idx in episode_indices:
+            try:
+                skills = self._annotate_episode(
+                    dataset, ep_idx, camera_key, coarse_goal, subtask_labels
+                )
+                if skills:
+                    annotations[ep_idx] = EpisodeSkills(
+                        episode_index=ep_idx,
+                        description=coarse_goal,
+                        skills=skills,
+                    )
+                    self.console.print(f"[green]✓ Episode {ep_idx}: {len(skills)} window skills[/green]")
+                else:
+                    self.console.print(f"[yellow]⚠ Episode {ep_idx}: no skills[/yellow]")
+            except Exception as e:
+                self.console.print(f"[red]Episode {ep_idx} failed: {e}[/red]")
+
+        return annotations
+
+    def _get_coarse_goal(self, dataset: LeRobotDataset) -> str:
+        if dataset.meta.tasks is not None and len(dataset.meta.tasks) > 0:
+            return str(dataset.meta.tasks.index[0])
+        return "Perform the demonstrated manipulation task."
+
+    def _annotate_episode(
+        self,
+        dataset: LeRobotDataset,
+        episode_index: int,
+        camera_key: str,
+        coarse_goal: str,
+        subtask_labels: list[str] | None = None,
+    ) -> list[Skill]:
+        ep = dataset.meta.episodes[episode_index]
+        ep_from = int(ep["dataset_from_index"])
+        ep_to = int(ep["dataset_to_index"])
+        length = ep_to - ep_from
+        fps = dataset.meta.fps
+        if length == 0:
+            return []
+
+        # Collect full windows: (images, t_start, t_end) using frame timestamps.
+        # If max_frames_per_window is set and window is larger, sample that many frames (order preserved).
+        window_specs: list[tuple[list[PIL.Image.Image], float, float]] = []
+        start = 0
+        while start + self.window_size <= length:
+            offsets = _sample_window_indices(
+                self.window_size,
+                self.max_frames_per_window or self.window_size,
+            )
+            frame_indices = [ep_from + start + i for i in offsets]
+            images = []
+            t_start = float(dataset[frame_indices[0]]["timestamp"].item())
+            for idx in frame_indices:
+                item = dataset[idx]
+                images.append(frame_to_pil(item[camera_key]))
+            t_end = t_start + self.window_size / fps
+            window_specs.append((images, t_start, t_end))
+            start += self.stride
+
+        # Last partial window
+        if start < length:
+            partial_len = ep_to - (ep_from + start)
+            offsets = _sample_window_indices(
+                partial_len,
+                self.max_frames_per_window or partial_len,
+            )
+            frame_indices = [ep_from + start + i for i in offsets]
+            images = []
+            t_start = float(dataset[frame_indices[0]]["timestamp"].item())
+            for idx in frame_indices:
+                item = dataset[idx]
+                images.append(frame_to_pil(item[camera_key]))
+            t_end = float(dataset[frame_indices[-1]]["timestamp"].item()) + 1.0 / fps
+            window_specs.append((images, t_start, t_end))
+
+        # Run in batches
+        skills: list[Skill] = []
+        for i in range(0, len(window_specs), self.batch_size):
+            chunk = window_specs[i : i + self.batch_size]
+            batch_images = [spec[0] for spec in chunk]
+            if len(batch_images) > 1:
+                skill_names = self.segmenter.segment_skill_from_images_batch(
+                    batch_images, coarse_goal, subtask_labels
+                )
+            else:
+                skill_names = [
+                    self.segmenter.segment_skill_from_images(
+                        batch_images[0], coarse_goal, subtask_labels
+                    )
+                ]
+            for (_, t_start, t_end), name in zip(chunk, skill_names, strict=True):
+                skills.append(Skill(name=name, start=t_start, end=t_end))
+
+        return skills
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="Image-window subtask annotation using Qwen VLM (frames as images for better accuracy)",
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+        epilog=textwrap.dedent("""\
+            Examples:
+              python -m lerobot.data_processing.annotations.subtask_annotate_image \\
+                --data-dir /path/to/dataset --camera-key observation.images.base \\
+                --window-size 8 --output-dir ./output
+
+              python -m lerobot.data_processing.annotations.subtask_annotate_image \\
+                --repo-id user/dataset --camera-key observation.images.base \\
+                --window-size 6 --stride 3 --model Qwen/Qwen2-VL-7B-Instruct
+
+              # Use Qwen3-VL (MoE)
+              python -m lerobot.data_processing.annotations.subtask_annotate_image \\
+                --data-dir /path/to/dataset --camera-key observation.images.base \\
+                --model Qwen/Qwen3-VL-30B-A3B-Instruct
+        """),
+    )
+    data_group = parser.add_mutually_exclusive_group(required=True)
+    data_group.add_argument("--data-dir", type=str, help="Path to local LeRobot dataset")
+    data_group.add_argument("--repo-id", type=str, help="HuggingFace Hub dataset repository ID")
+
+    parser.add_argument(
+        "--camera-key",
+        type=str,
+        required=True,
+        help="Image or video observation key (e.g. observation.images.base)",
+    )
+    parser.add_argument(
+        "--model",
+        type=str,
+        default="Qwen/Qwen2-VL-7B-Instruct",
+        help="VLM model: Qwen2-VL or Qwen3-VL (default: Qwen/Qwen2-VL-7B-Instruct)",
+    )
+    parser.add_argument(
+        "--device",
+        type=str,
+        default="cuda",
+    )
+    parser.add_argument(
+        "--window-size",
+        type=int,
+        default=8,
+        help="Number of frames per window (default: 8)",
+    )
+    parser.add_argument(
+        "--stride",
+        type=int,
+        default=None,
+        help="Stride for sliding window (default: window_size = non-overlapping)",
+    )
+    parser.add_argument(
+        "--batch-size",
+        type=int,
+        default=1,
+        help="Number of windows to process in one VLM call (default: 1; increase for speed)",
+    )
+    parser.add_argument(
+        "--max-frames-per-window",
+        type=int,
+        default=None,
+        metavar="N",
+        help="If window has more than N frames, randomly sample N frames (order kept) to avoid OOM (e.g. 16)",
+    )
+    parser.add_argument("--episodes", type=int, nargs="+", help="Episode indices to annotate (default: all)")
+    parser.add_argument("--skip-existing", action="store_true", help="Skip episodes that already have annotations")
+    parser.add_argument(
+        "--subtask-labels",
+        type=str,
+        nargs="*",
+        default=None,
+        help="Closed vocabulary: model must choose only from these labels",
+    )
+    parser.add_argument("--output-dir", type=str, help="Output directory for dataset with subtask_index")
+    parser.add_argument("--output-repo-id", type=str, help="Output repo id (default: <repo_id>_with_subtasks)")
+    parser.add_argument("--push-to-hub", action="store_true")
+
+    args = parser.parse_args()
+    console = Console()
+
+    # Load dataset
+    console.print("[cyan]Loading dataset...[/cyan]")
+    if args.data_dir:
+        dataset = LeRobotDataset(repo_id="local/dataset", root=args.data_dir, download_videos=False)
+    else:
+        dataset = LeRobotDataset(repo_id=args.repo_id, download_videos=True)
+    camera_keys = dataset.meta.camera_keys
+    if args.camera_key not in camera_keys:
+        console.print(f"[red]Error: camera key '{args.camera_key}' not in {camera_keys}[/red]")
+        return
+    console.print(f"[green]✓ Loaded dataset, {dataset.meta.total_episodes} episodes[/green]")
+
+    # Same Qwen VLM as subtask_annotate (Qwen2-VL or Qwen3-VL), image windows instead of video
+    segmenter = get_image_segmenter(args.model, args.device, torch.bfloat16)
+
+    annotator = SkillAnnotatorImage(
+        segmenter=segmenter,
+        window_size=args.window_size,
+        stride=args.stride,
+        batch_size=args.batch_size,
+        max_frames_per_window=args.max_frames_per_window,
+        console=console,
+    )
+    annotations = annotator.annotate_dataset(
+        dataset=dataset,
+        camera_key=args.camera_key,
+        episodes=args.episodes,
+        skip_existing=args.skip_existing,
+        subtask_labels=args.subtask_labels,
+    )
+
+    if not annotations:
+        console.print("[yellow]No annotations to save.[/yellow]")
+        return
+
+    output_dir = Path(args.output_dir) if args.output_dir else None
+    output_repo_id = args.output_repo_id
+    new_dataset = save_skill_annotations(dataset, annotations, output_dir, output_repo_id)
+
+    total_skills = sum(len(a.skills) for a in annotations.values())
+    console.print(f"[bold green]✓ Done.[/bold green] Episodes: {len(annotations)}, total window skills: {total_skills}")
+    console.print(f"  Dataset with subtask_index: {new_dataset.root}")
+
+    if args.push_to_hub and not args.data_dir:
+        console.print("[cyan]Pushing to Hub...[/cyan]")
+        try:
+            new_dataset.push_to_hub(push_videos=False)
+            console.print("[green]✓ Pushed.[/green]")
+        except Exception as e:
+            console.print(f"[red]Push failed: {e}[/red]")
+
+
+if __name__ == "__main__":
+    main()

src/lerobot/datasets/dataset_tools.py

@@ -37,7 +37,7 @@ import torch
 from tqdm import tqdm
 
 from lerobot.datasets.aggregate import aggregate_datasets
-from lerobot.datasets.compute_stats import aggregate_stats, compute_episode_stats, get_feature_stats
+from lerobot.datasets.compute_stats import aggregate_stats
 from lerobot.datasets.lerobot_dataset import LeRobotDataset, LeRobotDatasetMetadata
 from lerobot.datasets.utils import (
     DATA_DIR,
@@ -1522,122 +1522,6 @@ def modify_tasks(
     return dataset
 
 
-def recompute_stats(
-    dataset: LeRobotDataset,
-    skip_image_video: bool = True,
-    delta_action: bool = False,
-    delta_exclude_joints: list[str] | None = None,
-) -> LeRobotDataset:
-    """Recompute stats.json from scratch by iterating all episodes.
-
-    Args:
-        dataset: The LeRobotDataset to recompute stats for.
-        skip_image_video: If True (default), only recompute stats for numeric features
-            (action, state, etc.) and keep existing image/video stats unchanged.
-        delta_action: If True, compute action stats as delta (action - state).
-            Useful when training with use_delta_actions=True so normalization matches.
-        delta_exclude_joints: Joint names to exclude from delta conversion when
-            delta_action=True. These dims keep absolute stats. Uses dataset's
-            action feature names to build the mask. Default: ["gripper"].
-
-    Returns:
-        The same dataset with updated stats.
-    """
-    features = dataset.meta.features
-    numeric_features = {
-        k: v for k, v in features.items()
-        if v["dtype"] not in ["image", "video", "string"]
-        and k not in ["index", "episode_index", "task_index", "frame_index", "timestamp"]
-    }
-
-    if skip_image_video:
-        features_to_compute = numeric_features
-    else:
-        features_to_compute = {
-            k: v for k, v in features.items()
-            if v["dtype"] != "string"
-            and k not in ["index", "episode_index", "task_index", "frame_index", "timestamp"]
-        }
-
-    # Build delta mask if delta_action is enabled
-    delta_mask = None
-    if delta_action and "action" in features and "observation.state" in features:
-        if delta_exclude_joints is None:
-            delta_exclude_joints = ["gripper"]
-        action_names = features["action"].get("names")
-        if action_names is not None:
-            exclude = set(delta_exclude_joints)
-            delta_mask = [n not in exclude for n in action_names]
-        else:
-            action_dim = features["action"]["shape"][0]
-            delta_mask = [True] * action_dim
-        # Only recompute action stats when delta is enabled — state stays unchanged
-        features_to_compute = {"action": features["action"]}
-        logging.info(f"Recomputing action stats as delta (exclude: {delta_exclude_joints})")
-    else:
-        logging.info(f"Recomputing stats for features: {list(features_to_compute.keys())}")
-
-    data_dir = dataset.root / DATA_DIR
-    parquet_files = sorted(data_dir.glob("*/*.parquet"))
-    if not parquet_files:
-        raise ValueError(f"No parquet files found in {data_dir}")
-
-    all_episode_stats = []
-    numeric_keys = [k for k, v in features_to_compute.items() if v["dtype"] not in ["image", "video"]]
-    # Also need state for delta computation even though we don't recompute state stats
-    needs_state = delta_mask is not None
-
-    for parquet_path in tqdm(parquet_files, desc="Computing stats from data files"):
-        df = pd.read_parquet(parquet_path)
-
-        for ep_idx in sorted(df["episode_index"].unique()):
-            ep_df = df[df["episode_index"] == ep_idx]
-            episode_data = {}
-            for key in numeric_keys:
-                if key in ep_df.columns:
-                    values = ep_df[key].values
-                    if hasattr(values[0], "__len__"):
-                        episode_data[key] = np.stack(values)
-                    else:
-                        episode_data[key] = np.array(values)
-
-            # Apply delta conversion to actions before computing stats
-            if delta_mask is not None and "action" in episode_data:
-                from lerobot.processor.delta_action_processor import to_delta_actions
-
-                # Load state for delta even if we're not computing state stats
-                if needs_state and "observation.state" in ep_df.columns:
-                    state_values = ep_df["observation.state"].values
-                    if hasattr(state_values[0], "__len__"):
-                        states = np.stack(state_values)
-                    else:
-                        states = np.array(state_values)
-                    actions_t = torch.from_numpy(episode_data["action"]).float()
-                    states_t = torch.from_numpy(states).float()
-                    episode_data["action"] = to_delta_actions(actions_t, states_t, delta_mask).numpy()
-
-            ep_stats = compute_episode_stats(episode_data, features_to_compute)
-            all_episode_stats.append(ep_stats)
-
-    if not all_episode_stats:
-        logging.warning("No episode stats computed")
-        return dataset
-
-    new_stats = aggregate_stats(all_episode_stats)
-
-    # Merge: keep existing stats for features we didn't recompute
-    if dataset.meta.stats:
-        for key, value in dataset.meta.stats.items():
-            if key not in new_stats:
-                new_stats[key] = value
-
-    write_stats(new_stats, dataset.root)
-    dataset.meta.stats = new_stats
-
-    logging.info(f"Stats recomputed for {len(all_episode_stats)} episodes")
-    return dataset
-
-
 def convert_image_to_video_dataset(
     dataset: LeRobotDataset,
     output_dir: Path,

src/lerobot/datasets/lerobot_dataset.py

@@ -59,6 +59,7 @@ from lerobot.datasets.utils import (
     load_stats,
     load_subtasks,
     load_tasks,
+    load_tasks_high_level,
     update_chunk_file_indices,
     validate_episode_buffer,
     validate_frame,
@@ -163,6 +164,7 @@ class LeRobotDatasetMetadata:
         self.info = load_info(self.root)
         check_version_compatibility(self.repo_id, self._version, CODEBASE_VERSION)
         self.tasks = load_tasks(self.root)
+        self.tasks_high_level = load_tasks_high_level(self.root)
         self.subtasks = load_subtasks(self.root)
         self.episodes = load_episodes(self.root)
         self.stats = load_stats(self.root)
@@ -520,6 +522,7 @@ class LeRobotDatasetMetadata:
         _validate_feature_names(features)
 
         obj.tasks = None
+        obj.tasks_high_level = None
         obj.subtasks = None
         obj.episodes = None
         obj.stats = None
@@ -1067,7 +1070,17 @@ class LeRobotDataset(torch.utils.data.Dataset):
         if len(self.meta.video_keys) > 0:
             current_ts = item["timestamp"].item()
             query_timestamps = self._get_query_timestamps(current_ts, query_indices)
-            video_frames = self._query_videos(query_timestamps, ep_idx)
+            try:
+                video_frames = self._query_videos(query_timestamps, ep_idx)
+            except Exception as e:
+                print("\n" + "=" * 120)
+                print("[VIDEO DECODE FAILURE]")
+                print(f"item={item}")
+                print(f"query_indices={query_indices}")
+                print(f"query_timestamps={query_timestamps}")
+                print(f"ep_idx={ep_idx}")
+                print("=" * 120 + "\n")
+                raise
             item = {**video_frames, **item}
 
         if self.image_transforms is not None:
@@ -1078,6 +1091,14 @@ class LeRobotDataset(torch.utils.data.Dataset):
         # Add task as a string
         task_idx = item["task_index"].item()
         item["task"] = self.meta.tasks.iloc[task_idx].name
+        
+        # optionally add high level task index
+        if "task_index_high_level" in self.features:
+            high_level_task_idx = item["task_index_high_level"].item()
+            item["robot_utterance"] = self.meta.tasks_high_level.iloc[high_level_task_idx]["robot_utterance"]
+            item["user_prompt"] = self.meta.tasks_high_level.iloc[high_level_task_idx]["user_prompt"]
+        
+        
 
         # add subtask information if available
         if "subtask_index" in self.features and self.meta.subtasks is not None:

src/lerobot/datasets/utils.py

@@ -62,6 +62,8 @@ CHUNK_FILE_PATTERN = "chunk-{chunk_index:03d}/file-{file_index:03d}"
 DEFAULT_TASKS_PATH = "meta/tasks.parquet"
 DEFAULT_SUBTASKS_PATH = "meta/subtasks.parquet"
 DEFAULT_EPISODES_PATH = EPISODES_DIR + "/" + CHUNK_FILE_PATTERN + ".parquet"
+DEFAULT_TASKS_HIGH_LEVEL_PATH = "meta/tasks_high_level.parquet"
+DEFAULT_SUBTASKS_PATH = "meta/subtasks.parquet"
 DEFAULT_DATA_PATH = DATA_DIR + "/" + CHUNK_FILE_PATTERN + ".parquet"
 DEFAULT_VIDEO_PATH = VIDEO_DIR + "/{video_key}/" + CHUNK_FILE_PATTERN + ".mp4"
 DEFAULT_IMAGE_PATH = "images/{image_key}/episode-{episode_index:06d}/frame-{frame_index:06d}.png"
@@ -353,6 +355,20 @@ def load_tasks(local_dir: Path) -> pandas.DataFrame:
     tasks = pd.read_parquet(local_dir / DEFAULT_TASKS_PATH)
     return tasks
 
+def load_tasks_high_level(local_dir: Path) -> pandas.DataFrame | None:
+    """Load high-level tasks from tasks_high_level.parquet if it exists."""
+    tasks_high_level_path = local_dir / DEFAULT_TASKS_HIGH_LEVEL_PATH
+    if tasks_high_level_path.exists():
+        return pd.read_parquet(tasks_high_level_path)
+    return None
+
+
+def load_subtasks(local_dir: Path) -> pandas.DataFrame | None:
+    """Load subtasks from subtasks.parquet if it exists."""
+    subtasks_path = local_dir / DEFAULT_SUBTASKS_PATH
+    if subtasks_path.exists():
+        return pd.read_parquet(subtasks_path)
+    return None
 
 def load_subtasks(local_dir: Path) -> pandas.DataFrame | None:
     """Load subtasks from subtasks.parquet if it exists."""

src/lerobot/envs/libero.py

@@ -112,7 +112,6 @@ class LiberoEnv(gym.Env):
         visualization_height: int = 480,
         init_states: bool = True,
         episode_index: int = 0,
-        n_envs: int = 1,
         camera_name_mapping: dict[str, str] | None = None,
         num_steps_wait: int = 10,
         control_mode: str = "relative",
@@ -146,9 +145,7 @@ class LiberoEnv(gym.Env):
         self.episode_length = episode_length
         # Load once and keep
         self._init_states = get_task_init_states(task_suite, self.task_id) if self.init_states else None
-        self._reset_stride = n_envs  # when performing a reset, append `_reset_stride` to `init_state_id`.
-
-        self.init_state_id = self.episode_index  # tie each sub-env to a fixed init state
+        self._init_state_id = self.episode_index  # tie each sub-env to a fixed init state
 
         self._env = self._make_envs_task(task_suite, self.task_id)
         default_steps = 500
@@ -298,8 +295,7 @@ class LiberoEnv(gym.Env):
         self._env.seed(seed)
         raw_obs = self._env.reset()
         if self.init_states and self._init_states is not None:
-            raw_obs = self._env.set_init_state(self._init_states[self.init_state_id % len(self._init_states)])
-            self.init_state_id += self._reset_stride  # Change init_state_id when reset
+            raw_obs = self._env.set_init_state(self._init_states[self._init_state_id])
 
         # After reset, objects may be unstable (slightly floating, intersecting, etc.).
         # Step the simulator with a no-op action for a few frames so everything settles.
@@ -377,7 +373,6 @@ def _make_env_fns(
             init_states=init_states,
             episode_length=episode_length,
             episode_index=episode_index,
-            n_envs=n_envs,
             control_mode=control_mode,
             **local_kwargs,
         )

src/lerobot/motors/calibration_gui.py

@@ -221,7 +221,7 @@ class RangeFinderGUI:
 
         self.bus = bus
         self.groups = groups if groups is not None else {"all": list(bus.motors)}
-        self.group_names = list(self.groups)
+        self.group_names = list(groups)
         self.current_group = self.group_names[0]
 
         if not bus.is_connected:
@@ -230,20 +230,18 @@ class RangeFinderGUI:
         self.calibration = bus.read_calibration()
         self.res_table = bus.model_resolution_table
         self.present_cache = {
-            m: bus.read("Present_Position", m, normalize=False)
-            for motors in self.groups.values()
-            for m in motors
+            m: bus.read("Present_Position", m, normalize=False) for motors in groups.values() for m in motors
         }
 
         pygame.init()
         self.font = pygame.font.Font(None, FONT_SIZE)
 
-        label_pad = max(self.font.size(m)[0] for ms in self.groups.values() for m in ms)
+        label_pad = max(self.font.size(m)[0] for ms in groups.values() for m in ms)
         self.label_pad = label_pad
         width = 40 + label_pad + BAR_LEN + 6 + BTN_W + 10 + SAVE_W + 10
         self.controls_bottom = 10 + SAVE_H
         self.base_y = self.controls_bottom + TOP_GAP
-        height = self.base_y + PADDING_Y * len(self.groups[self.current_group]) + 40
+        height = self.base_y + PADDING_Y * len(groups[self.current_group]) + 40
 
         self.screen = pygame.display.set_mode((width, height))
         pygame.display.set_caption("Motors range finder")

src/lerobot/motors/damiao/damiao.py

@@ -23,7 +23,6 @@ from copy import deepcopy
 from functools import cached_property
 from typing import TYPE_CHECKING, Any, TypedDict
 
-from lerobot.utils.decorators import check_if_already_connected, check_if_not_connected
 from lerobot.utils.import_utils import _can_available
 
 if TYPE_CHECKING or _can_available:
@@ -37,6 +36,7 @@ else:
 
 import numpy as np
 
+from lerobot.utils.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError
 from lerobot.utils.robot_utils import precise_sleep
 from lerobot.utils.utils import enter_pressed, move_cursor_up
 
@@ -155,7 +155,6 @@ class DamiaoMotorsBus(MotorsBusBase):
         """Check if the CAN bus is connected."""
         return self._is_connected and self.canbus is not None
 
-    @check_if_already_connected
     def connect(self, handshake: bool = True) -> None:
         """
         Open the CAN bus and initialize communication.
@@ -163,6 +162,10 @@ class DamiaoMotorsBus(MotorsBusBase):
         Args:
             handshake: If True, ping all motors to verify they're present
         """
+        if self.is_connected:
+            raise DeviceAlreadyConnectedError(
+                f"{self.__class__.__name__}('{self.port}') is already connected."
+            )
 
         try:
             # Auto-detect interface type based on port name
@@ -208,9 +211,6 @@ class DamiaoMotorsBus(MotorsBusBase):
         logger.info("Starting handshake with motors...")
 
         # Drain any pending messages
-        if self.canbus is None:
-            raise RuntimeError("CAN bus is not initialized.")
-
         while self.canbus.recv(timeout=0.01):
             pass
 
@@ -246,7 +246,6 @@ class DamiaoMotorsBus(MotorsBusBase):
             )
         logger.info("Handshake successful. All motors ready.")
 
-    @check_if_not_connected
     def disconnect(self, disable_torque: bool = True) -> None:
         """
         Close the CAN bus connection.
@@ -254,6 +253,8 @@ class DamiaoMotorsBus(MotorsBusBase):
         Args:
             disable_torque: If True, disable torque on all motors before disconnecting
         """
+        if not self.is_connected:
+            raise DeviceNotConnectedError(f"{self.__class__.__name__}('{self.port}') is not connected.")
 
         if disable_torque:
             try:
@@ -282,10 +283,6 @@ class DamiaoMotorsBus(MotorsBusBase):
         recv_id = self._get_motor_recv_id(motor)
         data = [0xFF] * 7 + [command_byte]
         msg = can.Message(arbitration_id=motor_id, data=data, is_extended_id=False, is_fd=self.use_can_fd)
-
-        if self.canbus is None:
-            raise RuntimeError("CAN bus is not initialized.")
-
         self.canbus.send(msg)
         if msg := self._recv_motor_response(expected_recv_id=recv_id):
             self._process_response(motor_name, msg)
@@ -344,10 +341,6 @@ class DamiaoMotorsBus(MotorsBusBase):
         recv_id = self._get_motor_recv_id(motor)
         data = [motor_id & 0xFF, (motor_id >> 8) & 0xFF, CAN_CMD_REFRESH, 0, 0, 0, 0, 0]
         msg = can.Message(arbitration_id=CAN_PARAM_ID, data=data, is_extended_id=False, is_fd=self.use_can_fd)
-
-        if self.canbus is None:
-            raise RuntimeError("CAN bus is not initialized.")
-
         self.canbus.send(msg)
         return self._recv_motor_response(expected_recv_id=recv_id)
 
@@ -363,10 +356,6 @@ class DamiaoMotorsBus(MotorsBusBase):
         Returns:
             CAN message if received, None otherwise
         """
-
-        if self.canbus is None:
-            raise RuntimeError("CAN bus is not initialized.")
-
         try:
             start_time = time.time()
             messages_seen = []
@@ -405,13 +394,10 @@ class DamiaoMotorsBus(MotorsBusBase):
         Returns:
             Dictionary mapping recv_id to CAN message
         """
-        responses: dict[int, can.Message] = {}
+        responses = {}
         expected_set = set(expected_recv_ids)
         start_time = time.time()
 
-        if self.canbus is None:
-            raise RuntimeError("CAN bus is not initialized.")
-
         try:
             while len(responses) < len(expected_recv_ids) and (time.time() - start_time) < timeout:
                 # 100us poll timeout
@@ -475,9 +461,6 @@ class DamiaoMotorsBus(MotorsBusBase):
         motor_name = self._get_motor_name(motor)
         motor_type = self._motor_types[motor_name]
 
-        if self.canbus is None:
-            raise RuntimeError("CAN bus is not initialized.")
-
         data = self._encode_mit_packet(motor_type, kp, kd, position_degrees, velocity_deg_per_sec, torque)
         msg = can.Message(arbitration_id=motor_id, data=data, is_extended_id=False, is_fd=self.use_can_fd)
         self.canbus.send(msg)
@@ -505,9 +488,6 @@ class DamiaoMotorsBus(MotorsBusBase):
 
         recv_id_to_motor: dict[int, str] = {}
 
-        if self.canbus is None:
-            raise RuntimeError("CAN bus is not initialized.")
-
         # Step 1: Send all MIT control commands
         for motor, (kp, kd, position_degrees, velocity_deg_per_sec, torque) in commands.items():
             motor_id = self._get_motor_id(motor)
@@ -582,9 +562,10 @@ class DamiaoMotorsBus(MotorsBusBase):
         except Exception as e:
             logger.warning(f"Failed to decode response from {motor}: {e}")
 
-    @check_if_not_connected
     def read(self, data_name: str, motor: str) -> Value:
         """Read a value from a single motor. Positions are always in degrees."""
+        if not self.is_connected:
+            raise DeviceNotConnectedError(f"{self} is not connected.")
 
         # Refresh motor to get latest state
         msg = self._refresh_motor(motor)
@@ -614,7 +595,6 @@ class DamiaoMotorsBus(MotorsBusBase):
             raise ValueError(f"Unknown data_name: {data_name}")
         return mapping[data_name]
 
-    @check_if_not_connected
     def write(
         self,
         data_name: str,
@@ -625,6 +605,8 @@ class DamiaoMotorsBus(MotorsBusBase):
         Write a value to a single motor. Positions are always in degrees.
         Can write 'Goal_Position', 'Kp', or 'Kd'.
         """
+        if not self.is_connected:
+            raise DeviceNotConnectedError(f"{self} is not connected.")
 
         if data_name in ("Kp", "Kd"):
             self._gains[motor][data_name.lower()] = float(value)
@@ -674,10 +656,6 @@ class DamiaoMotorsBus(MotorsBusBase):
 
     def _batch_refresh(self, motors: list[str]) -> None:
         """Internal helper to refresh a list of motors and update cache."""
-
-        if self.canbus is None:
-            raise RuntimeError("CAN bus is not initialized.")
-
         # Send refresh commands
         for motor in motors:
             motor_id = self._get_motor_id(motor)
@@ -700,12 +678,10 @@ class DamiaoMotorsBus(MotorsBusBase):
             else:
                 logger.warning(f"Packet drop: {motor} (ID: 0x{recv_id:02X}). Using last known state.")
 
-    @check_if_not_connected
-    def sync_write(self, data_name: str, values: dict[str, Value]) -> None:
+    def sync_write(self, data_name: str, values: Value | dict[str, Value]) -> None:
         """
         Write values to multiple motors simultaneously. Positions are always in degrees.
         """
-
         if data_name in ("Kp", "Kd"):
             key = data_name.lower()
             for motor, val in values.items():
@@ -714,8 +690,6 @@ class DamiaoMotorsBus(MotorsBusBase):
         elif data_name == "Goal_Position":
             # Step 1: Send all MIT control commands
             recv_id_to_motor: dict[int, str] = {}
-            if self.canbus is None:
-                raise RuntimeError("CAN bus is not initialized.")
             for motor, value_degrees in values.items():
                 motor_id = self._get_motor_id(motor)
                 motor_name = self._get_motor_name(motor)
@@ -758,9 +732,9 @@ class DamiaoMotorsBus(MotorsBusBase):
 
     def record_ranges_of_motion(
         self,
-        motors: str | list[str] | None = None,
+        motors: NameOrID | list[NameOrID] | None = None,
         display_values: bool = True,
-    ) -> tuple[dict[str, Value], dict[str, Value]]:
+    ) -> tuple[dict[NameOrID, Value], dict[NameOrID, Value]]:
         """
         Interactively record the min/max values of each motor in degrees.
 

src/lerobot/motors/dynamixel/dynamixel.py

@@ -181,10 +181,10 @@ class DynamixelMotorsBus(SerialMotorsBus):
         for motor, m in self.motors.items():
             calibration[motor] = MotorCalibration(
                 id=m.id,
-                drive_mode=int(drive_modes[motor]),
-                homing_offset=int(offsets[motor]),
-                range_min=int(mins[motor]),
-                range_max=int(maxes[motor]),
+                drive_mode=drive_modes[motor],
+                homing_offset=offsets[motor],
+                range_min=mins[motor],
+                range_max=maxes[motor],
             )
 
         return calibration
@@ -198,7 +198,7 @@ class DynamixelMotorsBus(SerialMotorsBus):
         if cache:
             self.calibration = calibration_dict
 
-    def disable_torque(self, motors: int | str | list[str] | None = None, num_retry: int = 0) -> None:
+    def disable_torque(self, motors: str | list[str] | None = None, num_retry: int = 0) -> None:
         for motor in self._get_motors_list(motors):
             self.write("Torque_Enable", motor, TorqueMode.DISABLED.value, num_retry=num_retry)
 
@@ -206,7 +206,7 @@ class DynamixelMotorsBus(SerialMotorsBus):
         addr, length = get_address(self.model_ctrl_table, model, "Torque_Enable")
         self._write(addr, length, motor, TorqueMode.DISABLED.value, num_retry=num_retry)
 
-    def enable_torque(self, motors: int | str | list[str] | None = None, num_retry: int = 0) -> None:
+    def enable_torque(self, motors: str | list[str] | None = None, num_retry: int = 0) -> None:
         for motor in self._get_motors_list(motors):
             self.write("Torque_Enable", motor, TorqueMode.ENABLED.value, num_retry=num_retry)
 
@@ -235,7 +235,7 @@ class DynamixelMotorsBus(SerialMotorsBus):
         On Dynamixel Motors:
         Present_Position = Actual_Position + Homing_Offset
         """
-        half_turn_homings: dict[NameOrID, Value] = {}
+        half_turn_homings = {}
         for motor, pos in positions.items():
             model = self._get_motor_model(motor)
             max_res = self.model_resolution_table[model] - 1
@@ -258,6 +258,6 @@ class DynamixelMotorsBus(SerialMotorsBus):
             if raise_on_error:
                 raise ConnectionError(self.packet_handler.getTxRxResult(comm))
 
-            return None
+            return
 
         return {id_: data[0] for id_, data in data_list.items()}

src/lerobot/motors/feetech/feetech.py

@@ -126,7 +126,7 @@ class FeetechMotorsBus(SerialMotorsBus):
 
         self.port_handler = scs.PortHandler(self.port)
         # HACK: monkeypatch
-        self.port_handler.setPacketTimeout = patch_setPacketTimeout.__get__(  # type: ignore[method-assign]
+        self.port_handler.setPacketTimeout = patch_setPacketTimeout.__get__(
             self.port_handler, scs.PortHandler
         )
         self.packet_handler = scs.PacketHandler(protocol_version)
@@ -262,9 +262,9 @@ class FeetechMotorsBus(SerialMotorsBus):
             calibration[motor] = MotorCalibration(
                 id=m.id,
                 drive_mode=0,
-                homing_offset=int(offsets[motor]),
-                range_min=int(mins[motor]),
-                range_max=int(maxes[motor]),
+                homing_offset=offsets[motor],
+                range_min=mins[motor],
+                range_max=maxes[motor],
             )
 
         return calibration
@@ -284,7 +284,7 @@ class FeetechMotorsBus(SerialMotorsBus):
         On Feetech Motors:
         Present_Position = Actual_Position - Homing_Offset
         """
-        half_turn_homings: dict[NameOrID, Value] = {}
+        half_turn_homings = {}
         for motor, pos in positions.items():
             model = self._get_motor_model(motor)
             max_res = self.model_resolution_table[model] - 1
@@ -292,7 +292,7 @@ class FeetechMotorsBus(SerialMotorsBus):
 
         return half_turn_homings
 
-    def disable_torque(self, motors: int | str | list[str] | None = None, num_retry: int = 0) -> None:
+    def disable_torque(self, motors: str | list[str] | None = None, num_retry: int = 0) -> None:
         for motor in self._get_motors_list(motors):
             self.write("Torque_Enable", motor, TorqueMode.DISABLED.value, num_retry=num_retry)
             self.write("Lock", motor, 0, num_retry=num_retry)
@@ -303,7 +303,7 @@ class FeetechMotorsBus(SerialMotorsBus):
         addr, length = get_address(self.model_ctrl_table, model, "Lock")
         self._write(addr, length, motor, 0, num_retry=num_retry)
 
-    def enable_torque(self, motors: int | str | list[str] | None = None, num_retry: int = 0) -> None:
+    def enable_torque(self, motors: str | list[str] | None = None, num_retry: int = 0) -> None:
         for motor in self._get_motors_list(motors):
             self.write("Torque_Enable", motor, TorqueMode.ENABLED.value, num_retry=num_retry)
             self.write("Lock", motor, 1, num_retry=num_retry)
@@ -334,7 +334,7 @@ class FeetechMotorsBus(SerialMotorsBus):
     def _broadcast_ping(self) -> tuple[dict[int, int], int]:
         import scservo_sdk as scs
 
-        data_list: dict[int, int] = {}
+        data_list = {}
 
         status_length = 6
 
@@ -414,7 +414,7 @@ class FeetechMotorsBus(SerialMotorsBus):
         if not self._is_comm_success(comm):
             if raise_on_error:
                 raise ConnectionError(self.packet_handler.getTxRxResult(comm))
-            return None
+            return
 
         ids_errors = {id_: status for id_, status in ids_status.items() if self._is_error(status)}
         if ids_errors:

src/lerobot/motors/motors_bus.py

@@ -23,7 +23,6 @@ from __future__ import annotations
 
 import abc
 import logging
-from collections.abc import Sequence
 from contextlib import contextmanager
 from dataclasses import dataclass
 from enum import Enum
@@ -94,7 +93,7 @@ class MotorsBusBase(abc.ABC):
         pass
 
     @abc.abstractmethod
-    def sync_write(self, data_name: str, values: dict[str, Value]) -> None:
+    def sync_write(self, data_name: str, values: Value | dict[str, Value]) -> None:
         """Write values to multiple motors."""
         pass
 
@@ -180,16 +179,15 @@ class Motor:
 
 
 class PortHandler(Protocol):
-    is_open: bool
-    baudrate: int
-    packet_start_time: float
-    packet_timeout: float
-    tx_time_per_byte: float
-    is_using: bool
-    port_name: str
-    ser: serial.Serial
-
-    def __init__(self, port_name: str) -> None: ...
+    def __init__(self, port_name):
+        self.is_open: bool
+        self.baudrate: int
+        self.packet_start_time: float
+        self.packet_timeout: float
+        self.tx_time_per_byte: float
+        self.is_using: bool
+        self.port_name: str
+        self.ser: serial.Serial
 
     def openPort(self): ...
     def closePort(self): ...
@@ -242,22 +240,19 @@ class PacketHandler(Protocol):
     def regWriteTxRx(self, port, id, address, length, data): ...
     def syncReadTx(self, port, start_address, data_length, param, param_length): ...
     def syncWriteTxOnly(self, port, start_address, data_length, param, param_length): ...
-    def broadcastPing(self, port): ...
 
 
 class GroupSyncRead(Protocol):
-    port: str
-    ph: PortHandler
-    start_address: int
-    data_length: int
-    last_result: bool
-    is_param_changed: bool
-    param: list
-    data_dict: dict
+    def __init__(self, port, ph, start_address, data_length):
+        self.port: str
+        self.ph: PortHandler
+        self.start_address: int
+        self.data_length: int
+        self.last_result: bool
+        self.is_param_changed: bool
+        self.param: list
+        self.data_dict: dict
 
-    def __init__(
-        self, port: PortHandler, ph: PacketHandler, start_address: int, data_length: int
-    ) -> None: ...
     def makeParam(self): ...
     def addParam(self, id): ...
     def removeParam(self, id): ...
@@ -270,17 +265,15 @@ class GroupSyncRead(Protocol):
 
 
 class GroupSyncWrite(Protocol):
-    port: str
-    ph: PortHandler
-    start_address: int
-    data_length: int
-    is_param_changed: bool
-    param: list
-    data_dict: dict
+    def __init__(self, port, ph, start_address, data_length):
+        self.port: str
+        self.ph: PortHandler
+        self.start_address: int
+        self.data_length: int
+        self.is_param_changed: bool
+        self.param: list
+        self.data_dict: dict
 
-    def __init__(
-        self, port: PortHandler, ph: PacketHandler, start_address: int, data_length: int
-    ) -> None: ...
     def makeParam(self): ...
     def addParam(self, id, data): ...
     def removeParam(self, id): ...
@@ -407,7 +400,7 @@ class SerialMotorsBus(MotorsBusBase):
         else:
             raise TypeError(f"'{motor}' should be int, str.")
 
-    def _get_motor_model(self, motor: NameOrID) -> str:
+    def _get_motor_model(self, motor: NameOrID) -> int:
         if isinstance(motor, str):
             return self.motors[motor].model
         elif isinstance(motor, int):
@@ -415,19 +408,17 @@ class SerialMotorsBus(MotorsBusBase):
         else:
             raise TypeError(f"'{motor}' should be int, str.")
 
-    def _get_motors_list(self, motors: NameOrID | Sequence[NameOrID] | None) -> list[str]:
+    def _get_motors_list(self, motors: str | list[str] | None) -> list[str]:
         if motors is None:
             return list(self.motors)
         elif isinstance(motors, str):
             return [motors]
-        elif isinstance(motors, int):
-            return [self._id_to_name(motors)]
-        elif isinstance(motors, Sequence):
-            return [m if isinstance(m, str) else self._id_to_name(m) for m in motors]
+        elif isinstance(motors, list):
+            return motors.copy()
         else:
             raise TypeError(motors)
 
-    def _get_ids_values_dict(self, values: Value | dict[str, Value] | None) -> dict[int, Value]:
+    def _get_ids_values_dict(self, values: Value | dict[str, Value] | None) -> list[str]:
         if isinstance(values, (int | float)):
             return dict.fromkeys(self.ids, values)
         elif isinstance(values, dict):
@@ -649,19 +640,18 @@ class SerialMotorsBus(MotorsBusBase):
         pass
 
     @abc.abstractmethod
-    def enable_torque(self, motors: int | str | list[str] | None = None, num_retry: int = 0) -> None:
+    def enable_torque(self, motors: str | list[str] | None = None, num_retry: int = 0) -> None:
         """Enable torque on selected motors.
 
         Args:
-            motors (int | str | list[str] | None, optional): Same semantics as :pymeth:`disable_torque`.
-                Defaults to `None`.
+            motor (int): Same semantics as :pymeth:`disable_torque`. Defaults to `None`.
             num_retry (int, optional): Number of additional retry attempts on communication failure.
                 Defaults to 0.
         """
         pass
 
     @contextmanager
-    def torque_disabled(self, motors: str | list[str] | None = None):
+    def torque_disabled(self, motors: int | str | list[str] | None = None):
         """Context-manager that guarantees torque is re-enabled.
 
         This helper is useful to temporarily disable torque when configuring motors.
@@ -738,19 +728,24 @@ class SerialMotorsBus(MotorsBusBase):
         """
         pass
 
-    def reset_calibration(self, motors: NameOrID | Sequence[NameOrID] | None = None) -> None:
+    def reset_calibration(self, motors: NameOrID | list[NameOrID] | None = None) -> None:
         """Restore factory calibration for the selected motors.
 
         Homing offset is set to ``0`` and min/max position limits are set to the full usable range.
         The in-memory :pyattr:`calibration` is cleared.
 
         Args:
-            motors (NameOrID | Sequence[NameOrID] | None, optional): Selection of motors. `None` (default)
+            motors (NameOrID | list[NameOrID] | None, optional): Selection of motors. `None` (default)
                 resets every motor.
         """
-        motor_names = self._get_motors_list(motors)
+        if motors is None:
+            motors = list(self.motors)
+        elif isinstance(motors, (str | int)):
+            motors = [motors]
+        elif not isinstance(motors, list):
+            raise TypeError(motors)
 
-        for motor in motor_names:
+        for motor in motors:
             model = self._get_motor_model(motor)
             max_res = self.model_resolution_table[model] - 1
             self.write("Homing_Offset", motor, 0, normalize=False)
@@ -759,9 +754,7 @@ class SerialMotorsBus(MotorsBusBase):
 
         self.calibration = {}
 
-    def set_half_turn_homings(
-        self, motors: NameOrID | Sequence[NameOrID] | None = None
-    ) -> dict[NameOrID, Value]:
+    def set_half_turn_homings(self, motors: NameOrID | list[NameOrID] | None = None) -> dict[NameOrID, Value]:
         """Centre each motor range around its current position.
 
         The function computes and writes a homing offset such that the present position becomes exactly one
@@ -771,12 +764,17 @@ class SerialMotorsBus(MotorsBusBase):
             motors (NameOrID | list[NameOrID] | None, optional): Motors to adjust. Defaults to all motors (`None`).
 
         Returns:
-            dict[str, Value]: Mapping *motor name → written homing offset*.
+            dict[NameOrID, Value]: Mapping *motor → written homing offset*.
         """
-        motor_names = self._get_motors_list(motors)
+        if motors is None:
+            motors = list(self.motors)
+        elif isinstance(motors, (str | int)):
+            motors = [motors]
+        elif not isinstance(motors, list):
+            raise TypeError(motors)
 
-        self.reset_calibration(motor_names)
-        actual_positions = self.sync_read("Present_Position", motor_names, normalize=False)
+        self.reset_calibration(motors)
+        actual_positions = self.sync_read("Present_Position", motors, normalize=False)
         homing_offsets = self._get_half_turn_homings(actual_positions)
         for motor, offset in homing_offsets.items():
             self.write("Homing_Offset", motor, offset)
@@ -788,8 +786,8 @@ class SerialMotorsBus(MotorsBusBase):
         pass
 
     def record_ranges_of_motion(
-        self, motors: NameOrID | Sequence[NameOrID] | None = None, display_values: bool = True
-    ) -> tuple[dict[str, Value], dict[str, Value]]:
+        self, motors: NameOrID | list[NameOrID] | None = None, display_values: bool = True
+    ) -> tuple[dict[NameOrID, Value], dict[NameOrID, Value]]:
         """Interactively record the min/max encoder values of each motor.
 
         Move the joints by hand (with torque disabled) while the method streams live positions. Press
@@ -801,25 +799,30 @@ class SerialMotorsBus(MotorsBusBase):
             display_values (bool, optional): When `True` (default) a live table is printed to the console.
 
         Returns:
-            tuple[dict[str, Value], dict[str, Value]]: Two dictionaries *mins* and *maxes* with the
+            tuple[dict[NameOrID, Value], dict[NameOrID, Value]]: Two dictionaries *mins* and *maxes* with the
                 extreme values observed for each motor.
         """
-        motor_names = self._get_motors_list(motors)
+        if motors is None:
+            motors = list(self.motors)
+        elif isinstance(motors, (str | int)):
+            motors = [motors]
+        elif not isinstance(motors, list):
+            raise TypeError(motors)
 
-        start_positions = self.sync_read("Present_Position", motor_names, normalize=False)
+        start_positions = self.sync_read("Present_Position", motors, normalize=False)
         mins = start_positions.copy()
         maxes = start_positions.copy()
 
         user_pressed_enter = False
         while not user_pressed_enter:
-            positions = self.sync_read("Present_Position", motor_names, normalize=False)
+            positions = self.sync_read("Present_Position", motors, normalize=False)
             mins = {motor: min(positions[motor], min_) for motor, min_ in mins.items()}
             maxes = {motor: max(positions[motor], max_) for motor, max_ in maxes.items()}
 
             if display_values:
                 print("\n-------------------------------------------")
                 print(f"{'NAME':<15} | {'MIN':>6} | {'POS':>6} | {'MAX':>6}")
-                for motor in motor_names:
+                for motor in motors:
                     print(f"{motor:<15} | {mins[motor]:>6} | {positions[motor]:>6} | {maxes[motor]:>6}")
 
             if enter_pressed():
@@ -827,9 +830,9 @@ class SerialMotorsBus(MotorsBusBase):
 
             if display_values and not user_pressed_enter:
                 # Move cursor up to overwrite the previous output
-                move_cursor_up(len(motor_names) + 3)
+                move_cursor_up(len(motors) + 3)
 
-        same_min_max = [motor for motor in motor_names if mins[motor] == maxes[motor]]
+        same_min_max = [motor for motor in motors if mins[motor] == maxes[motor]]
         if same_min_max:
             raise ValueError(f"Some motors have the same min and max values:\n{pformat(same_min_max)}")
 
@@ -952,12 +955,12 @@ class SerialMotorsBus(MotorsBusBase):
             if raise_on_error:
                 raise ConnectionError(self.packet_handler.getTxRxResult(comm))
             else:
-                return None
+                return
         if self._is_error(error):
             if raise_on_error:
                 raise RuntimeError(self.packet_handler.getRxPacketError(error))
             else:
-                return None
+                return
 
         return model_number
 
@@ -1004,13 +1007,12 @@ class SerialMotorsBus(MotorsBusBase):
         err_msg = f"Failed to read '{data_name}' on {id_=} after {num_retry + 1} tries."
         value, _, _ = self._read(addr, length, id_, num_retry=num_retry, raise_on_error=True, err_msg=err_msg)
 
-        decoded = self._decode_sign(data_name, {id_: value})
+        id_value = self._decode_sign(data_name, {id_: value})
 
         if normalize and data_name in self.normalized_data:
-            normalized = self._normalize(decoded)
-            return normalized[id_]
+            id_value = self._normalize(id_value)
 
-        return decoded[id_]
+        return id_value[id_]
 
     def _read(
         self,
@@ -1021,7 +1023,7 @@ class SerialMotorsBus(MotorsBusBase):
         num_retry: int = 0,
         raise_on_error: bool = True,
         err_msg: str = "",
-    ) -> tuple[int, int, int]:
+    ) -> tuple[int, int]:
         if length == 1:
             read_fn = self.packet_handler.read1ByteTxRx
         elif length == 2:
@@ -1071,14 +1073,13 @@ class SerialMotorsBus(MotorsBusBase):
         model = self.motors[motor].model
         addr, length = get_address(self.model_ctrl_table, model, data_name)
 
-        int_value = int(value)
         if normalize and data_name in self.normalized_data:
-            int_value = self._unnormalize({id_: value})[id_]
+            value = self._unnormalize({id_: value})[id_]
 
-        int_value = self._encode_sign(data_name, {id_: int_value})[id_]
+        value = self._encode_sign(data_name, {id_: value})[id_]
 
-        err_msg = f"Failed to write '{data_name}' on {id_=} with '{int_value}' after {num_retry + 1} tries."
-        self._write(addr, length, id_, int_value, num_retry=num_retry, raise_on_error=True, err_msg=err_msg)
+        err_msg = f"Failed to write '{data_name}' on {id_=} with '{value}' after {num_retry + 1} tries."
+        self._write(addr, length, id_, value, num_retry=num_retry, raise_on_error=True, err_msg=err_msg)
 
     def _write(
         self,
@@ -1112,7 +1113,7 @@ class SerialMotorsBus(MotorsBusBase):
     def sync_read(
         self,
         data_name: str,
-        motors: NameOrID | Sequence[NameOrID] | None = None,
+        motors: str | list[str] | None = None,
         *,
         normalize: bool = True,
         num_retry: int = 0,
@@ -1121,7 +1122,7 @@ class SerialMotorsBus(MotorsBusBase):
 
         Args:
             data_name (str): Register name.
-            motors (NameOrID | Sequence[NameOrID] | None, optional): Motors to query. `None` (default) reads every motor.
+            motors (str | list[str] | None, optional): Motors to query. `None` (default) reads every motor.
             normalize (bool, optional): Normalisation flag.  Defaults to `True`.
             num_retry (int, optional): Retry attempts.  Defaults to `0`.
 
@@ -1142,17 +1143,16 @@ class SerialMotorsBus(MotorsBusBase):
         addr, length = get_address(self.model_ctrl_table, model, data_name)
 
         err_msg = f"Failed to sync read '{data_name}' on {ids=} after {num_retry + 1} tries."
-        raw_ids_values, _ = self._sync_read(
+        ids_values, _ = self._sync_read(
             addr, length, ids, num_retry=num_retry, raise_on_error=True, err_msg=err_msg
         )
 
-        decoded = self._decode_sign(data_name, raw_ids_values)
+        ids_values = self._decode_sign(data_name, ids_values)
 
         if normalize and data_name in self.normalized_data:
-            normalized = self._normalize(decoded)
-            return {self._id_to_name(id_): value for id_, value in normalized.items()}
+            ids_values = self._normalize(ids_values)
 
-        return {self._id_to_name(id_): value for id_, value in decoded.items()}
+        return {self._id_to_name(id_): value for id_, value in ids_values.items()}
 
     def _sync_read(
         self,
@@ -1224,24 +1224,21 @@ class SerialMotorsBus(MotorsBusBase):
             num_retry (int, optional): Retry attempts.  Defaults to `0`.
         """
 
-        raw_ids_values = self._get_ids_values_dict(values)
-        models = [self._id_to_model(id_) for id_ in raw_ids_values]
+        ids_values = self._get_ids_values_dict(values)
+        models = [self._id_to_model(id_) for id_ in ids_values]
         if self._has_different_ctrl_tables:
             assert_same_address(self.model_ctrl_table, models, data_name)
 
         model = next(iter(models))
         addr, length = get_address(self.model_ctrl_table, model, data_name)
 
-        int_ids_values = {id_: int(val) for id_, val in raw_ids_values.items()}
         if normalize and data_name in self.normalized_data:
-            int_ids_values = self._unnormalize(raw_ids_values)
+            ids_values = self._unnormalize(ids_values)
 
-        int_ids_values = self._encode_sign(data_name, int_ids_values)
+        ids_values = self._encode_sign(data_name, ids_values)
 
-        err_msg = f"Failed to sync write '{data_name}' with ids_values={int_ids_values} after {num_retry + 1} tries."
-        self._sync_write(
-            addr, length, int_ids_values, num_retry=num_retry, raise_on_error=True, err_msg=err_msg
-        )
+        err_msg = f"Failed to sync write '{data_name}' with {ids_values=} after {num_retry + 1} tries."
+        self._sync_write(addr, length, ids_values, num_retry=num_retry, raise_on_error=True, err_msg=err_msg)
 
     def _sync_write(
         self,

src/lerobot/policies/factory.py

@@ -34,6 +34,7 @@ from lerobot.policies.diffusion.configuration_diffusion import DiffusionConfig
 from lerobot.policies.groot.configuration_groot import GrootConfig
 from lerobot.policies.pi0.configuration_pi0 import PI0Config
 from lerobot.policies.pi05.configuration_pi05 import PI05Config
+from lerobot.policies.pi05_full.configuration_pi05 import PI05FullConfig
 from lerobot.policies.pretrained import PreTrainedPolicy
 from lerobot.policies.sac.configuration_sac import SACConfig
 from lerobot.policies.sac.reward_model.configuration_classifier import RewardClassifierConfig
@@ -390,6 +391,13 @@ def make_pre_post_processors(
             config=policy_cfg,
             dataset_stats=kwargs.get("dataset_stats"),
         )
+    elif isinstance(policy_cfg, PI05FullConfig):
+        from lerobot.policies.pi05_full.processor_pi05 import make_pi05_full_pre_post_processors
+
+        processors = make_pi05_full_pre_post_processors(
+            config=policy_cfg,
+            dataset_stats=kwargs.get("dataset_stats"),
+        )
 
     else:
         try:
@@ -470,13 +478,6 @@ def make_policy(
     cfg.output_features = {key: ft for key, ft in features.items() if ft.type is FeatureType.ACTION}
     if not cfg.input_features:
         cfg.input_features = {key: ft for key, ft in features.items() if key not in cfg.output_features}
-
-    # Store action feature names for delta_exclude_joints support
-    if ds_meta is not None and hasattr(cfg, "action_feature_names"):
-        action_names = ds_meta.features.get(ACTION, {}).get("names")
-        if action_names is not None:
-            cfg.action_feature_names = list(action_names)
-
     kwargs["config"] = cfg
 
     # Pass dataset_stats to the policy if available (needed for some policies like SARM)

src/lerobot/policies/pi0/configuration_pi0.py

@@ -50,13 +50,6 @@ class PI0Config(PreTrainedConfig):
     min_period: float = 4e-3
     max_period: float = 4.0
 
-    # Delta actions: converts absolute actions to delta (relative to state).
-    use_delta_actions: bool = False
-    # Joint names to exclude from delta (kept absolute). Empty list = all dims delta.
-    delta_exclude_joints: list[str] = field(default_factory=lambda: ["gripper"])
-    # Populated at runtime from dataset metadata by make_policy.
-    action_feature_names: list[str] | None = None
-
     # Real-Time Chunking (RTC) configuration
     rtc_config: RTCConfig | None = None
 

src/lerobot/policies/pi0/processor_pi0.py

@@ -21,10 +21,8 @@ import torch
 from lerobot.configs.types import PipelineFeatureType, PolicyFeature
 from lerobot.policies.pi0.configuration_pi0 import PI0Config
 from lerobot.processor import (
-    AbsoluteActionsProcessorStep,
     AddBatchDimensionProcessorStep,
     ComplementaryDataProcessorStep,
-    DeltaActionsProcessorStep,
     DeviceProcessorStep,
     NormalizerProcessorStep,
     PolicyAction,
@@ -128,13 +126,7 @@ def make_pi0_pre_post_processors(
         A tuple containing the configured pre-processor and post-processor pipelines.
     """
 
-    delta_step = DeltaActionsProcessorStep(
-        enabled=config.use_delta_actions,
-        exclude_joints=getattr(config, "delta_exclude_joints", []),
-        action_names=getattr(config, "action_feature_names", None),
-    )
-
-    # OpenPI order: raw → delta → normalize → model → unnormalize → absolute
+    # Add remaining processors
     input_steps: list[ProcessorStep] = [
         RenameObservationsProcessorStep(rename_map={}),  # To mimic the same processor as pretrained one
         AddBatchDimensionProcessorStep(),
@@ -146,7 +138,6 @@ def make_pi0_pre_post_processors(
             padding="max_length",
         ),
         DeviceProcessorStep(device=config.device),
-        delta_step,
         NormalizerProcessorStep(
             features={**config.input_features, **config.output_features},
             norm_map=config.normalization_mapping,
@@ -158,7 +149,6 @@ def make_pi0_pre_post_processors(
         UnnormalizerProcessorStep(
             features=config.output_features, norm_map=config.normalization_mapping, stats=dataset_stats
         ),
-        AbsoluteActionsProcessorStep(enabled=config.use_delta_actions, delta_step=delta_step),
         DeviceProcessorStep(device="cpu"),
     ]
 

src/lerobot/policies/pi05/configuration_pi05.py

@@ -50,13 +50,6 @@ class PI05Config(PreTrainedConfig):
     min_period: float = 4e-3
     max_period: float = 4.0
 
-    # Delta actions: converts absolute actions to delta (relative to state).
-    use_delta_actions: bool = False
-    # Joint names to exclude from delta (kept absolute). Empty list = all dims delta.
-    delta_exclude_joints: list[str] = field(default_factory=lambda: ["gripper"])
-    # Populated at runtime from dataset metadata by make_policy.
-    action_feature_names: list[str] | None = None
-
     # Real-Time Chunking (RTC) configuration
     rtc_config: RTCConfig | None = None
 

src/lerobot/policies/pi05/processor_pi05.py

@@ -25,9 +25,7 @@ from lerobot.configs.types import PipelineFeatureType, PolicyFeature
 from lerobot.policies.pi05.configuration_pi05 import PI05Config
 from lerobot.policies.pi05.modeling_pi05 import pad_vector
 from lerobot.processor import (
-    AbsoluteActionsProcessorStep,
     AddBatchDimensionProcessorStep,
-    DeltaActionsProcessorStep,
     DeviceProcessorStep,
     NormalizerProcessorStep,
     PolicyAction,
@@ -131,19 +129,10 @@ def make_pi05_pre_post_processors(
         A tuple containing the configured pre-processor and post-processor pipelines.
     """
 
-    delta_step = DeltaActionsProcessorStep(
-        enabled=config.use_delta_actions,
-        exclude_joints=getattr(config, "delta_exclude_joints", []),
-        action_names=getattr(config, "action_feature_names", None),
-    )
-
-    # OpenPI order: raw → delta → normalize → model → unnormalize → absolute
-    # NOTE: NormalizerProcessorStep MUST come before Pi05PrepareStateTokenizerProcessorStep
-    # because the tokenizer step expects normalized state in [-1, 1] range for discretization
+    # Add remaining processors
     input_steps: list[ProcessorStep] = [
         RenameObservationsProcessorStep(rename_map={}),  # To mimic the same processor as pretrained one
         AddBatchDimensionProcessorStep(),
-        delta_step,
         # NOTE: NormalizerProcessorStep MUST come before Pi05PrepareStateTokenizerProcessorStep
         # because the tokenizer step expects normalized state in [-1, 1] range for discretization
         NormalizerProcessorStep(
@@ -165,7 +154,6 @@ def make_pi05_pre_post_processors(
         UnnormalizerProcessorStep(
             features=config.output_features, norm_map=config.normalization_mapping, stats=dataset_stats
         ),
-        AbsoluteActionsProcessorStep(enabled=config.use_delta_actions, delta_step=delta_step),
         DeviceProcessorStep(device="cpu"),
     ]
 

src/lerobot/policies/pi05_full/README.md

@@ -0,0 +1,49 @@
+# π₀.₅ (pi05)
+
+This repository contains the Hugging Face port of **π₀.₅**, adapted from [OpenPI](https://github.com/Physical-Intelligence/openpi) by the Physical Intelligence.
+It is designed as a **Vision-Language-Action model with open-world generalization**.
+
+---
+
+## Model Overview
+
+| Feature              | π₀                                                     | π₀.₅                                      |
+| -------------------- | ------------------------------------------------------ | ----------------------------------------- |
+| Time Conditioning    | Concatenates time with actions via `action_time_mlp_*` | Uses `time_mlp_*` for AdaRMS conditioning |
+| AdaRMS               | Not used                                               | Used in action expert                     |
+| Tokenizer Length     | 48 tokens                                              | 200 tokens                                |
+| Discrete State Input | False (Uses `state_proj` layer)                        | True                                      |
+| Parameter Count      | Higher (includes state embedding)                      | Lower (no state embedding)                |
+
+---
+
+## Citation
+
+If you use this work, please cite both **OpenPI** and the π₀.₅ paper:
+
+```bibtex
+@misc{openpi2024,
+  author       = {Physical Intelligence Lab},
+  title        = {OpenPI: PyTorch Implementation of π0 and π0.5 Policies},
+  year         = {2024},
+  publisher    = {GitHub},
+  howpublished = {\url{https://github.com/Physical-Intelligence/openpi}},
+  license      = {Apache-2.0}
+}
+
+@misc{intelligence2025pi05visionlanguageactionmodelopenworld,
+  title        = {π₀.₅: a Vision-Language-Action Model with Open-World Generalization},
+  author       = {Physical Intelligence and Kevin Black and Noah Brown and James Darpinian and Karan Dhabalia and Danny Driess and Adnan Esmail and Michael Equi and Chelsea Finn and Niccolo Fusai and Manuel Y. Galliker and Dibya Ghosh and Lachy Groom and Karol Hausman and Brian Ichter and Szymon Jakubczak and Tim Jones and Liyiming Ke and Devin LeBlanc and Sergey Levine and Adrian Li-Bell and Mohith Mothukuri and Suraj Nair and Karl Pertsch and Allen Z. Ren and Lucy Xiaoyang Shi and Laura Smith and Jost Tobias Springenberg and Kyle Stachowicz and James Tanner and Quan Vuong and Homer Walke and Anna Walling and Haohuan Wang and Lili Yu and Ury Zhilinsky},
+  year         = {2025},
+  eprint       = {2504.16054},
+  archivePrefix= {arXiv},
+  primaryClass = {cs.LG},
+  url          = {https://arxiv.org/abs/2504.16054},
+}
+```
+
+---
+
+## License
+
+This port follows the **Apache 2.0 License**, consistent with the original [OpenPI repository](https://github.com/Physical-Intelligence/openpi).

src/lerobot/policies/pi05_full/__init__.py

@@ -0,0 +1,21 @@
+#!/usr/bin/env python
+
+# Copyright 2025 Physical Intelligence and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from .configuration_pi05 import PI05FullConfig
+from .modeling_pi05 import PI05FullPolicy
+from .processor_pi05 import make_pi05_full_pre_post_processors
+
+__all__ = ["PI05FullConfig", "PI05FullPolicy", "make_pi05_full_pre_post_processors"]

src/lerobot/policies/pi05_full/annotate/annotate_libero.sh

@@ -0,0 +1,50 @@
+#!/bin/bash
+
+# Example script to run synthetic data generation with Qwen VLM
+# This generates user prompts and robot utterances for hierarchical policy training
+
+# Configuration
+REPO_ID="lerobot/libero_10"
+MODEL="Qwen/Qwen3-VL-30B-A3B-Instruct"
+# or: MODEL="Qwen/Qwen2-VL-7B-Instruct"
+
+
+OUTPUT_DIR="/fsx/jade_choghari/outputs/libero-10-annotate-high"
+
+BATCH_SIZE=16
+TEMPERATURE=0.9
+SAMPLE_INTERVAL=5.0  # generate dialogue every 1 second (all episodes processed)
+
+# Run subtask annotation
+# python /admin/home/jade_choghari/lerobot/src/lerobot/policies/pi05_full/annotate/subtask_annotate.py \
+#     --repo-id "$REPO_ID" \
+#     --video-key observation.images.image \
+#     --output-dir "$OUTPUT_DIR" \
+#     --skip-existing \
+#     --output-repo-id "jadechoghari/libero10-annotate" \
+#     --batch-size "$BATCH_SIZE" \
+# run synthetic data generation (all episodes processed)
+# python examples/dataset/annotate_pgen.py \
+#     --repo-id "$REPO_ID" \
+#     --model "$MODEL" \
+#     --output-dir "$OUTPUT_DIR" \
+#     --temperature "$TEMPERATURE" \
+#     --batch-size "$BATCH_SIZE" \
+#     --sample-interval "$SAMPLE_INTERVAL" \
+#     --image-key observation.images.base \
+#     --num-image-views-per-sample 1
+
+# for faster testing, increase sample interval:
+# --sample-interval 5.0  # Samples every 5 seconds (much faster)
+
+# to push to hub after generation:
+# add --push-to-hub flag
+
+# efficient batch processing: 4 episodes at once
+python /admin/home/jade_choghari/lerobot/src/lerobot/policies/pi05_full/annotate/high_level_annotate.py \
+    --data-dir "/fsx/jade_choghari/outputs/libero-10-annotate" \
+    --output-dir "$OUTPUT_DIR" \
+    --video-mode \
+    --video-key observation.images.image \
+    --video-batch-size "$BATCH_SIZE" \
+    --sample-interval 5.0

src/lerobot/policies/pi05_full/annotate/load_lerobot_high.py

@@ -0,0 +1,52 @@
+import torch
+from huggingface_hub import HfApi
+
+import lerobot
+from lerobot.datasets.lerobot_dataset import LeRobotDataset, LeRobotDatasetMetadata
+from lerobot.policies.factory import make_pre_post_processors
+from lerobot.configs.policies import PreTrainedConfig
+
+# /fsx/jade_choghari/data/libero_10_subtasks_kw_converted
+dataset = LeRobotDataset(repo_id="lerobot/libero_10_image_subtask")
+
+dataloader = torch.utils.data.DataLoader(
+        dataset,
+        num_workers=0,
+        batch_size=2,
+        shuffle=True,
+)
+
+cfg = PreTrainedConfig.from_pretrained(
+    pretrained_name_or_path="/fsx/jade_choghari/models/pi05-base",
+)
+cfg.dtype = "bfloat16"
+
+pre_processor, post_processor = make_pre_post_processors(
+    policy_cfg=cfg,
+    pretrained_path="/fsx/jade_choghari/models/pi05-base",
+)
+batch = next(iter(dataloader))
+breakpoint()
+batch1 = pre_processor(batch)
+breakpoint()
+print(batch.keys())
+# print(batch['task_index_high_level'].shape)
+# print(batch['task_index_high_level'])
+# print(batch['user_prompt'][0])
+# print(batch['robot_utterance'][0])
+# print(batch['task'][0])
+
+valid_episode_list = []
+for episode_idx in range(len(dataset.meta.episodes)):
+        subtask_index = dataset[episode_idx]["subtask_index"]
+        valid_episode_list.append(episode_idx)
+
+print(len(valid_episode_list))
+
+# read this parquet /fsx/jade_choghari/outputs/pgen_annotations1/meta/tasks.parquett
+# import pandas as pd
+# tasks_df = pd.read_parquet('/fsx/jade_choghari/outputs/pgen_annotations1/meta/tasks.parquet')
+
+# # print all
+# print(tasks_df.columns)
+# breakpoint()

src/lerobot/policies/pi05_full/annotate/run_pgen.sh

@@ -0,0 +1,49 @@
+#!/bin/bash
+
+# Example script to run synthetic data generation with Qwen VLM
+# This generates user prompts and robot utterances for hierarchical policy training
+
+# Configuration
+REPO_ID="jadechoghari/collect-data"
+MODEL="Qwen/Qwen3-VL-30B-A3B-Instruct"
+# or: MODEL="Qwen/Qwen2-VL-7B-Instruct"
+
+
+OUTPUT_DIR="/fsx/jade_choghari/outputs/collect-data-pgen_new"
+
+BATCH_SIZE=32
+TEMPERATURE=0.9
+SAMPLE_INTERVAL=5.0  # generate dialogue every 1 second (all episodes processed)
+
+# Run subtask annotation
+python /admin/home/jade_choghari/lerobot/src/lerobot/policies/pi05_full/annotate/subtask_annotate.py \
+    --repo-id "$REPO_ID" \
+    --video-key observation.images.base \
+    --output-dir "$OUTPUT_DIR" \
+    --output-repo-id "jadechoghari/collect-data-with-subtasks"
+# run synthetic data generation (all episodes processed)
+# python examples/dataset/annotate_pgen.py \
+#     --repo-id "$REPO_ID" \
+#     --model "$MODEL" \
+#     --output-dir "$OUTPUT_DIR" \
+#     --temperature "$TEMPERATURE" \
+#     --batch-size "$BATCH_SIZE" \
+#     --sample-interval "$SAMPLE_INTERVAL" \
+#     --image-key observation.images.base \
+#     --num-image-views-per-sample 1
+
+# for faster testing, increase sample interval:
+# --sample-interval 5.0  # Samples every 5 seconds (much faster)
+
+# to push to hub after generation:
+# add --push-to-hub flag
+
+# efficient batch processing: 4 episodes at once
+# python examples/dataset/annotate_pgen.py \
+#     --repo-id "$REPO_ID" \
+#     --model "$MODEL" \
+#     --output-dir "$OUTPUT_DIR" \
+#     --video-mode \
+#     --video-key observation.images.up \
+#     --video-batch-size "$BATCH_SIZE" \
+#     --sample-interval 1.0

src/lerobot/policies/pi05_full/configuration_pi05.py

@@ -0,0 +1,183 @@
+#!/usr/bin/env python
+
+# Copyright 2025 Physical Intelligence and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from dataclasses import dataclass, field
+
+from lerobot.configs.policies import PreTrainedConfig
+from lerobot.configs.types import FeatureType, NormalizationMode, PolicyFeature
+from lerobot.optim.optimizers import AdamWConfig
+from lerobot.optim.schedulers import CosineDecayWithWarmupSchedulerConfig
+from lerobot.policies.rtc.configuration_rtc import RTCConfig
+from lerobot.utils.constants import ACTION, OBS_IMAGES, OBS_STATE
+
+DEFAULT_IMAGE_SIZE = 224
+
+
+@PreTrainedConfig.register_subclass("pi05_full")
+@dataclass
+class PI05FullConfig(PreTrainedConfig):
+    paligemma_variant: str = "gemma_2b"
+    action_expert_variant: str = "gemma_300m"
+    dtype: str = "float32"  # Options: "bfloat16", "float32"
+
+    n_obs_steps: int = 1
+    chunk_size: int = 50  # Number of action steps to predict, in openpi called "action_horizon"
+    n_action_steps: int = 50  # Number of action steps to execute
+
+    # Shorter state and action vectors will be padded to these dimensions
+    max_state_dim: int = 32
+    max_action_dim: int = 32
+
+    # Flow matching parameters: see openpi `PI0Pytorch`
+    num_inference_steps: int = 10
+    time_sampling_beta_alpha: float = 1.5
+    time_sampling_beta_beta: float = 1.0
+    time_sampling_scale: float = 0.999
+    time_sampling_offset: float = 0.001
+    min_period: float = 4e-3
+    max_period: float = 4.0
+
+    # Real-Time Chunking (RTC) configuration
+    rtc_config: RTCConfig | None = None
+
+    image_resolution: tuple[int, int] = (
+        DEFAULT_IMAGE_SIZE,
+        DEFAULT_IMAGE_SIZE,
+    )  # see openpi `preprocessing_pytorch.py`
+
+    # Add empty images. Used to add empty cameras when no image features are present.
+    empty_cameras: int = 0
+
+    normalization_mapping: dict[str, NormalizationMode] = field(
+        default_factory=lambda: {
+            "VISUAL": NormalizationMode.IDENTITY,
+            "STATE": NormalizationMode.MEAN_STD,  # Pi0.5 uses quantiles for state
+            "ACTION": NormalizationMode.MEAN_STD,  # Pi0.5 uses quantiles for action
+        }
+    )
+
+    action_tokenizer_name: str = "physical-intelligence/fast"
+    text_tokenizer_name: str = "google/paligemma-3b-pt-224"
+    max_action_tokens: int = 256
+    fast_skip_tokens: int = 128
+
+    # subtask stuff
+    max_decoding_steps: int = 200
+    temperature: float = 0.0
+    subtask_regeneration_interval: float = 1.0  # Regenerate subtask tokens every N seconds (0 = every call)
+
+    # Training settings
+    gradient_checkpointing: bool = False  # Enable gradient checkpointing for memory optimization
+    compile_model: bool = False  # Whether to use torch.compile for model optimization
+    compile_mode: str = "max-autotune"  # Torch compile mode
+    device: str | None = None  # Device to use for the model (None = auto-detect)
+
+    # Finetuning settings
+    freeze_vision_encoder: bool = False  # Freeze only the vision encoder
+    train_expert_only: bool = False  # Freeze entire VLM, train only action expert and projections
+    knowledge_insulation: bool = True  # Enable knowledge insulation in attention (blocks gradients from action to VLM K/V)
+
+    # Loss weights (used when knowledge_insulation is enabled)
+    loss_weight_flow: float = 1.0  # Weight for flow matching MSE loss (continuous actions)
+    loss_weight_action_ce: float = 1.0  # Weight for FAST action token cross-entropy loss
+    loss_weight_subtask_ce: float = 1.0  # Weight for subtask token cross-entropy loss
+
+    # Optimizer settings: see openpi `AdamW`
+    optimizer_lr: float = 2.5e-5  # see openpi `CosineDecaySchedule: peak_lr`
+    optimizer_betas: tuple[float, float] = (0.9, 0.95)
+    optimizer_eps: float = 1e-8
+    optimizer_weight_decay: float = 0.01
+    optimizer_grad_clip_norm: float = 1.0
+
+    # Scheduler settings: see openpi `CosineDecaySchedule`
+    # Note: These will auto-scale if --steps < scheduler_decay_steps
+    # For example, --steps=3000 will scale warmup to 100 and decay to 3000
+    scheduler_warmup_steps: int = 1_000
+    scheduler_decay_steps: int = 30_000
+    scheduler_decay_lr: float = 2.5e-6
+
+    tokenizer_max_length: int = 48  # see openpi `__post_init__`
+
+    def __post_init__(self):
+        super().__post_init__()
+
+        # Validate configuration
+        if self.n_action_steps > self.chunk_size:
+            raise ValueError(
+                f"n_action_steps ({self.n_action_steps}) cannot be greater than chunk_size ({self.chunk_size})"
+            )
+
+        if self.paligemma_variant not in ["gemma_300m", "gemma_2b"]:
+            raise ValueError(f"Invalid paligemma_variant: {self.paligemma_variant}")
+
+        if self.action_expert_variant not in ["gemma_300m", "gemma_2b"]:
+            raise ValueError(f"Invalid action_expert_variant: {self.action_expert_variant}")
+
+        if self.dtype not in ["bfloat16", "float32"]:
+            raise ValueError(f"Invalid dtype: {self.dtype}")
+
+    def validate_features(self) -> None:
+        """Validate and set up input/output features."""
+        for i in range(self.empty_cameras):
+            key = OBS_IMAGES + f".empty_camera_{i}"
+            empty_camera = PolicyFeature(
+                type=FeatureType.VISUAL,
+                shape=(3, *self.image_resolution),  # Use configured image resolution
+            )
+            self.input_features[key] = empty_camera
+
+        if OBS_STATE not in self.input_features:
+            state_feature = PolicyFeature(
+                type=FeatureType.STATE,
+                shape=(self.max_state_dim,),  # Padded to max_state_dim
+            )
+            self.input_features[OBS_STATE] = state_feature
+
+        if ACTION not in self.output_features:
+            action_feature = PolicyFeature(
+                type=FeatureType.ACTION,
+                shape=(self.max_action_dim,),  # Padded to max_action_dim
+            )
+            self.output_features[ACTION] = action_feature
+
+    def get_optimizer_preset(self) -> AdamWConfig:
+        return AdamWConfig(
+            lr=self.optimizer_lr,
+            betas=self.optimizer_betas,
+            eps=self.optimizer_eps,
+            weight_decay=self.optimizer_weight_decay,
+            grad_clip_norm=self.optimizer_grad_clip_norm,
+        )
+
+    def get_scheduler_preset(self):
+        return CosineDecayWithWarmupSchedulerConfig(
+            peak_lr=self.optimizer_lr,
+            decay_lr=self.scheduler_decay_lr,
+            num_warmup_steps=self.scheduler_warmup_steps,
+            num_decay_steps=self.scheduler_decay_steps,
+        )
+
+    @property
+    def observation_delta_indices(self) -> None:
+        return None
+
+    @property
+    def action_delta_indices(self) -> list:
+        return list(range(self.chunk_size))
+
+    @property
+    def reward_delta_indices(self) -> None:
+        return None

src/lerobot/policies/pi05_full/inference.py

@@ -0,0 +1,92 @@
+import torch
+from huggingface_hub import HfApi
+
+import lerobot
+from lerobot.datasets.lerobot_dataset import LeRobotDataset, LeRobotDatasetMetadata
+# import make_pre_post_processors
+from lerobot.policies.factory import make_pre_post_processors
+from lerobot.policies.pi05.configuration_pi05 import PI05Config
+from lerobot.policies.factory import make_policy, make_policy_config
+from lerobot.configs.policies import PreTrainedConfig
+
+cfg = PreTrainedConfig.from_pretrained(
+    pretrained_name_or_path="/fsx/jade_choghari/models/pi05-base",
+)
+cfg.dtype = "bfloat16"
+
+pre_processor, post_processor = make_pre_post_processors(
+    policy_cfg=cfg,
+    pretrained_path="/fsx/jade_choghari/models/pi05-base",
+)
+
+delta_timestamps = {'action': [0.0, 0.03333333333333333, 0.06666666666666667, 0.1, 0.13333333333333333, 0.16666666666666666, 0.2, 0.23333333333333334, 0.26666666666666666, 0.3, 0.3333333333333333, 0.36666666666666664, 0.4, 0.43333333333333335, 0.4666666666666667, 0.5, 0.5333333333333333, 0.5666666666666667, 0.6, 0.6333333333333333, 0.6666666666666666, 0.7, 0.7333333333333333, 0.7666666666666667, 0.8, 0.8333333333333334, 0.8666666666666667, 0.9, 0.9333333333333333, 0.9666666666666667, 1.0, 1.0333333333333334, 1.0666666666666667, 1.1, 1.1333333333333333, 1.1666666666666667, 1.2, 1.2333333333333334, 1.2666666666666666, 1.3, 1.3333333333333333, 1.3666666666666667, 1.4, 1.4333333333333333, 1.4666666666666666, 1.5, 1.5333333333333334, 1.5666666666666667, 1.6, 1.6333333333333333]}
+
+dataset = LeRobotDataset(repo_id="local", root="/fsx/jade_choghari/outputs/pgen_annotations1", delta_timestamps=delta_timestamps)
+
+# rename map --rename_map='{
+#         "observation.images.side": "observation.images.base_0_rgb",
+#         "observation.images.up": "observation.images.left_wrist_0_rgb"
+#         }'
+rename_map = {
+    "observation.images.side": "observation.images.base_0_rgb",
+    "observation.images.up": "observation.images.left_wrist_0_rgb"
+}
+policy = make_policy(
+    cfg=cfg,
+    ds_meta=dataset.meta,
+    rename_map=rename_map,
+)
+
+dataloader = torch.utils.data.DataLoader(
+        dataset,
+        num_workers=0,
+        batch_size=4,
+        shuffle=True,
+)
+
+batch = next(iter(dataloader))
+breakpoint()
+batch = pre_processor(batch)
+policy.train()
+# run inference
+# action = policy.select_action(batch)
+loss, loss_dict = policy.forward(batch)
+breakpoint()
+# import requests
+# from PIL import Image
+# from transformers import AutoProcessor
+# model = policy.model.paligemma_with_expert.paligemma
+# model = model.to(device="cuda", dtype=torch.bfloat16)
+# model.eval()
+# prompt = "Describe this image."
+# url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"
+# image = Image.open(requests.get(url, stream=True).raw)
+# processor = AutoProcessor.from_pretrained(
+#     "google/paligemma-3b-pt-224",
+# )
+# inputs = processor(image, prompt, return_tensors="pt").to(model.device)
+# print("generating...")
+# output = model.generate(
+#     **inputs,
+#     max_new_tokens=50,
+#     use_cache=True,  # default dynamic cache
+# )
+# print(processor.decode(output[0], skip_special_tokens=True))
+
+
+# # other model
+# from transformers import PaliGemmaForConditionalGeneration
+# model = PaliGemmaForConditionalGeneration.from_pretrained(
+#     "google/paligemma2-3b-pt-224",
+#     torch_dtype=torch.bfloat16,
+#     device_map="auto",
+# )
+# model.eval()
+# print("generating...")
+# output = model.generate(
+#     **inputs,
+#     max_new_tokens=100,
+#     use_cache=True,  # default dynamic cache
+# )
+# print("Model 2 output:")
+# print(processor.decode(output[0], skip_special_tokens=True))

src/lerobot/policies/pi05_full/processor_pi05.py

@@ -0,0 +1,194 @@
+#!/usr/bin/env python
+
+# Copyright 2025 Physical Intelligence and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from copy import deepcopy
+from dataclasses import dataclass
+from typing import Any
+
+import numpy as np
+import torch
+
+from lerobot.configs.types import PipelineFeatureType, PolicyFeature
+from lerobot.policies.pi05_full.configuration_pi05 import PI05FullConfig
+from lerobot.policies.pi05_full.modeling_pi05 import pad_vector
+from lerobot.processor import (
+    ActionTokenizerProcessorStep,
+    AddBatchDimensionProcessorStep,
+    DeviceProcessorStep,
+    NormalizerProcessorStep,
+    PolicyAction,
+    PolicyProcessorPipeline,
+    ProcessorStep,
+    ProcessorStepRegistry,
+    RenameObservationsProcessorStep,
+    TokenizerProcessorStep,
+    UnnormalizerProcessorStep,
+)
+from lerobot.processor.converters import policy_action_to_transition, transition_to_policy_action
+from lerobot.processor.core import EnvTransition, TransitionKey
+from lerobot.utils.constants import (
+    OBS_STATE,
+    POLICY_POSTPROCESSOR_DEFAULT_NAME,
+    POLICY_PREPROCESSOR_DEFAULT_NAME,
+)
+
+
+@ProcessorStepRegistry.register(name="pi05_full_prepare_state_tokenizer_processor_step")
+@dataclass
+class Pi05FullPrepareStateTokenizerProcessorStep(ProcessorStep):
+    """
+    Processor step to prepare the state and tokenize the language input.
+    """
+
+    max_state_dim: int = 32
+    task_key: str = "task"
+    subtask_key: str = "subtask"
+
+    def __call__(self, transition: EnvTransition) -> EnvTransition:
+        transition = transition.copy()
+
+        state = transition.get(TransitionKey.OBSERVATION, {}).get(OBS_STATE)
+        if state is None:
+            raise ValueError("State is required for PI05")
+        user_prompts = transition.get(TransitionKey.COMPLEMENTARY_DATA, {}).get(self.task_key)
+        if user_prompts is None:
+            raise ValueError("No user prompts found in complementary data")
+        commands = transition.get(TransitionKey.COMPLEMENTARY_DATA, {}).get(self.subtask_key)
+
+        # TODO: check if this necessary
+        state = deepcopy(state)
+
+        # Prepare state (pad to max_state_dim)
+        state = pad_vector(state, self.max_state_dim)
+
+        # State should already be normalized to [-1, 1] by the NormalizerProcessorStep that runs before this step
+        # Discretize into 256 bins (see openpi `PaligemmaTokenizer.tokenize()`)
+        state_np = state.cpu().numpy()
+        discretized_states = np.digitize(state_np, bins=np.linspace(-1, 1, 256 + 1)[:-1]) - 1
+
+        full_prompts = []
+        for i, user_prompt in enumerate(user_prompts):
+            cleaned_text = user_prompt.strip().replace("_", " ").replace("\n", " ")
+            cleaned_text = cleaned_text.lower()   # all lowercase # NOTE: added by (jadechoghari)
+            state_str = " ".join(map(str, discretized_states[i]))
+            full_prompt = f"Task: {cleaned_text}, State: {state_str};\n"
+            full_prompts.append(full_prompt)
+        
+        transition[TransitionKey.COMPLEMENTARY_DATA][self.task_key] = full_prompts
+
+        # process commands (optional)
+        if commands is not None:
+            full_commands = []
+            for i, command in enumerate(commands):
+                cleaned_text = command.strip().replace("_", " ").replace("\n", " ")
+                cleaned_text = cleaned_text.lower()   # all lowercase # NOTE: added by (jadechoghari)
+                full_command = f"Subtask: {cleaned_text};\n"
+                full_commands.append(full_command)
+
+            transition[TransitionKey.COMPLEMENTARY_DATA][self.subtask_key] = full_commands
+
+        # note: action tokens will be processed in the ActionTokenizerProcessorStep
+        # Normalize state to [-1, 1] range if needed (assuming it's already normalized by normalizer processor step!!)
+        # Discretize into 256 bins (see openpi `PaligemmaTokenizer.tokenize()`)
+        return transition
+
+    def transform_features(
+        self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]]
+    ) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]:
+        """
+        This step does not alter the feature definitions.
+        """
+        return features
+
+
+def make_pi05_full_pre_post_processors(
+    config: PI05FullConfig,
+    dataset_stats: dict[str, dict[str, torch.Tensor]] | None = None,
+) -> tuple[
+    PolicyProcessorPipeline[dict[str, Any], dict[str, Any]],
+    PolicyProcessorPipeline[PolicyAction, PolicyAction],
+]:
+    """
+    Constructs pre-processor and post-processor pipelines for the PI0 policy.
+
+    The pre-processing pipeline prepares input data for the model by:
+    1. Renaming features to match pretrained configurations.
+    2. Normalizing input and output features based on dataset statistics.
+    3. Adding a batch dimension.
+    4. Appending a newline character to the task description for tokenizer compatibility.
+    5. Tokenizing the text prompt using the PaliGemma tokenizer.
+    6. Moving all data to the specified device.
+
+    The post-processing pipeline handles the model's output by:
+    1. Moving data to the CPU.
+    2. Unnormalizing the output features to their original scale.
+
+    Args:
+        config: The configuration object for the PI0 policy.
+        dataset_stats: A dictionary of statistics for normalization.
+        preprocessor_kwargs: Additional arguments for the pre-processor pipeline.
+        postprocessor_kwargs: Additional arguments for the post-processor pipeline.
+
+    Returns:
+        A tuple containing the configured pre-processor and post-processor pipelines.
+    """
+
+    # Add remaining processors
+    input_steps: list[ProcessorStep] = [
+        RenameObservationsProcessorStep(rename_map={}),  # To mimic the same processor as pretrained one
+        AddBatchDimensionProcessorStep(),
+        # NOTE: NormalizerProcessorStep MUST come before Pi05PrepareStateTokenizerProcessorStep
+        # because the tokenizer step expects normalized state in [-1, 1] range for discretization
+        NormalizerProcessorStep(
+            features={**config.input_features, **config.output_features},
+            norm_map=config.normalization_mapping,
+            stats=dataset_stats,
+        ),
+        Pi05FullPrepareStateTokenizerProcessorStep(max_state_dim=config.max_state_dim),
+        TokenizerProcessorStep(
+            tokenizer_name=config.text_tokenizer_name,
+            max_length=config.tokenizer_max_length,
+            padding_side="right",
+            padding="max_length",
+        ),
+        ActionTokenizerProcessorStep(
+            action_tokenizer_name=config.action_tokenizer_name,
+            max_action_tokens=config.max_action_tokens,
+            fast_skip_tokens=config.fast_skip_tokens,
+            paligemma_tokenizer_name=config.text_tokenizer_name,
+        ),
+        DeviceProcessorStep(device=config.device),
+    ]
+
+    output_steps: list[ProcessorStep] = [
+        UnnormalizerProcessorStep(
+            features=config.output_features, norm_map=config.normalization_mapping, stats=dataset_stats
+        ),
+        DeviceProcessorStep(device="cpu"),
+    ]
+
+    return (
+        PolicyProcessorPipeline[dict[str, Any], dict[str, Any]](
+            steps=input_steps,
+            name=POLICY_PREPROCESSOR_DEFAULT_NAME,
+        ),
+        PolicyProcessorPipeline[PolicyAction, PolicyAction](
+            steps=output_steps,
+            name=POLICY_POSTPROCESSOR_DEFAULT_NAME,
+            to_transition=policy_action_to_transition,
+            to_output=transition_to_policy_action,
+        ),
+    )

src/lerobot/policies/pi0_fast/configuration_pi0_fast.py

@@ -41,9 +41,6 @@ class PI0FastConfig(PreTrainedConfig):
     max_action_dim: int = 32
     max_action_tokens: int = 256
 
-    # Delta actions: converts absolute actions to delta (relative to state).
-    use_delta_actions: bool = False
-
     # Real-Time Chunking (RTC) configuration
     rtc_config: RTCConfig | None = None
 

src/lerobot/policies/pi0_fast/modeling_pi0_fast.py

@@ -48,14 +48,12 @@ from lerobot.configs.policies import PreTrainedConfig
 from lerobot.policies.pi0_fast.configuration_pi0_fast import PI0FastConfig
 from lerobot.policies.pretrained import PreTrainedPolicy, T
 from lerobot.policies.rtc.modeling_rtc import RTCProcessor
-from lerobot.processor.delta_action_processor import to_absolute_actions
 from lerobot.utils.constants import (
     ACTION,
     ACTION_TOKEN_MASK,
     ACTION_TOKENS,
     OBS_LANGUAGE_ATTENTION_MASK,
     OBS_LANGUAGE_TOKENS,
-    OBS_STATE,
     OPENPI_ATTENTION_MASK_VALUE,
 )
 
@@ -1317,12 +1315,6 @@ class PI0FastPolicy(PreTrainedPolicy):
             action_tokens, action_horizon=action_horizon, action_dim=action_dim
         )
 
-        if self.config.use_delta_actions and OBS_STATE in batch:
-            state = pad_vector(batch[OBS_STATE], self.config.max_state_dim)
-            continuous_actions = to_absolute_actions(
-                continuous_actions, state, [True] * continuous_actions.shape[-1]
-        )
-
         return continuous_actions
 
     def forward(self, batch: dict[str, Tensor]) -> tuple[Tensor, dict]:

src/lerobot/policies/pi0_fast/processor_pi0_fast.py

@@ -27,7 +27,6 @@ from lerobot.policies.pi0_fast.modeling_pi0_fast import pad_vector
 from lerobot.processor import (
     ActionTokenizerProcessorStep,
     AddBatchDimensionProcessorStep,
-    DeltaActionsProcessorStep,
     DeviceProcessorStep,
     NormalizerProcessorStep,
     PolicyAction,
@@ -148,7 +147,6 @@ def make_pi0_fast_pre_post_processors(
             padding_side="right",
             padding="max_length",
         ),
-        DeltaActionsProcessorStep(enabled=config.use_delta_actions),
         ActionTokenizerProcessorStep(
             action_tokenizer_name=config.action_tokenizer_name,
             max_action_tokens=config.max_action_tokens,

src/lerobot/policies/smolvla/modeling_smolvla.py

@@ -378,16 +378,16 @@ class SmolVLAPolicy(PreTrainedPolicy):
         actions_is_pad = batch.get("actions_id_pad")
         loss_dict = {}
         losses = self.model.forward(images, img_masks, lang_tokens, lang_masks, state, actions, noise, time)
-        loss_dict["losses_after_forward"] = losses.clone().mean().item()
+        loss_dict["losses_after_forward"] = losses.clone()
 
         if actions_is_pad is not None:
             in_episode_bound = ~actions_is_pad
             losses = losses * in_episode_bound.unsqueeze(-1)
-            loss_dict["losses_after_in_ep_bound"] = losses.clone().mean().item()
+            loss_dict["losses_after_in_ep_bound"] = losses.clone()
 
         # Remove padding
         losses = losses[:, :, : self.config.max_action_dim]
-        loss_dict["losses_after_rm_padding"] = losses.clone().mean().item()
+        loss_dict["losses_after_rm_padding"] = losses.clone()
 
         if reduction == "none":
             # Return per-sample losses (B,) by averaging over time and action dims

src/lerobot/processor/__init__.py

@@ -28,14 +28,7 @@ from .core import (
     RobotObservation,
     TransitionKey,
 )
-from .delta_action_processor import (
-    AbsoluteActionsProcessorStep,
-    DeltaActionsProcessorStep,
-    MapDeltaActionToRobotActionStep,
-    MapTensorToDeltaActionDictStep,
-    to_absolute_actions,
-    to_delta_actions,
-)
+from .delta_action_processor import MapDeltaActionToRobotActionStep, MapTensorToDeltaActionDictStep
 from .device_processor import DeviceProcessorStep
 from .factory import (
     make_default_processors,
@@ -104,8 +97,6 @@ __all__ = [
     "make_default_teleop_action_processor",
     "make_default_robot_action_processor",
     "make_default_robot_observation_processor",
-    "AbsoluteActionsProcessorStep",
-    "DeltaActionsProcessorStep",
     "MapDeltaActionToRobotActionStep",
     "MapTensorToDeltaActionDictStep",
     "NormalizerProcessorStep",
@@ -135,8 +126,6 @@ __all__ = [
     "transition_to_batch",
     "TransitionKey",
     "TruncatedProcessorStep",
-    "to_absolute_actions",
-    "to_delta_actions",
     "UnnormalizerProcessorStep",
     "VanillaObservationProcessorStep",
 ]

src/lerobot/processor/converters.py

@@ -171,9 +171,11 @@ def _extract_complementary_data(batch: dict[str, Any]) -> dict[str, Any]:
     subtask_key = {"subtask": batch["subtask"]} if "subtask" in batch else {}
     index_key = {"index": batch["index"]} if "index" in batch else {}
     task_index_key = {"task_index": batch["task_index"]} if "task_index" in batch else {}
+    user_prompt_key = {"user_prompt": batch["user_prompt"]} if "user_prompt" in batch else {}
+    subtask_key = {"subtask": batch["subtask"]} if "subtask" in batch else {}
     episode_index_key = {"episode_index": batch["episode_index"]} if "episode_index" in batch else {}
 
-    return {**pad_keys, **task_key, **subtask_key, **index_key, **task_index_key, **episode_index_key}
+    return {**pad_keys, **task_key, **index_key, **task_index_key, **episode_index_key, **user_prompt_key, **subtask_key}
 
 
 def create_transition(

src/lerobot/processor/delta_action_processor.py

@@ -14,54 +14,12 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from collections.abc import Sequence
-from dataclasses import dataclass, field
-from typing import Any
-
-import torch
-from torch import Tensor
+from dataclasses import dataclass
 
 from lerobot.configs.types import FeatureType, PipelineFeatureType, PolicyFeature
-from lerobot.utils.constants import OBS_STATE
 
-from .core import EnvTransition, PolicyAction, RobotAction, TransitionKey
-from .pipeline import ActionProcessorStep, ProcessorStep, ProcessorStepRegistry, RobotActionProcessorStep
-
-
-def to_delta_actions(actions: Tensor, state: Tensor, mask: Sequence[bool]) -> Tensor:
-    """Convert absolute actions to delta: delta = action - state (for masked dims).
-
-    Args:
-        actions: (B, T, action_dim) or (B, action_dim).
-        state: (B, state_dim). Broadcast across time dimension.
-        mask: Which dims to convert. Can be shorter than action_dim.
-    """
-    mask_t = torch.tensor(mask, dtype=actions.dtype, device=actions.device)
-    dims = mask_t.shape[0]
-    state_offset = state[..., :dims] * mask_t
-    if actions.ndim == 3:
-        state_offset = state_offset.unsqueeze(-2)
-    actions = actions.clone()
-    actions[..., :dims] -= state_offset
-    return actions
-
-
-def to_absolute_actions(actions: Tensor, state: Tensor, mask: Sequence[bool]) -> Tensor:
-    """Convert delta actions back to absolute: absolute = delta + state (for masked dims).
-
-    Args:
-        actions: (B, T, action_dim) or (B, action_dim).
-        state: (B, state_dim). Broadcast across time dimension.
-        mask: Which dims to convert. Can be shorter than action_dim.
-    """
-    mask_t = torch.tensor(mask, dtype=actions.dtype, device=actions.device)
-    dims = mask_t.shape[0]
-    state_offset = state[..., :dims] * mask_t
-    if actions.ndim == 3:
-        state_offset = state_offset.unsqueeze(-2)
-    actions = actions.clone()
-    actions[..., :dims] += state_offset
-    return actions
+from .core import PolicyAction, RobotAction
+from .pipeline import ActionProcessorStep, ProcessorStepRegistry, RobotActionProcessorStep
 
 
 @ProcessorStepRegistry.register("map_tensor_to_delta_action_dict")
@@ -183,126 +141,3 @@ class MapDeltaActionToRobotActionStep(RobotActionProcessorStep):
             )
 
         return features
-
-
-@ProcessorStepRegistry.register("delta_actions_processor")
-@dataclass
-class DeltaActionsProcessorStep(ProcessorStep):
-    """Converts absolute actions to delta actions (action -= state) for masked dimensions.
-
-    Mirrors OpenPI's DeltaActions transform. Applied during preprocessing so the model
-    trains on relative offsets instead of absolute positions.
-    Caches the last seen state so a paired AbsoluteActionsProcessorStep can reverse
-    the conversion during postprocessing.
-
-    Attributes:
-        enabled: Whether to apply the delta conversion.
-        exclude_joints: Joint names to keep absolute (not converted to delta).
-        action_names: Action dimension names from dataset metadata, used to build
-            the mask from exclude_joints. If None, all dims are converted.
-    """
-
-    enabled: bool = False
-    exclude_joints: list[str] = field(default_factory=list)
-    action_names: list[str] | None = None
-    _last_state: torch.Tensor | None = field(default=None, init=False, repr=False)
-
-    def _build_mask(self, action_dim: int) -> list[bool]:
-        if not self.exclude_joints or self.action_names is None:
-            return [True] * action_dim
-
-        exclude_tokens = [str(name).lower() for name in self.exclude_joints if name]
-        if not exclude_tokens:
-            return [True] * action_dim
-
-        mask = []
-        for name in self.action_names[:action_dim]:
-            action_name = str(name).lower()
-            is_excluded = any(token == action_name or token in action_name for token in exclude_tokens)
-            mask.append(not is_excluded)
-
-        if len(mask) < action_dim:
-            mask.extend([True] * (action_dim - len(mask)))
-
-        return mask
-
-    def __call__(self, transition: EnvTransition) -> EnvTransition:
-        observation = transition.get(TransitionKey.OBSERVATION, {})
-        state = observation.get(OBS_STATE) if observation else None
-
-        # Always cache state for the paired AbsoluteActionsProcessorStep
-        if state is not None:
-            self._last_state = state
-
-        if not self.enabled:
-            return transition
-
-        new_transition = transition.copy()
-        action = new_transition.get(TransitionKey.ACTION)
-        if action is None or state is None:
-            return new_transition
-
-        mask = self._build_mask(action.shape[-1])
-        new_transition[TransitionKey.ACTION] = to_delta_actions(action, state, mask)
-        return new_transition
-
-    def get_config(self) -> dict[str, Any]:
-        return {"enabled": self.enabled, "exclude_joints": self.exclude_joints}
-
-    def transform_features(
-        self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]]
-    ) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]:
-        return features
-
-
-@ProcessorStepRegistry.register("absolute_actions_processor")
-@dataclass
-class AbsoluteActionsProcessorStep(ProcessorStep):
-    """Converts delta actions back to absolute actions (action += state) for all dimensions.
-
-    Mirrors OpenPI's AbsoluteActions transform. Applied during postprocessing so
-    predicted deltas are converted back to absolute positions for execution.
-    Reads the cached state from its paired DeltaActionsProcessorStep.
-
-    Attributes:
-        enabled: Whether to apply the absolute conversion.
-        delta_step: Reference to the paired DeltaActionsProcessorStep that caches state.
-    """
-
-    enabled: bool = False
-    delta_step: DeltaActionsProcessorStep | None = field(default=None, repr=False)
-
-    def __call__(self, transition: EnvTransition) -> EnvTransition:
-        if not self.enabled:
-            return transition
-
-        if self.delta_step is None:
-            raise RuntimeError(
-                "AbsoluteActionsProcessorStep requires a paired DeltaActionsProcessorStep "
-                "but delta_step is None. Ensure delta_step is set when constructing the postprocessor."
-            )
-
-        if self.delta_step._last_state is None:
-            raise RuntimeError(
-                "AbsoluteActionsProcessorStep requires state from DeltaActionsProcessorStep "
-                "but no state has been cached. Ensure the preprocessor runs before the postprocessor."
-            )
-
-        new_transition = transition.copy()
-        action = new_transition.get(TransitionKey.ACTION)
-        if action is None:
-            return new_transition
-
-        mask = self.delta_step._build_mask(action.shape[-1])
-        new_transition[TransitionKey.ACTION] = to_absolute_actions(
-            action, self.delta_step._last_state, mask
-        )
-        return new_transition
-
-    def get_config(self) -> dict[str, Any]:
-        return {"enabled": self.enabled}
-
-    def transform_features(
-        self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]]
-    ) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]:
-        return features

src/lerobot/processor/env_processor.py

@@ -17,7 +17,7 @@ from dataclasses import dataclass
 
 import torch
 
-from lerobot.configs.types import FeatureType, PipelineFeatureType, PolicyFeature
+from lerobot.configs.types import PipelineFeatureType, PolicyFeature
 from lerobot.utils.constants import OBS_IMAGES, OBS_PREFIX, OBS_STATE, OBS_STR
 
 from .pipeline import ObservationProcessorStep, ProcessorStepRegistry
@@ -92,7 +92,7 @@ class LiberoProcessorStep(ObservationProcessorStep):
 
         # copy over non-STATE features
         for ft, feats in features.items():
-            if ft != FeatureType.STATE:
+            if ft != PipelineFeatureType.STATE:
                 new_features[ft] = feats.copy()
 
         # rebuild STATE features
@@ -100,11 +100,13 @@ class LiberoProcessorStep(ObservationProcessorStep):
 
         # add our new flattened state
         state_feats[OBS_STATE] = PolicyFeature(
-            type=FeatureType.STATE,
+            key=OBS_STATE,
             shape=(8,),  # [eef_pos(3), axis_angle(3), gripper(2)]
+            dtype="float32",
+            description=("Concatenated end-effector position (3), axis-angle (3), and gripper qpos (2)."),
         )
 
-        new_features[FeatureType.STATE] = state_feats
+        new_features[PipelineFeatureType.STATE] = state_feats
 
         return new_features
 

src/lerobot/processor/normalize_processor.py

@@ -331,9 +331,11 @@ class _NormalizationMixin:
                 )
 
             mean, std = stats["mean"], stats["std"]
+            # Avoid division by zero by adding a small epsilon.
+            denom = std + self.eps
             if inverse:
-                return tensor * (std + 1e-6) + mean
-            return (tensor - mean) / (std + 1e-6)
+                return tensor * std + mean
+            return (tensor - mean) / denom
 
         if norm_mode == NormalizationMode.MIN_MAX:
             min_val = stats.get("min", None)
@@ -365,7 +367,11 @@ class _NormalizationMixin:
                     "QUANTILES normalization mode requires q01 and q99 stats, please update the dataset with the correct stats using the `augment_dataset_quantile_stats.py` script"
                 )
 
-            denom = q99 - q01 + 1e-6
+            denom = q99 - q01
+            # Avoid division by zero by adding epsilon when quantiles are identical
+            denom = torch.where(
+                denom == 0, torch.tensor(self.eps, device=tensor.device, dtype=tensor.dtype), denom
+            )
             if inverse:
                 return (tensor + 1.0) * denom / 2.0 + q01
             return 2.0 * (tensor - q01) / denom - 1.0

src/lerobot/processor/pipeline.py

@@ -413,7 +413,7 @@ class DataProcessorPipeline(HubMixin, Generic[TInput, TOutput]):
         Args:
             save_directory: The directory where the pipeline will be saved. If None, saves to
                 HF_LEROBOT_HOME/processors/{sanitized_pipeline_name}.
-            repo_id: ID of your repository on the Hub. Used only if `push_to_hub=true`.
+            repo_id: ID of your repository on the Hub. Used only if `push_to_hub=True`.
             push_to_hub: Whether or not to push your object to the Hugging Face Hub after saving it.
             card_kwargs: Additional arguments passed to the card template to customize the card.
             config_filename: The name of the JSON configuration file. If None, a name is

src/lerobot/processor/tokenizer_processor.py

@@ -37,6 +37,9 @@ from lerobot.utils.constants import (
     OBS_LANGUAGE_SUBTASK_ATTENTION_MASK,
     OBS_LANGUAGE_SUBTASK_TOKENS,
     OBS_LANGUAGE_TOKENS,
+    OBS_LANGUAGE_USER_PROMPT,
+    OBS_LANGUAGE_USER_PROMPT_ATTENTION_MASK,
+    OBS_LANGUAGE_USER_PROMPT_TOKENS,
 )
 from lerobot.utils.import_utils import _transformers_available
 
@@ -141,6 +144,32 @@ class TokenizerProcessorStep(ObservationProcessorStep):
 
         return None
 
+    def get_user_prompt(self, transition: EnvTransition) -> list[str] | None:
+        """
+        Extracts the user_prompt from the transition's complementary data.
+
+        Args:
+            transition: The environment transition.
+
+        Returns:
+            A list of user_prompt strings, or None if the user_prompt key is not found or the value is None.
+        """
+        complementary_data = transition.get(TransitionKey.COMPLEMENTARY_DATA)
+        if complementary_data is None:
+            return None
+
+        user_prompt = complementary_data.get("user_prompt")
+        if user_prompt is None:
+            return None
+
+        # Standardize to a list of strings for the tokenizer
+        if isinstance(user_prompt, str):
+            return [user_prompt]
+        elif isinstance(user_prompt, list) and all(isinstance(t, str) for t in user_prompt):
+            return user_prompt
+
+        return None
+    
     def get_subtask(self, transition: EnvTransition) -> list[str] | None:
         """
         Extracts the subtask from the transition's complementary data.
@@ -169,16 +198,16 @@ class TokenizerProcessorStep(ObservationProcessorStep):
 
     def observation(self, observation: RobotObservation) -> RobotObservation:
         """
-        Tokenizes the task description and adds it to the observation dictionary.
+        Tokenizes the task description and user_prompt (if available) and adds them to the observation dictionary.
 
-        This method retrieves the task, tokenizes it, moves the resulting tensors to the
+        This method retrieves the task and user_prompt, tokenizes them, moves the resulting tensors to the
         same device as other data in the transition, and updates the observation.
 
         Args:
             observation: The original observation dictionary.
 
         Returns:
-            The updated observation dictionary including token IDs and an attention mask.
+            The updated observation dictionary including token IDs and attention masks.
         """
         task = self.get_task(self.transition)
         if task is None:
@@ -204,11 +233,45 @@ class TokenizerProcessorStep(ObservationProcessorStep):
         new_observation[OBS_LANGUAGE_TOKENS] = tokenized_prompt["input_ids"]
         new_observation[OBS_LANGUAGE_ATTENTION_MASK] = tokenized_prompt["attention_mask"].to(dtype=torch.bool)
 
+        # Tokenize user_prompt if available
+        user_prompt = self.get_user_prompt(self.transition)
+        if user_prompt is not None:
+            tokenized_user_prompt = self._tokenize_text(user_prompt)
+
+            # Move new tokenized tensors to the detected device
+            if target_device is not None:
+                tokenized_user_prompt = {
+                    k: v.to(target_device) if isinstance(v, torch.Tensor) else v
+                    for k, v in tokenized_user_prompt.items()
+                }
+
+            # Add tokenized user_prompt to the observation
+            new_observation[OBS_LANGUAGE_USER_PROMPT_TOKENS] = tokenized_user_prompt["input_ids"]
+            new_observation[OBS_LANGUAGE_USER_PROMPT_ATTENTION_MASK] = tokenized_user_prompt["attention_mask"].to(dtype=torch.bool)
+
         # Tokenize subtask if available
         subtask = self.get_subtask(self.transition)
         if subtask is not None:
             tokenized_subtask = self._tokenize_text(subtask)
 
+            # Add EOS token at the end of each subtask sequence (before padding)
+            eos_token_id = self.input_tokenizer.eos_token_id
+            input_ids = tokenized_subtask["input_ids"]
+            attention_mask = tokenized_subtask["attention_mask"]
+            for i in range(input_ids.size(0)):
+                # Find the length of actual tokens (sum of attention mask)
+                seq_len = attention_mask[i].sum().item()
+
+                max_len = input_ids.size(1)
+                if seq_len >= max_len:
+                    raise ValueError(
+                        f"No room to append EOS: seq_len={seq_len} equals max_length={max_len}. "
+                        "Increase max_length or tokenize with padding=False then pad after adding EOS."
+                    )
+                # Add EOS token at the end
+                input_ids[i, seq_len] = eos_token_id
+                attention_mask[i, seq_len] = 1
+
             # Move new tokenized tensors to the detected device
             if target_device is not None:
                 tokenized_subtask = {
@@ -320,6 +383,28 @@ class TokenizerProcessorStep(ObservationProcessorStep):
                 type=FeatureType.LANGUAGE, shape=(self.max_length,)
             )
 
+        # Add features for user_prompt tokens and attention mask if they don't already exist
+        if OBS_LANGUAGE_USER_PROMPT_TOKENS not in features[PipelineFeatureType.OBSERVATION]:
+            features[PipelineFeatureType.OBSERVATION][OBS_LANGUAGE_USER_PROMPT_TOKENS] = PolicyFeature(
+                type=FeatureType.LANGUAGE, shape=(self.max_length,)
+            )
+
+        if OBS_LANGUAGE_USER_PROMPT_ATTENTION_MASK not in features[PipelineFeatureType.OBSERVATION]:
+            features[PipelineFeatureType.OBSERVATION][OBS_LANGUAGE_USER_PROMPT_ATTENTION_MASK] = PolicyFeature(
+                type=FeatureType.LANGUAGE, shape=(self.max_length,)
+            )
+
+        # Add features for subtask tokens and attention mask if they don't already exist
+        if OBS_LANGUAGE_SUBTASK_TOKENS not in features[PipelineFeatureType.OBSERVATION]:
+            features[PipelineFeatureType.OBSERVATION][OBS_LANGUAGE_SUBTASK_TOKENS] = PolicyFeature(
+                type=FeatureType.LANGUAGE, shape=(self.max_length,)
+            )
+
+        if OBS_LANGUAGE_SUBTASK_ATTENTION_MASK not in features[PipelineFeatureType.OBSERVATION]:
+            features[PipelineFeatureType.OBSERVATION][OBS_LANGUAGE_SUBTASK_ATTENTION_MASK] = PolicyFeature(
+                type=FeatureType.LANGUAGE, shape=(self.max_length,)
+            )
+
         return features
 
 
@@ -573,4 +658,4 @@ class ActionTokenizerProcessorStep(ActionProcessorStep):
         Returns:
             The updated dictionary of policy features.
         """
-        return features
+        return features

src/lerobot/robots/bi_openarm_follower/bi_openarm_follower.py

@@ -19,7 +19,6 @@ from functools import cached_property
 
 from lerobot.processor import RobotAction, RobotObservation
 from lerobot.robots.openarm_follower import OpenArmFollower, OpenArmFollowerConfig
-from lerobot.utils.decorators import check_if_already_connected, check_if_not_connected
 
 from ..robot import Robot
 from .config_bi_openarm_follower import BiOpenArmFollowerConfig
@@ -113,7 +112,6 @@ class BiOpenArmFollower(Robot):
     def is_connected(self) -> bool:
         return self.left_arm.is_connected and self.right_arm.is_connected
 
-    @check_if_already_connected
     def connect(self, calibrate: bool = True) -> None:
         self.left_arm.connect(calibrate)
         self.right_arm.connect(calibrate)
@@ -135,7 +133,6 @@ class BiOpenArmFollower(Robot):
             "Motor ID configuration is typically done via manufacturer tools for CAN motors."
         )
 
-    @check_if_not_connected
     def get_observation(self) -> RobotObservation:
         obs_dict = {}
 
@@ -149,7 +146,6 @@ class BiOpenArmFollower(Robot):
 
         return obs_dict
 
-    @check_if_not_connected
     def send_action(
         self,
         action: RobotAction,
@@ -174,7 +170,6 @@ class BiOpenArmFollower(Robot):
 
         return {**prefixed_sent_action_left, **prefixed_sent_action_right}
 
-    @check_if_not_connected
     def disconnect(self):
         self.left_arm.disconnect()
         self.right_arm.disconnect()

src/lerobot/robots/bi_so_follower/bi_so_follower.py

@@ -19,7 +19,6 @@ from functools import cached_property
 
 from lerobot.processor import RobotAction, RobotObservation
 from lerobot.robots.so_follower import SOFollower, SOFollowerRobotConfig
-from lerobot.utils.decorators import check_if_already_connected, check_if_not_connected
 
 from ..robot import Robot
 from .config_bi_so_follower import BiSOFollowerConfig
@@ -97,7 +96,6 @@ class BiSOFollower(Robot):
     def is_connected(self) -> bool:
         return self.left_arm.is_connected and self.right_arm.is_connected
 
-    @check_if_already_connected
     def connect(self, calibrate: bool = True) -> None:
         self.left_arm.connect(calibrate)
         self.right_arm.connect(calibrate)
@@ -118,7 +116,6 @@ class BiSOFollower(Robot):
         self.left_arm.setup_motors()
         self.right_arm.setup_motors()
 
-    @check_if_not_connected
     def get_observation(self) -> RobotObservation:
         obs_dict = {}
 
@@ -132,7 +129,6 @@ class BiSOFollower(Robot):
 
         return obs_dict
 
-    @check_if_not_connected
     def send_action(self, action: RobotAction) -> RobotAction:
         # Remove "left_" prefix
         left_action = {
@@ -152,7 +148,6 @@ class BiSOFollower(Robot):
 
         return {**prefixed_sent_action_left, **prefixed_sent_action_right}
 
-    @check_if_not_connected
     def disconnect(self):
         self.left_arm.disconnect()
         self.right_arm.disconnect()

src/lerobot/robots/openarm_follower/openarm_follower.py

@@ -23,7 +23,7 @@ from lerobot.cameras.utils import make_cameras_from_configs
 from lerobot.motors import Motor, MotorCalibration, MotorNormMode
 from lerobot.motors.damiao import DamiaoMotorsBus
 from lerobot.processor import RobotAction, RobotObservation
-from lerobot.utils.decorators import check_if_already_connected, check_if_not_connected
+from lerobot.utils.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError
 
 from ..robot import Robot
 from ..utils import ensure_safe_goal_position
@@ -119,7 +119,6 @@ class OpenArmFollower(Robot):
         """Check if robot is connected."""
         return self.bus.is_connected and all(cam.is_connected for cam in self.cameras.values())
 
-    @check_if_already_connected
     def connect(self, calibrate: bool = True) -> None:
         """
         Connect to the robot and optionally calibrate.
@@ -127,6 +126,8 @@ class OpenArmFollower(Robot):
         We assume that at connection time, the arms are in a safe rest position,
         and torque can be safely disabled to run calibration if needed.
         """
+        if self.is_connected:
+            raise DeviceAlreadyConnectedError(f"{self} already connected")
 
         # Connect to CAN bus
         logger.info(f"Connecting arm on {self.config.port}...")
@@ -218,7 +219,6 @@ class OpenArmFollower(Robot):
             "Motor ID configuration is typically done via manufacturer tools for CAN motors."
         )
 
-    @check_if_not_connected
     def get_observation(self) -> RobotObservation:
         """
         Get current observation from robot including position, velocity, and torque.
@@ -228,6 +228,9 @@ class OpenArmFollower(Robot):
         """
         start = time.perf_counter()
 
+        if not self.is_connected:
+            raise DeviceNotConnectedError(f"{self} is not connected.")
+
         obs_dict: dict[str, Any] = {}
 
         states = self.bus.sync_read_all_states()
@@ -250,7 +253,6 @@ class OpenArmFollower(Robot):
 
         return obs_dict
 
-    @check_if_not_connected
     def send_action(
         self,
         action: RobotAction,
@@ -270,6 +272,8 @@ class OpenArmFollower(Robot):
         Returns:
             The action actually sent (potentially clipped)
         """
+        if not self.is_connected:
+            raise DeviceNotConnectedError(f"{self} is not connected.")
 
         goal_pos = {key.removesuffix(".pos"): val for key, val in action.items() if key.endswith(".pos")}
 
@@ -329,9 +333,10 @@ class OpenArmFollower(Robot):
 
         return {f"{motor}.pos": val for motor, val in goal_pos.items()}
 
-    @check_if_not_connected
     def disconnect(self):
         """Disconnect from robot."""
+        if not self.is_connected:
+            raise DeviceNotConnectedError(f"{self} is not connected.")
 
         # Disconnect CAN bus
         self.bus.disconnect(self.config.disable_torque_on_disconnect)

src/lerobot/robots/so_follower/config_so_follower.py

@@ -40,7 +40,7 @@ class SOFollowerConfig:
     cameras: dict[str, CameraConfig] = field(default_factory=dict)
 
     # Set to `True` for backward compatibility with previous policies/dataset
-    use_degrees: bool = True
+    use_degrees: bool = False
 
 
 @RobotConfig.register_subclass("so101_follower")

src/lerobot/scripts/lerobot_edit_dataset.py

@@ -109,14 +109,11 @@ Using JSON config file:
         --config_path path/to/edit_config.json
 """
 
-import abc
 import logging
 import shutil
 from dataclasses import dataclass
 from pathlib import Path
 
-import draccus
-
 from lerobot.configs import parser
 from lerobot.datasets.dataset_tools import (
     convert_image_to_video_dataset,
@@ -132,46 +129,39 @@ from lerobot.utils.utils import init_logging
 
 
 @dataclass
-class OperationConfig(draccus.ChoiceRegistry, abc.ABC):
-    @property
-    def type(self) -> str:
-        return self.get_choice_name(self.__class__)
-
-
-@OperationConfig.register_subclass("delete_episodes")
-@dataclass
-class DeleteEpisodesConfig(OperationConfig):
+class DeleteEpisodesConfig:
+    type: str = "delete_episodes"
     episode_indices: list[int] | None = None
 
 
-@OperationConfig.register_subclass("split")
 @dataclass
-class SplitConfig(OperationConfig):
+class SplitConfig:
+    type: str = "split"
     splits: dict[str, float | list[int]] | None = None
 
 
-@OperationConfig.register_subclass("merge")
 @dataclass
-class MergeConfig(OperationConfig):
+class MergeConfig:
+    type: str = "merge"
     repo_ids: list[str] | None = None
 
 
-@OperationConfig.register_subclass("remove_feature")
 @dataclass
-class RemoveFeatureConfig(OperationConfig):
+class RemoveFeatureConfig:
+    type: str = "remove_feature"
     feature_names: list[str] | None = None
 
 
-@OperationConfig.register_subclass("modify_tasks")
 @dataclass
-class ModifyTasksConfig(OperationConfig):
+class ModifyTasksConfig:
+    type: str = "modify_tasks"
     new_task: str | None = None
     episode_tasks: dict[str, str] | None = None
 
 
-@OperationConfig.register_subclass("convert_image_to_video")
 @dataclass
-class ConvertImageToVideoConfig(OperationConfig):
+class ConvertImageToVideoConfig:
+    type: str = "convert_image_to_video"
     output_dir: str | None = None
     vcodec: str = "libsvtav1"
     pix_fmt: str = "yuv420p"
@@ -187,7 +177,14 @@ class ConvertImageToVideoConfig(OperationConfig):
 @dataclass
 class EditDatasetConfig:
     repo_id: str
-    operation: OperationConfig
+    operation: (
+        DeleteEpisodesConfig
+        | SplitConfig
+        | MergeConfig
+        | RemoveFeatureConfig
+        | ModifyTasksConfig
+        | ConvertImageToVideoConfig
+    )
     root: str | None = None
     new_repo_id: str | None = None
     push_to_hub: bool = False
@@ -453,8 +450,10 @@ def edit_dataset(cfg: EditDatasetConfig) -> None:
     elif operation_type == "convert_image_to_video":
         handle_convert_image_to_video(cfg)
     else:
-        available = ", ".join(OperationConfig.get_known_choices())
-        raise ValueError(f"Unknown operation: {operation_type}\nAvailable operations: {available}")
+        raise ValueError(
+            f"Unknown operation type: {operation_type}\n"
+            f"Available operations: delete_episodes, split, merge, remove_feature, modify_tasks, convert_image_to_video"
+        )
 
 
 def main() -> None:

src/lerobot/scripts/lerobot_mirror_dataset.py

@@ -1,366 +0,0 @@
-# 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.
-
-"""
-Mirror a bimanual robot dataset by swapping left/right arms and inverting joint values.
-
-This script creates a mirrored version of a dataset where:
-1. Left and right arm observations/actions are swapped
-2. Joint values are inverted according to a mirroring mask
-3. Video frames are horizontally flipped
-
-Example usage:
-```shell
-python -m lerobot.scripts.lerobot_mirror_dataset \
-    --repo_id=pepijn/openarm_bimanual \
-    --output_repo_id=pepijn/openarm_bimanual_mirrored
-```
-"""
-
-import argparse
-import logging
-import os
-import subprocess
-from concurrent.futures import ThreadPoolExecutor, as_completed
-from pathlib import Path
-
-import numpy as np
-import pandas as pd
-from tqdm import tqdm
-
-from lerobot.datasets.lerobot_dataset import LeRobotDataset, LeRobotDatasetMetadata
-from lerobot.datasets.utils import (
-    DATA_DIR,
-    DEFAULT_DATA_PATH,
-    write_info,
-    write_stats,
-    write_tasks,
-)
-from lerobot.utils.constants import HF_LEROBOT_HOME
-
-logger = logging.getLogger(__name__)
-
-OPENARM_MIRRORING_MASK = {
-    "joint_1": -1,  # Pan - invert
-    "joint_2": -1,  # Lift - invert
-    "joint_3": -1,  # Roll - invert
-    "joint_4": 1,   # Elbow - no invert
-    "joint_5": -1,  # W-Roll - invert
-    "joint_6": -1,  # W-Pitch - invert
-    "joint_7": -1,  # W-Yaw - invert
-    "gripper": 1,   # Gripper - no invert
-}
-
-
-def get_mirroring_mask(robot_type: str) -> dict[str, int]:
-    """Get the mirroring mask for a given robot type."""
-    if robot_type in ["bi_openarm_follower", "openarm_follower", "bi_openarms_follower", "openarms_follower"]:
-        return OPENARM_MIRRORING_MASK
-    raise ValueError(f"Unknown robot type: {robot_type}. Add a mirroring mask for this robot.")
-
-
-def swap_left_right_name(name: str) -> str:
-    """Swap 'left' and 'right' in a feature name."""
-    # Use placeholder to avoid double-swap
-    result = name.replace("left_", "LEFT_PLACEHOLDER_")
-    result = result.replace("right_", "left_")
-    result = result.replace("LEFT_PLACEHOLDER_", "right_")
-    return result
-
-
-def mirror_feature_names(names: list[str]) -> tuple[list[str], dict[int, int]]:
-    """Mirror feature names by swapping left/right and return the new names and index mapping."""
-    mirrored_names = [swap_left_right_name(n) for n in names]
-    old_to_new_idx = {}
-    for old_idx, old_name in enumerate(names):
-        new_name = swap_left_right_name(old_name)
-        new_idx = mirrored_names.index(new_name)
-        old_to_new_idx[old_idx] = new_idx
-    return mirrored_names, old_to_new_idx
-
-
-def apply_mirroring_mask(
-    value: float,
-    feature_name: str,
-    mirroring_mask: dict[str, int],
-) -> float:
-    """Apply mirroring mask to a joint value."""
-    name_without_prefix = feature_name.split("_", 1)[1] if "_" in feature_name else feature_name
-    joint_name = name_without_prefix.split(".")[0]
-    if joint_name in mirroring_mask:
-        return value * mirroring_mask[joint_name]
-    return value
-
-
-def mirror_array(
-    array: np.ndarray,
-    names: list[str],
-    mirroring_mask: dict[str, int],
-) -> np.ndarray:
-    """Mirror an array of values (action or state) by swapping left/right and applying mask."""
-    mirrored_names, idx_mapping = mirror_feature_names(names)
-    result = np.zeros_like(array)
-    for old_idx, new_idx in idx_mapping.items():
-        old_name = names[old_idx]
-        new_name = mirrored_names[new_idx]
-        value = array[old_idx]
-        mirrored_value = apply_mirroring_mask(value, new_name, mirroring_mask)
-        result[new_idx] = mirrored_value
-    return result
-
-
-def flip_video_frames(
-    input_path: Path,
-    output_path: Path,
-    fps: float,
-    vcodec: str = "libsvtav1",
-):
-    """Flip video frames horizontally using FFmpeg with same settings as encode_video_frames."""
-    output_path.parent.mkdir(parents=True, exist_ok=True)
-    cmd = [
-        "ffmpeg", "-y", "-i", str(input_path),
-        "-vf", "hflip",
-        "-c:v", vcodec,
-        "-g", "2",
-        "-crf", "30",
-        "-r", str(int(fps)),
-        "-pix_fmt", "yuv420p",
-        "-loglevel", "error",
-    ]
-    if vcodec == "libsvtav1":
-        cmd.extend(["-preset", "12"])
-    cmd.append(str(output_path))
-    result = subprocess.run(cmd, capture_output=True, text=True)
-    if result.returncode != 0:
-        raise RuntimeError(f"FFmpeg failed: {result.stderr}")
-
-
-def mirror_dataset(
-    repo_id: str,
-    output_repo_id: str,
-    root: str | Path | None = None,
-    output_root: str | Path | None = None,
-    mirroring_mask: dict[str, int] | None = None,
-    vcodec: str = "libsvtav1",
-    num_workers: int | None = None,
-) -> LeRobotDataset:
-    """Mirror a bimanual robot dataset."""
-    logger.info(f"Loading dataset: {repo_id}")
-    dataset = LeRobotDataset(repo_id, root=root)
-
-    if mirroring_mask is None:
-        robot_type = dataset.meta.robot_type or "bi_openarms_follower"
-        mirroring_mask = get_mirroring_mask(robot_type)
-        logger.info(f"Using mirroring mask for robot type: {robot_type}")
-
-    output_root = Path(output_root) if output_root else HF_LEROBOT_HOME / output_repo_id
-
-    mirrored_features = {}
-    for key, feat in dataset.meta.features.items():
-        new_key = swap_left_right_name(key)
-        new_feat = feat.copy()
-        if "names" in new_feat and new_feat["names"]:
-            new_feat["names"] = [swap_left_right_name(n) for n in new_feat["names"]]
-        mirrored_features[new_key] = new_feat
-
-    logger.info("Creating mirrored dataset metadata...")
-    new_meta = LeRobotDatasetMetadata.create(
-        repo_id=output_repo_id,
-        fps=dataset.meta.fps,
-        features=mirrored_features,
-        robot_type=dataset.meta.robot_type,
-        root=output_root,
-        use_videos=len(dataset.meta.video_keys) > 0,
-    )
-
-    if dataset.meta.tasks is not None:
-        write_tasks(dataset.meta.tasks, new_meta.root)
-        new_meta.tasks = dataset.meta.tasks.copy()
-
-    _mirror_data(dataset, new_meta, mirroring_mask)
-    _mirror_videos(dataset, new_meta, vcodec, num_workers)
-    _copy_episodes_metadata(dataset, new_meta)
-
-    logger.info(f"Mirrored dataset saved to: {output_root}")
-    return LeRobotDataset(output_repo_id, root=output_root)
-
-
-def _mirror_data(
-    src_dataset: LeRobotDataset,
-    dst_meta: LeRobotDatasetMetadata,
-    mirroring_mask: dict[str, int],
-) -> None:
-    """Mirror parquet data files."""
-    data_dir = src_dataset.root / DATA_DIR
-    parquet_files = sorted(data_dir.glob("*/*.parquet"))
-
-    if not parquet_files:
-        raise ValueError(f"No parquet files found in {data_dir}")
-
-    action_names = src_dataset.meta.features.get("action", {}).get("names", [])
-    state_names = src_dataset.meta.features.get("observation.state", {}).get("names", [])
-
-    for src_path in tqdm(parquet_files, desc="Mirroring data files"):
-        df = pd.read_parquet(src_path).reset_index(drop=True)
-        relative_path = src_path.relative_to(src_dataset.root)
-        chunk_dir = relative_path.parts[1]
-        file_name = relative_path.parts[2]
-        chunk_idx = int(chunk_dir.split("-")[1])
-        file_idx = int(file_name.split("-")[1].split(".")[0])
-
-        if "action" in df.columns and action_names:
-            actions = np.stack(df["action"].values)
-            mirrored_actions = np.array([
-                mirror_array(row, action_names, mirroring_mask) for row in actions
-            ])
-            df["action"] = list(mirrored_actions)
-
-        if "observation.state" in df.columns and state_names:
-            states = np.stack(df["observation.state"].values)
-            mirrored_states = np.array([
-                mirror_array(row, state_names, mirroring_mask) for row in states
-            ])
-            df["observation.state"] = list(mirrored_states)
-
-        dst_path = dst_meta.root / DEFAULT_DATA_PATH.format(chunk_index=chunk_idx, file_index=file_idx)
-        dst_path.parent.mkdir(parents=True, exist_ok=True)
-        df.to_parquet(dst_path, index=False)
-
-
-def _mirror_videos(
-    src_dataset: LeRobotDataset,
-    dst_meta: LeRobotDatasetMetadata,
-    vcodec: str,
-    num_workers: int | None = None,
-) -> None:
-    """Mirror video files by flipping horizontally and swapping left/right names."""
-    if not src_dataset.meta.video_keys:
-        return
-
-    video_tasks = []
-    for old_video_key in src_dataset.meta.video_keys:
-        new_video_key = swap_left_right_name(old_video_key)
-        for ep_idx in range(src_dataset.meta.total_episodes):
-            try:
-                src_path = src_dataset.root / src_dataset.meta.get_video_file_path(ep_idx, old_video_key)
-                dst_relative = src_dataset.meta.get_video_file_path(ep_idx, old_video_key)
-                dst_relative_str = str(dst_relative).replace(old_video_key, new_video_key)
-                dst_path = dst_meta.root / dst_relative_str
-                if src_path.exists():
-                    video_tasks.append((src_path, dst_path))
-            except KeyError:
-                continue
-
-    def process_video(task, pbar):
-        src_path, dst_path = task
-        pbar.set_postfix_str(src_path.name)
-        flip_video_frames(src_path, dst_path, src_dataset.meta.fps, vcodec)
-        return src_path
-
-    if num_workers is None:
-        num_workers = os.cpu_count() or 16
-    num_workers = min(len(video_tasks), num_workers)
-    logger.info(f"Processing {len(video_tasks)} videos with {num_workers} workers")
-    with tqdm(total=len(video_tasks), desc="Mirroring videos") as pbar:
-        with ThreadPoolExecutor(max_workers=num_workers) as executor:
-            futures = {executor.submit(process_video, t, pbar): t for t in video_tasks}
-            for future in as_completed(futures):
-                task = futures[future]
-                future.result()
-                pbar.set_postfix_str(f"done: {task[0].name}")
-                pbar.update(1)
-
-
-def _copy_episodes_metadata(
-    src_dataset: LeRobotDataset,
-    dst_meta: LeRobotDatasetMetadata,
-) -> None:
-    """Copy episodes metadata with swapped video keys."""
-    episodes_dir = src_dataset.root / "meta/episodes"
-    dst_episodes_dir = dst_meta.root / "meta/episodes"
-
-    if episodes_dir.exists():
-        dst_episodes_dir.mkdir(parents=True, exist_ok=True)
-        for src_parquet in episodes_dir.glob("*/*.parquet"):
-            df = pd.read_parquet(src_parquet)
-            columns_to_rename = {}
-            for col in df.columns:
-                if col.startswith("videos/"):
-                    parts = col.split("/")
-                    if len(parts) >= 2:
-                        video_key = parts[1]
-                        new_video_key = swap_left_right_name(video_key)
-                        new_col = col.replace(f"videos/{video_key}/", f"videos/{new_video_key}/")
-                        columns_to_rename[col] = new_col
-            if columns_to_rename:
-                df = df.rename(columns=columns_to_rename)
-            dst_parquet = dst_episodes_dir / src_parquet.relative_to(episodes_dir)
-            dst_parquet.parent.mkdir(parents=True, exist_ok=True)
-            df.to_parquet(dst_parquet, index=False)
-
-    dst_meta.info.update({
-        "total_episodes": src_dataset.meta.total_episodes,
-        "total_frames": src_dataset.meta.total_frames,
-        "total_tasks": src_dataset.meta.total_tasks,
-        "total_videos": src_dataset.meta.total_videos,
-        "total_chunks": src_dataset.meta.total_chunks,
-    })
-    write_info(dst_meta.info, dst_meta.root)
-
-    if src_dataset.meta.stats is not None:
-        mirrored_stats = _mirror_stats(src_dataset.meta.stats)
-        write_stats(mirrored_stats, dst_meta.root)
-
-
-def _mirror_stats(stats: dict) -> dict:
-    """Mirror stats by swapping left/right in feature names."""
-    mirrored = {}
-    for key, value in stats.items():
-        new_key = swap_left_right_name(key)
-        if isinstance(value, dict):
-            mirrored[new_key] = _mirror_stats(value)
-        else:
-            mirrored[new_key] = value
-    return mirrored
-
-
-def main():
-    logging.basicConfig(level=logging.INFO)
-    parser = argparse.ArgumentParser(description="Mirror a bimanual robot dataset")
-    parser.add_argument("--repo_id", type=str, required=True, help="Source dataset repo_id")
-    parser.add_argument("--output_repo_id", type=str, required=True, help="Output dataset repo_id")
-    parser.add_argument("--root", type=str, default=None, help="Source dataset root directory")
-    parser.add_argument("--output_root", type=str, default=None, help="Output dataset root directory")
-    parser.add_argument("--vcodec", type=str, default="libsvtav1", help="Video codec (libsvtav1, h264, hevc)")
-    parser.add_argument("--num_workers", type=int, default=None, help="Number of parallel workers for video processing")
-    parser.add_argument("--push-to-hub", action="store_true", help="Push mirrored dataset to HuggingFace Hub")
-    args = parser.parse_args()
-
-    dataset = mirror_dataset(
-        repo_id=args.repo_id,
-        output_repo_id=args.output_repo_id,
-        root=args.root,
-        output_root=args.output_root,
-        vcodec=args.vcodec,
-        num_workers=args.num_workers,
-    )
-
-    if getattr(args, "push_to_hub", False):
-        logger.info(f"Pushing dataset to HuggingFace Hub: {args.output_repo_id}")
-        dataset.push_to_hub()
-
-
-if __name__ == "__main__":
-    main()
-

src/lerobot/scripts/lerobot_setup_can.py

@@ -45,7 +45,7 @@ from dataclasses import dataclass, field
 
 import draccus
 
-from lerobot.utils.import_utils import _can_available
+from lerobot.utils.import_utils import is_package_available
 
 MOTOR_NAMES = {
     0x01: "joint_1",
@@ -336,7 +336,7 @@ def run_speed(cfg: CANSetupConfig):
 
 @draccus.wrap()
 def setup_can(cfg: CANSetupConfig):
-    if not _can_available:
+    if not is_package_available("can"):
         print("Error: python-can not installed. Install with: pip install python-can")
         sys.exit(1)
 

src/lerobot/scripts/lerobot_train.py

@@ -175,6 +175,8 @@ def train(cfg: TrainPipelineConfig, accelerator: Accelerator | None = None):
         from accelerate.utils import DistributedDataParallelKwargs
 
         ddp_kwargs = DistributedDataParallelKwargs(find_unused_parameters=True)
+        # Accelerate auto-detects the device based on the available hardware and ignores the policy.device setting.
+        # Force the device to be CPU when policy.device is set to CPU.
         force_cpu = cfg.policy.device == "cpu"
         accelerator = Accelerator(
             step_scheduler_with_optimizer=False,
@@ -209,98 +211,16 @@ def train(cfg: TrainPipelineConfig, accelerator: Accelerator | None = None):
     torch.backends.cuda.matmul.allow_tf32 = True
 
     # Dataset loading synchronization: main process downloads first to avoid race conditions
-    delta_action_stats = None
     if is_main_process:
         logging.info("Creating dataset")
         dataset = make_dataset(cfg)
 
-        # Compute delta action stats BEFORE distributed sync to avoid NCCL timeout
-        if getattr(cfg.policy, "use_delta_actions", False):
-            import numpy as np
-
-            from lerobot.datasets.compute_stats import get_feature_stats
-            from lerobot.processor.delta_action_processor import DeltaActionsProcessorStep, to_delta_actions
-
-            chunk_size = cfg.policy.chunk_size
-            hf = dataset.hf_dataset
-            total_frames = len(hf)
-            sample_upper_bound = total_frames - chunk_size
-            if sample_upper_bound <= 0:
-                raise ValueError(
-                    f"Cannot compute delta action stats: total_frames={total_frames}, chunk_size={chunk_size}"
-                )
-
-            max_samples = min(100_000, sample_upper_bound)
-            indices = np.random.choice(sample_upper_bound, max_samples, replace=False)
-
-            action_names = dataset.meta.features.get("action", {}).get("names")
-            delta_mask_step = DeltaActionsProcessorStep(
-                enabled=True,
-                exclude_joints=getattr(cfg.policy, "delta_exclude_joints", []),
-                action_names=action_names,
-            )
-            delta_mask = delta_mask_step._build_mask(dataset.meta.features["action"]["shape"][0])
-            logging.info(
-                f"use_delta_actions is enabled — computing delta action stats "
-                f"from {max_samples} chunk samples (chunk_size={chunk_size})"
-            )
-
-            all_delta_actions = []
-            episode_indices = np.array(hf["episode_index"])
-            for idx in indices:
-                idx = int(idx)
-                ep_idx = episode_indices[idx]
-                end_idx = min(idx + chunk_size, total_frames)
-                if end_idx > idx and episode_indices[end_idx - 1] != ep_idx:
-                    continue
-
-                chunk_data = hf[idx:end_idx]
-                actions = torch.tensor(np.stack([np.asarray(a) for a in chunk_data["action"]])).float()
-                state = torch.tensor(np.asarray(chunk_data["observation.state"][0])).float()
-
-                delta = to_delta_actions(actions.unsqueeze(0), state.unsqueeze(0), delta_mask).squeeze(0)
-                all_delta_actions.append(delta.numpy())
-
-            if not all_delta_actions:
-                raise RuntimeError("Failed to compute delta action stats: no valid chunks found.")
-
-            all_delta = np.concatenate(all_delta_actions, axis=0)
-            delta_stats = get_feature_stats(all_delta, axis=0, keepdims=all_delta.ndim == 1)
-            delta_action_stats = delta_stats
-            dataset.meta.stats["action"] = delta_action_stats
-
-            norm_type = "UNKNOWN"
-            if hasattr(cfg.policy, "normalization_mapping"):
-                from lerobot.configs.types import NormalizationMode
-                action_norm = cfg.policy.normalization_mapping.get("ACTION", None)
-                norm_type = action_norm.value if action_norm else "UNKNOWN"
-
-            excluded_dims = len(delta_mask) - sum(delta_mask)
-            logging.info(
-                f"Delta action stats ({len(all_delta_actions)} chunks, {len(all_delta)} values, norm={norm_type}): "
-                f"delta_dims={sum(delta_mask)}/{len(delta_mask)} (excluded={excluded_dims}), "
-                f"mean={np.abs(delta_stats['mean']).mean():.4f}, std={delta_stats['std'].mean():.4f}, "
-                f"q01={delta_stats['q01'].mean():.4f}, q99={delta_stats['q99'].mean():.4f}"
-            )
-            if norm_type == "QUANTILES":
-                q_range = (delta_stats['q99'] - delta_stats['q01']).mean()
-                logging.info(f"  Quantile range (q99-q01): {q_range:.4f}")
-
     accelerator.wait_for_everyone()
 
     # Now all other processes can safely load the dataset
     if not is_main_process:
         dataset = make_dataset(cfg)
 
-    # Ensure all ranks use the exact same delta action stats.
-    if getattr(cfg.policy, "use_delta_actions", False):
-        if accelerator.num_processes > 1 and torch.distributed.is_initialized():
-            stats_list = [delta_action_stats]
-            torch.distributed.broadcast_object_list(stats_list, src=0)
-            delta_action_stats = stats_list[0]
-        if delta_action_stats is not None:
-            dataset.meta.stats["action"] = delta_action_stats
-
     # 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.
@@ -326,22 +246,10 @@ def train(cfg: TrainPipelineConfig, accelerator: Accelerator | None = None):
     # Wait for all processes to finish policy creation before continuing
     accelerator.wait_for_everyone()
 
-    processor_pretrained_path = cfg.policy.pretrained_path
-    if (
-        getattr(cfg.policy, "use_delta_actions", False)
-        and processor_pretrained_path is not None
-        and not cfg.resume
-    ):
-        logging.warning(
-            "use_delta_actions=true with pretrained processors can skip delta transforms if "
-            "the checkpoint processors do not define them. Building processors from current policy config."
-        )
-        processor_pretrained_path = None
-
     # Create processors - only provide dataset_stats if not resuming from saved processors
     processor_kwargs = {}
     postprocessor_kwargs = {}
-    if (processor_pretrained_path and not cfg.resume) or not processor_pretrained_path:
+    if (cfg.policy.pretrained_path and not cfg.resume) or not cfg.policy.pretrained_path:
         # Only provide dataset_stats when not resuming from saved processor state
         processor_kwargs["dataset_stats"] = dataset.meta.stats
 
@@ -349,7 +257,7 @@ def train(cfg: TrainPipelineConfig, accelerator: Accelerator | None = None):
     if cfg.policy.type == "sarm":
         processor_kwargs["dataset_meta"] = dataset.meta
 
-    if processor_pretrained_path is not None:
+    if cfg.policy.pretrained_path is not None:
         processor_kwargs["preprocessor_overrides"] = {
             "device_processor": {"device": device.type},
             "normalizer_processor": {
@@ -371,7 +279,7 @@ def train(cfg: TrainPipelineConfig, accelerator: Accelerator | None = None):
 
     preprocessor, postprocessor = make_pre_post_processors(
         policy_cfg=cfg.policy,
-        pretrained_path=processor_pretrained_path,
+        pretrained_path=cfg.policy.pretrained_path,
         **processor_kwargs,
         **postprocessor_kwargs,
     )
@@ -430,11 +338,21 @@ def train(cfg: TrainPipelineConfig, accelerator: Accelerator | None = None):
 
     # create dataloader for offline training
     if hasattr(cfg.policy, "drop_n_last_frames"):
+        # loop over dataset subtask parquet file to find episode indices that don't have subtask index != -1
+        # valid_episode_list passed to episode_indexes_to_use
+        valid_episode_list = []
+        for episode_idx in range(len(dataset.meta.episodes)):
+            subtask_index = dataset[episode_idx]["subtask_index"]
+            if subtask_index != -1:
+                valid_episode_list.append(episode_idx)
+
+        episode_indices_to_use = valid_episode_list
+
         shuffle = False
         sampler = EpisodeAwareSampler(
             dataset.meta.episodes["dataset_from_index"],
             dataset.meta.episodes["dataset_to_index"],
-            episode_indices_to_use=dataset.episodes,
+            episode_indices_to_use=episode_indices_to_use,
             drop_n_last_frames=cfg.policy.drop_n_last_frames,
             shuffle=True,
         )
@@ -489,36 +407,7 @@ def train(cfg: TrainPipelineConfig, accelerator: Accelerator | None = None):
     for _ in range(step, cfg.steps):
         start_time = time.perf_counter()
         batch = next(dl_iter)
-
-        # Debug logging for first few steps and periodically
-        if is_main_process and (step < 3 or (cfg.log_freq > 0 and step % (cfg.log_freq * 10) == 0)):
-            action = batch.get("action")
-            state = batch.get("observation.state")
-            if action is not None and state is not None:
-                logging.info(
-                    f"[DEBUG step={step}] PRE-PROCESSOR — "
-                    f"action: shape={tuple(action.shape)}, mean={action.mean():.4f}, std={action.std():.4f}, "
-                    f"min={action.min():.4f}, max={action.max():.4f} | "
-                    f"state: shape={tuple(state.shape)}, mean={state.mean():.4f}"
-                )
-
         batch = preprocessor(batch)
-
-        if is_main_process and (step < 3 or (cfg.log_freq > 0 and step % (cfg.log_freq * 10) == 0)):
-            action = batch.get("action")
-            state = batch.get("observation.state")
-            if action is not None:
-                logging.info(
-                    f"[DEBUG step={step}] POST-PROCESSOR — "
-                    f"action: shape={tuple(action.shape)}, mean={action.mean():.4f}, std={action.std():.4f}, "
-                    f"min={action.min():.4f}, max={action.max():.4f}"
-                )
-                if state is not None:
-                    logging.info(
-                        f"[DEBUG step={step}] POST-PROCESSOR — "
-                        f"state: shape={tuple(state.shape)}, mean={state.mean():.4f}, std={state.std():.4f}"
-                    )
-
         train_tracker.dataloading_s = time.perf_counter() - start_time
 
         train_tracker, output_dict = update_policy(

src/lerobot/teleoperators/bi_openarm_leader/bi_openarm_leader.py

@@ -19,7 +19,6 @@ from functools import cached_property
 
 from lerobot.processor import RobotAction
 from lerobot.teleoperators.openarm_leader import OpenArmLeaderConfig
-from lerobot.utils.decorators import check_if_already_connected, check_if_not_connected
 
 from ..openarm_leader import OpenArmLeader
 from ..teleoperator import Teleoperator
@@ -89,7 +88,6 @@ class BiOpenArmLeader(Teleoperator):
     def is_connected(self) -> bool:
         return self.left_arm.is_connected and self.right_arm.is_connected
 
-    @check_if_already_connected
     def connect(self, calibrate: bool = True) -> None:
         self.left_arm.connect(calibrate)
         self.right_arm.connect(calibrate)
@@ -111,7 +109,6 @@ class BiOpenArmLeader(Teleoperator):
             "Motor ID configuration is typically done via manufacturer tools for CAN motors."
         )
 
-    @check_if_not_connected
     def get_action(self) -> RobotAction:
         action_dict = {}
 
@@ -129,7 +126,6 @@ class BiOpenArmLeader(Teleoperator):
         # TODO: Implement force feedback
         raise NotImplementedError
 
-    @check_if_not_connected
     def disconnect(self) -> None:
         self.left_arm.disconnect()
         self.right_arm.disconnect()

src/lerobot/teleoperators/bi_so_leader/bi_so_leader.py

@@ -18,7 +18,7 @@ import logging
 from functools import cached_property
 
 from lerobot.teleoperators.so_leader import SOLeaderTeleopConfig
-from lerobot.utils.decorators import check_if_already_connected, check_if_not_connected
+from lerobot.utils.decorators import check_if_not_connected
 
 from ..so_leader import SOLeader
 from ..teleoperator import Teleoperator
@@ -72,7 +72,6 @@ class BiSOLeader(Teleoperator):
     def is_connected(self) -> bool:
         return self.left_arm.is_connected and self.right_arm.is_connected
 
-    @check_if_already_connected
     def connect(self, calibrate: bool = True) -> None:
         self.left_arm.connect(calibrate)
         self.right_arm.connect(calibrate)
@@ -111,7 +110,6 @@ class BiSOLeader(Teleoperator):
         # TODO: Implement force feedback
         raise NotImplementedError
 
-    @check_if_not_connected
     def disconnect(self) -> None:
         self.left_arm.disconnect()
         self.right_arm.disconnect()

src/lerobot/teleoperators/openarm_leader/openarm_leader.py

@@ -21,7 +21,7 @@ from typing import Any
 from lerobot.motors import Motor, MotorCalibration, MotorNormMode
 from lerobot.motors.damiao import DamiaoMotorsBus
 from lerobot.processor import RobotAction
-from lerobot.utils.decorators import check_if_already_connected, check_if_not_connected
+from lerobot.utils.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError
 
 from ..teleoperator import Teleoperator
 from .config_openarm_leader import OpenArmLeaderConfig
@@ -84,7 +84,6 @@ class OpenArmLeader(Teleoperator):
         """Check if teleoperator is connected."""
         return self.bus.is_connected
 
-    @check_if_already_connected
     def connect(self, calibrate: bool = True) -> None:
         """
         Connect to the teleoperator.
@@ -92,6 +91,8 @@ class OpenArmLeader(Teleoperator):
         For manual control, we disable torque after connecting so the
         arm can be moved by hand.
         """
+        if self.is_connected:
+            raise DeviceAlreadyConnectedError(f"{self} already connected")
 
         # Connect to CAN bus
         logger.info(f"Connecting arm on {self.config.port}...")
@@ -182,7 +183,6 @@ class OpenArmLeader(Teleoperator):
             "Motor ID configuration is typically done via manufacturer tools for CAN motors."
         )
 
-    @check_if_not_connected
     def get_action(self) -> RobotAction:
         """
         Get current action from the leader arm.
@@ -193,6 +193,8 @@ class OpenArmLeader(Teleoperator):
         Reads all motor states (pos/vel/torque) in one CAN refresh cycle.
         """
         start = time.perf_counter()
+        if not self.is_connected:
+            raise DeviceNotConnectedError(f"{self} is not connected.")
 
         action_dict: dict[str, Any] = {}
 
@@ -212,9 +214,10 @@ class OpenArmLeader(Teleoperator):
     def send_feedback(self, feedback: dict[str, float]) -> None:
         raise NotImplementedError("Feedback is not yet implemented for OpenArm leader.")
 
-    @check_if_not_connected
     def disconnect(self) -> None:
         """Disconnect from teleoperator."""
+        if not self.is_connected:
+            raise DeviceNotConnectedError(f"{self} is not connected.")
 
         # Disconnect CAN bus
         # For manual control, ensure torque is disabled before disconnecting

src/lerobot/teleoperators/so_leader/config_so_leader.py

@@ -28,7 +28,7 @@ class SOLeaderConfig:
     port: str
 
     # Whether to use degrees for angles
-    use_degrees: bool = True
+    use_degrees: bool = False
 
 
 @TeleoperatorConfig.register_subclass("so101_leader")

src/lerobot/utils/constants.py

@@ -26,6 +26,9 @@ OBS_IMAGES = OBS_IMAGE + "s"
 OBS_LANGUAGE = OBS_STR + ".language"
 OBS_LANGUAGE_TOKENS = OBS_LANGUAGE + ".tokens"
 OBS_LANGUAGE_ATTENTION_MASK = OBS_LANGUAGE + ".attention_mask"
+OBS_LANGUAGE_USER_PROMPT = OBS_STR + ".user_prompt"
+OBS_LANGUAGE_USER_PROMPT_TOKENS = OBS_LANGUAGE_USER_PROMPT + ".tokens"
+OBS_LANGUAGE_USER_PROMPT_ATTENTION_MASK = OBS_LANGUAGE_USER_PROMPT_TOKENS + ".attention_mask"
 OBS_LANGUAGE_SUBTASK = OBS_STR + ".subtask"
 OBS_LANGUAGE_SUBTASK_TOKENS = OBS_LANGUAGE_SUBTASK + ".tokens"
 OBS_LANGUAGE_SUBTASK_ATTENTION_MASK = OBS_LANGUAGE_SUBTASK + ".attention_mask"
@@ -89,3 +92,101 @@ LIBERO_KEY_JOINTS_POS = "robot_state/joints/pos"
 LIBERO_KEY_JOINTS_VEL = "robot_state/joints/vel"
 LIBERO_KEY_PIXELS_AGENTVIEW = "pixels/agentview_image"
 LIBERO_KEY_PIXELS_EYE_IN_HAND = "pixels/robot0_eye_in_hand_image"
+
+# Skill segmentation prompt template for VLM-based subtask annotation
+# Placeholders: {goal_context}, {subtask_labels_section}
+# When subtask_labels are provided, use format_subtask_labels_section() to fill {subtask_labels_section}.
+
+
+def format_subtask_labels_section(subtask_labels: list[str]) -> str:
+    """Format a list of subtask labels for insertion into SKILL_SEGMENTATION_PROMPT_TEMPLATE.
+    The model will be instructed to choose only from these labels.
+    """
+    if not subtask_labels:
+        return ""
+    return "\n".join(f'    "{label}",' for label in subtask_labels).rstrip(",")
+
+
+SKILL_SEGMENTATION_PROMPT_TEMPLATE = """# Role
+You are a Robotics Vision System specializing in temporal action segmentation for robot manipulation demonstrations.
+
+# Video duration (critical)
+The total video length is **{video_duration_seconds} seconds** ({video_duration_mm_ss}). All "start" and "end" values in your JSON must be numeric seconds in the range [0.0, {video_duration_seconds}]. The last skill's "end" must be exactly **{video_duration_seconds}**. Do not stop earlier.
+
+# Task
+{goal_context}Segment this robot demonstration video into short atomic manipulation skills. Each skill should:
+- Last approximately 1-3 seconds (or longer if the action takes longer)
+- Describe a clear, single action (e.g., "pick up object", "move arm left", "release gripper")
+- Have precise start and end timestamps in seconds (float)
+
+# Requirements
+1. **Atomic Actions**: Each skill should be a single, indivisible action
+2. **Complete Coverage**: Skills must cover the entire video from 0.0 to {video_duration_seconds} seconds with no gaps
+3. **Boundary Consistency**: The end of one skill equals the start of the next
+4. **Natural Language**: Use clear, descriptive names for each skill
+5. **Timestamps**: Use seconds as floats (e.g. 12.5) for all timestamps; the last "end" must be {video_duration_seconds}. If the video has a visible timer in the corner showing elapsed time in seconds, use it to report accurate start and end times for each skill.
+# Subtask Label Set (Closed Vocabulary)
+        You MUST strictly identify the video segments using ONLY the following labels. Do not create new labels or modify existing ones:
+
+        [
+        {subtask_labels_section}
+        ]
+
+        The video shows one successful execution of all subtasks in a logical order.
+
+        # Ground-Truth Semantics (Very Important)
+        Use **visual state changes** to define when a subtask starts and ends. Do NOT assume equal durations for the subtasks.
+
+        - A subtask **starts** at the first frame where the robot's motion clearly initiates that subtask.
+        - A subtask **ends** at the first frame where that specific action is visually completed and the manipulated object reaches a temporary, stable configuration.
+
+        If there are short pauses or micro-motions that don't clearly correspond to a new subtask, they belong to the **current** subtask.
+
+        # Hard Constraints & Logic
+        1. **Continuous Coverage (No Gaps):**
+           - The entire video from 0.0 to {video_duration_seconds} seconds must be covered by subtasks.
+           - There can be no gaps between subtasks.
+           - If there is any idle or ambiguous time between clear actions, extend the *preceding* subtask to cover it.
+
+        2. **Boundary Consistency:**
+           - The `"end"` timestamp of one subtask must be exactly equal to the `"start"` timestamp of the next subtask.
+           - Boundaries must coincide with a real visual state transition, not just a convenient time split.
+
+        3. **Chronological Order, One Occurrence Each:**
+           - This is a single successful demonstration.
+           - Each subtask from the vocabulary appears **exactly once**, in the correct logical order.
+           - **Durations may be very different** between subtasks. Never assume they are similar lengths. Base all boundaries only on the video.
+
+        4. **Reject Uniform Segmentation (Important):**
+           - Do NOT simply divide the video into equal or nearly equal time chunks.
+           - If your boundaries would result in subtasks with similar durations (e.g. all around 5 seconds), treat this as evidence that your segmentation is wrong and refine the boundaries.
+           - Only use nearly equal durations if the video truly shows each subtask taking the same amount of time (this is very rare).
+
+        5. **Timestamps (critical):**
+           - Use numeric seconds (float) in the JSON, e.g. 0.0, 5.2, 12.8.
+           - The first subtask always starts at 0.0.
+           - The last subtask must end at exactly {video_duration_seconds} (the full video length).
+           - **Time is displayed inside the video**: a visible timer in one corner shows the elapsed time in seconds (from 0.0 to the end). Use this on-screen timer to set accurate start and end times for each skill.
+        Format this as a bullet list.
+
+# Output Format
+output ONLY valid JSON with this exact structure. The last skill's "end" MUST be exactly {video_duration_seconds}. Use the timestamps you read from the visible timer in the video:
+
+```json
+{{
+  "skills": [
+    {{"name": "first skill", "start": 0.0, "end": 5.0}},
+    {{"name": "second skill", "start": 5.0, "end": 12.0}},
+    {{"name": "last skill", "start": 12.0, "end": {video_duration_seconds}}}
+  ]
+}}
+```
+
+The first skill must start at 0.0 and the last skill must end at **{video_duration_seconds}** (the total video duration in seconds).
+# Strict Structural Rule
+This video contains exactly 4 subtasks.
+You MUST output exactly 4 segments.
+Each segment must use a unique label from the vocabulary.
+No label may be repeated.
+
+"""

tests/policies/test_delta_actions.py

@@ -1,344 +0,0 @@
-"""Tests for delta action transforms — full pipeline validation.
-
-Tests the complete flow matching OpenPI:
-  raw actions → DeltaActions → Normalize(delta_stats) → model → Unnormalize → AbsoluteActions
-
-Uses real dataset: lerobot-data-collection/dagger_final_1_21
-"""
-
-import numpy as np
-import pytest
-import torch
-
-from lerobot.configs.types import FeatureType, NormalizationMode, PolicyFeature
-from lerobot.datasets.compute_stats import get_feature_stats
-from lerobot.datasets.lerobot_dataset import LeRobotDataset
-from lerobot.processor import TransitionKey, batch_to_transition
-from lerobot.processor.delta_action_processor import (
-    AbsoluteActionsProcessorStep,
-    DeltaActionsProcessorStep,
-    to_absolute_actions,
-    to_delta_actions,
-)
-from lerobot.processor.normalize_processor import NormalizerProcessorStep, UnnormalizerProcessorStep
-from lerobot.utils.constants import ACTION, OBS_STATE
-
-CHUNK_SIZE = 10
-REPO_ID = "lerobot-data-collection/dagger_final_1_21"
-
-
-@pytest.fixture(scope="module")
-def dataset():
-    return LeRobotDataset(REPO_ID, episodes=[0])
-
-
-@pytest.fixture(scope="module")
-def action_dim(dataset):
-    return dataset.meta.features["action"]["shape"][0]
-
-
-def _build_action_chunks(dataset, chunk_size, max_chunks=50):
-    """Build action chunks from hf_dataset, like the training script does."""
-    hf = dataset.hf_dataset
-    total = len(hf)
-    all_ep = torch.tensor([int(hf[i]["episode_index"]) for i in range(total)])
-    chunks, states = [], []
-    for i in range(total - chunk_size + 1):
-        if all_ep[i] != all_ep[i + chunk_size - 1]:
-            continue
-        chunk_actions = torch.stack([hf[i + k]["action"] for k in range(chunk_size)]).float()
-        state = hf[i]["observation.state"].float()
-        chunks.append(chunk_actions)
-        states.append(state)
-        if len(chunks) >= max_chunks:
-            break
-    assert len(chunks) > 0, f"No valid chunks found. total={total}, ep_indices={all_ep.tolist()}"
-    return torch.stack(chunks), torch.stack(states)
-
-
-def _compute_delta_chunk_stats(action_chunks, states, mask):
-    all_deltas = []
-    for actions, state in zip(action_chunks, states):
-        delta = to_delta_actions(actions.unsqueeze(0), state.unsqueeze(0), mask).squeeze(0)
-        all_deltas.append(delta.numpy())
-    all_delta = np.concatenate(all_deltas, axis=0)
-    return get_feature_stats(all_delta, axis=0, keepdims=all_delta.ndim == 1)
-
-
-# --- Basic roundtrip tests ---
-
-def test_roundtrip_3d(action_dim):
-    actions = torch.randn(4, CHUNK_SIZE, action_dim)
-    state = torch.randn(4, action_dim)
-    mask = [True] * action_dim
-    recovered = to_absolute_actions(to_delta_actions(actions, state, mask), state, mask)
-    torch.testing.assert_close(recovered, actions)
-
-
-def test_roundtrip_2d(action_dim):
-    actions = torch.randn(4, action_dim)
-    state = torch.randn(4, action_dim)
-    mask = [True] * action_dim
-    recovered = to_absolute_actions(to_delta_actions(actions, state, mask), state, mask)
-    torch.testing.assert_close(recovered, actions)
-
-
-def test_no_mutation(action_dim):
-    actions = torch.randn(2, CHUNK_SIZE, action_dim)
-    original = actions.clone()
-    state = torch.randn(2, action_dim)
-    to_delta_actions(actions, state, [True] * action_dim)
-    torch.testing.assert_close(actions, original)
-
-
-def test_exclude_joints_supports_partial_name_matching():
-    names = [
-        "right_joint_1.pos",
-        "right_gripper.pos",
-        "left_joint_1.pos",
-        "left_gripper.pos",
-    ]
-    step = DeltaActionsProcessorStep(enabled=True, exclude_joints=["gripper"], action_names=names)
-    assert step._build_mask(len(names)) == [True, False, True, False]
-
-
-# --- Chunk-level delta stats test ---
-
-def test_chunk_stats_have_larger_std_than_frame_stats(dataset, action_dim):
-    """Chunk-level delta stats should have larger std than per-frame delta stats."""
-    action_chunks, states = _build_action_chunks(dataset, CHUNK_SIZE)
-    mask = [True] * action_dim
-
-    chunk_stats = _compute_delta_chunk_stats(action_chunks, states, mask)
-
-    # Per-frame stats
-    hf = dataset.hf_dataset
-    n = min(500, len(hf))
-    frame_actions = torch.stack([hf[i]["action"] for i in range(n)]).float()
-    frame_states = torch.stack([hf[i]["observation.state"] for i in range(n)]).float()
-    frame_deltas = to_delta_actions(frame_actions, frame_states, mask).numpy()
-    frame_stats = get_feature_stats(frame_deltas, axis=0, keepdims=frame_deltas.ndim == 1)
-
-    assert chunk_stats["std"].mean() >= frame_stats["std"].mean(), (
-        f"Chunk std ({chunk_stats['std'].mean():.4f}) should be >= "
-        f"frame std ({frame_stats['std'].mean():.4f})"
-    )
-
-
-# --- Full pipeline roundtrip: delta → normalize → unnormalize → absolute ---
-
-def test_full_pipeline_roundtrip(dataset, action_dim):
-    """Test the complete OpenPI pipeline: delta → normalize → unnormalize → absolute."""
-    action_chunks, states = _build_action_chunks(dataset, CHUNK_SIZE)
-    mask = [True] * action_dim
-
-    delta_stats = _compute_delta_chunk_stats(action_chunks, states, mask)
-    stats = {ACTION: {k: v for k, v in delta_stats.items()}}
-
-    features = {ACTION: PolicyFeature(type=FeatureType.ACTION, shape=(action_dim,))}
-    norm_map = {FeatureType.ACTION: NormalizationMode.MEAN_STD}
-
-    delta_step = DeltaActionsProcessorStep(enabled=True)
-    normalizer = NormalizerProcessorStep(features=features, norm_map=norm_map, stats=stats)
-    unnormalizer = UnnormalizerProcessorStep(features=features, norm_map=norm_map, stats=stats)
-    absolute_step = AbsoluteActionsProcessorStep(enabled=True, delta_step=delta_step)
-
-    original_actions = action_chunks[0].unsqueeze(0)
-    state = states[0].unsqueeze(0)
-
-    batch = {ACTION: original_actions, OBS_STATE: state}
-    transition = batch_to_transition(batch)
-
-    # Forward: delta → normalize
-    t1 = delta_step(transition)
-    t2 = normalizer(t1)
-
-    normalized_action = t2[TransitionKey.ACTION]
-    assert normalized_action.abs().mean() < 10, (
-        f"Normalized actions should be in reasonable range, got mean abs {normalized_action.abs().mean():.2f}"
-    )
-
-    # Reverse: unnormalize → absolute
-    t3 = unnormalizer(t2)
-    t4 = absolute_step(t3)
-
-    recovered_actions = t4[TransitionKey.ACTION]
-    torch.testing.assert_close(recovered_actions, original_actions, atol=1e-4, rtol=1e-4)
-
-
-def test_normalized_delta_values_are_reasonable(dataset, action_dim):
-    """With correct chunk stats, normalized delta actions should be in a reasonable range."""
-    action_chunks, states = _build_action_chunks(dataset, CHUNK_SIZE)
-    mask = [True] * action_dim
-
-    delta_stats = _compute_delta_chunk_stats(action_chunks, states, mask)
-    mean = torch.tensor(delta_stats["mean"]).float()
-    std = torch.tensor(delta_stats["std"]).float()
-
-    all_normalized = []
-    for actions, state in zip(action_chunks, states):
-        delta = to_delta_actions(actions.unsqueeze(0), state.unsqueeze(0), mask).squeeze(0)
-        normalized = (delta - mean) / (std + 1e-6)
-        all_normalized.append(normalized)
-
-    all_normalized = torch.cat(all_normalized, dim=0)
-
-    pct_in_range = (all_normalized.abs() < 5).float().mean()
-    assert pct_in_range > 0.9, (
-        f"Only {pct_in_range*100:.1f}% of normalized values in [-5, 5], expected >90%"
-    )
-
-    assert all_normalized.mean().abs() < 1.0, (
-        f"Mean of normalized deltas is {all_normalized.mean():.2f}, expected near 0"
-    )
-
-
-def test_processor_step_roundtrip(dataset, action_dim):
-    """DeltaActionsProcessorStep applies delta; to_absolute_actions recovers original."""
-    hf = dataset.hf_dataset
-    batch = {
-        ACTION: torch.stack([hf[i]["action"] for i in range(4)]),
-        OBS_STATE: torch.stack([hf[i]["observation.state"] for i in range(4)]),
-    }
-    original_actions = batch[ACTION].clone()
-    transition = batch_to_transition(batch)
-
-    step = DeltaActionsProcessorStep(enabled=True)
-    delta_transition = step(transition)
-    assert not torch.allclose(delta_transition[TransitionKey.ACTION], original_actions)
-
-    state = transition[TransitionKey.OBSERVATION][OBS_STATE]
-    mask = [True] * action_dim
-    recovered = to_absolute_actions(delta_transition[TransitionKey.ACTION], state, mask)
-    torch.testing.assert_close(recovered, original_actions)
-
-
-def test_processor_step_disabled_is_noop(dataset, action_dim):
-    """enabled=False should be a no-op."""
-    hf = dataset.hf_dataset
-    batch = {
-        ACTION: torch.stack([hf[i]["action"] for i in range(2)]),
-        OBS_STATE: torch.stack([hf[i]["observation.state"] for i in range(2)]),
-    }
-    original = batch[ACTION].clone()
-    transition = batch_to_transition(batch)
-    result = DeltaActionsProcessorStep(enabled=False)(transition)
-    torch.testing.assert_close(result[TransitionKey.ACTION], original)
-
-
-# --- Training batch shape validation ---
-
-def test_delta_with_action_chunks(dataset, action_dim):
-    """Verify delta works correctly with (B, chunk_size, action_dim) shaped actions."""
-    action_chunks, states = _build_action_chunks(dataset, CHUNK_SIZE)
-
-    # Simulate a training batch: actions=(B, chunk_size, action_dim), state=(B, state_dim)
-    batch_actions = action_chunks[:4]  # (4, chunk_size, action_dim)
-    batch_states = states[:4]  # (4, state_dim)
-
-    mask = [True] * action_dim
-    delta = to_delta_actions(batch_actions, batch_states, mask)
-
-    # First action in each chunk should be close to zero (action[t] - state[t] ≈ small)
-    first_deltas = delta[:, 0, :]  # (B, action_dim)
-    assert first_deltas.abs().mean() < delta.abs().mean(), (
-        f"First action in chunk should have smaller delta than average. "
-        f"First: {first_deltas.abs().mean():.4f}, Average: {delta.abs().mean():.4f}"
-    )
-
-    # Later actions should have larger deltas
-    last_deltas = delta[:, -1, :]  # (B, action_dim)
-    assert last_deltas.abs().mean() >= first_deltas.abs().mean(), (
-        f"Last action in chunk should have >= delta than first. "
-        f"Last: {last_deltas.abs().mean():.4f}, First: {first_deltas.abs().mean():.4f}"
-    )
-
-    # Roundtrip
-    recovered = to_absolute_actions(delta, batch_states, mask)
-    torch.testing.assert_close(recovered, batch_actions)
-
-
-def test_delta_stats_match_actual_data_distribution(dataset, action_dim):
-    """Verify computed stats match the actual delta distribution."""
-    action_chunks, states = _build_action_chunks(dataset, CHUNK_SIZE)
-    mask = [True] * action_dim
-
-    # Compute stats like the training script does
-    delta_stats = _compute_delta_chunk_stats(action_chunks, states, mask)
-
-    # Also compute directly
-    all_deltas = []
-    for actions, state in zip(action_chunks, states):
-        delta = to_delta_actions(actions.unsqueeze(0), state.unsqueeze(0), mask).squeeze(0)
-        all_deltas.append(delta)
-    all_deltas_tensor = torch.cat(all_deltas, dim=0)
-
-    # Compare mean
-    actual_mean = all_deltas_tensor.mean(dim=0).numpy()
-    np.testing.assert_allclose(delta_stats["mean"], actual_mean, atol=0.01)
-
-    # Compare std
-    actual_std = all_deltas_tensor.std(dim=0).numpy()
-    np.testing.assert_allclose(delta_stats["std"], actual_std, atol=0.1)
-
-    # Verify q01 < mean < q99
-    assert (delta_stats["q01"] < delta_stats["mean"]).all(), "q01 should be < mean"
-    assert (delta_stats["mean"] < delta_stats["q99"]).all(), "mean should be < q99"
-
-
-def test_quantile_normalization_roundtrip(dataset, action_dim):
-    """Full roundtrip with QUANTILES normalization (what OpenPI uses for pi05)."""
-    action_chunks, states = _build_action_chunks(dataset, CHUNK_SIZE)
-    mask = [True] * action_dim
-
-    delta_stats = _compute_delta_chunk_stats(action_chunks, states, mask)
-    stats = {ACTION: {k: v for k, v in delta_stats.items()}}
-
-    features = {ACTION: PolicyFeature(type=FeatureType.ACTION, shape=(action_dim,))}
-    norm_map = {FeatureType.ACTION: NormalizationMode.QUANTILES}
-
-    delta_step = DeltaActionsProcessorStep(enabled=True)
-    normalizer = NormalizerProcessorStep(features=features, norm_map=norm_map, stats=stats)
-    unnormalizer = UnnormalizerProcessorStep(features=features, norm_map=norm_map, stats=stats)
-    absolute_step = AbsoluteActionsProcessorStep(enabled=True, delta_step=delta_step)
-
-    original_actions = action_chunks[0].unsqueeze(0)
-    state = states[0].unsqueeze(0)
-
-    batch = {ACTION: original_actions, OBS_STATE: state}
-    transition = batch_to_transition(batch)
-
-    # Forward: delta → quantile normalize
-    t1 = delta_step(transition)
-    t2 = normalizer(t1)
-
-    normalized = t2[TransitionKey.ACTION]
-    # Most values should be in [-1, 1] with quantile normalization
-    pct_in_range = (normalized.abs() < 2).float().mean()
-    assert pct_in_range > 0.5, (
-        f"Only {pct_in_range*100:.1f}% in [-2, 2] after quantile norm, expected >50%"
-    )
-
-    # Reverse: unnormalize → absolute
-    t3 = unnormalizer(t2)
-    t4 = absolute_step(t3)
-
-    recovered = t4[TransitionKey.ACTION]
-    torch.testing.assert_close(recovered, original_actions, atol=1e-3, rtol=1e-3)
-
-
-def test_state_not_modified_by_delta(dataset, action_dim):
-    """State should never be modified by the delta processor."""
-    hf = dataset.hf_dataset
-    batch = {
-        ACTION: torch.stack([hf[i]["action"] for i in range(4)]),
-        OBS_STATE: torch.stack([hf[i]["observation.state"] for i in range(4)]),
-    }
-    original_state = batch[OBS_STATE].clone()
-    transition = batch_to_transition(batch)
-
-    step = DeltaActionsProcessorStep(enabled=True)
-    result = step(transition)
-
-    result_state = result[TransitionKey.OBSERVATION][OBS_STATE]
-    torch.testing.assert_close(result_state, original_state)

tests/scripts/test_edit_dataset_parsing.py

@@ -1,71 +0,0 @@
-#!/usr/bin/env python
-
-# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import draccus
-import pytest
-
-from lerobot.scripts.lerobot_edit_dataset import (
-    ConvertImageToVideoConfig,
-    DeleteEpisodesConfig,
-    EditDatasetConfig,
-    MergeConfig,
-    ModifyTasksConfig,
-    OperationConfig,
-    RemoveFeatureConfig,
-    SplitConfig,
-)
-
-
-def parse_cfg(cli_args: list[str]) -> EditDatasetConfig:
-    """Helper to parse CLI args into an EditDatasetConfig via draccus."""
-    return draccus.parse(EditDatasetConfig, args=cli_args)
-
-
-class TestOperationTypeParsing:
-    """Test that --operation.type correctly selects the right config subclass."""
-
-    @pytest.mark.parametrize(
-        "type_name, expected_cls",
-        [
-            ("delete_episodes", DeleteEpisodesConfig),
-            ("split", SplitConfig),
-            ("merge", MergeConfig),
-            ("remove_feature", RemoveFeatureConfig),
-            ("modify_tasks", ModifyTasksConfig),
-            ("convert_image_to_video", ConvertImageToVideoConfig),
-        ],
-    )
-    def test_operation_type_resolves_correct_class(self, type_name, expected_cls):
-        cfg = parse_cfg(["--repo_id", "test/repo", "--operation.type", type_name])
-        assert isinstance(cfg.operation, expected_cls), (
-            f"Expected {expected_cls.__name__}, got {type(cfg.operation).__name__}"
-        )
-
-    @pytest.mark.parametrize(
-        "type_name, expected_cls",
-        [
-            ("delete_episodes", DeleteEpisodesConfig),
-            ("split", SplitConfig),
-            ("merge", MergeConfig),
-            ("remove_feature", RemoveFeatureConfig),
-            ("modify_tasks", ModifyTasksConfig),
-            ("convert_image_to_video", ConvertImageToVideoConfig),
-        ],
-    )
-    def test_get_choice_name_roundtrips(self, type_name, expected_cls):
-        cfg = parse_cfg(["--repo_id", "test/repo", "--operation.type", type_name])
-        resolved_name = OperationConfig.get_choice_name(type(cfg.operation))
-        assert resolved_name == type_name