Add chained SLURM mirror-and-double dataset script.

Provide a standalone DataTrove workflow that mirrors bimanual shards, aggregates mirrored output, builds a doubled dataset, and optionally pushes the final dataset to the Hub.

Made-with: Cursor

.github/workflows/full_tests.yml

@@ -101,9 +101,11 @@ jobs:
     runs-on:
       group: aws-general-8-plus
     if: |
-      (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.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'
+      )
     outputs:
       image_tag: ${{ steps.set_tag.outputs.image_tag }}
     env:

.github/workflows/unbound_deps_tests.yml

@@ -91,6 +91,7 @@ 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:

benchmarks/video/README.md

@@ -28,9 +28,9 @@ We don't expect the same optimal settings for a dataset of images from a simulat
 For these reasons, we run this benchmark on four representative datasets:
 
 - `lerobot/pusht_image`: (96 x 96 pixels) simulation with simple geometric shapes, fixed camera.
-- `aliberts/aloha_mobile_shrimp_image`: (480 x 640 pixels) real-world indoor, moving camera.
-- `aliberts/paris_street`: (720 x 1280 pixels) real-world outdoor, moving camera.
-- `aliberts/kitchen`: (1080 x 1920 pixels) real-world indoor, fixed camera.
+- `lerobot/aloha_mobile_shrimp_image`: (480 x 640 pixels) real-world indoor, moving camera.
+- `lerobot/paris_street`: (720 x 1280 pixels) real-world outdoor, moving camera.
+- `lerobot/kitchen`: (1080 x 1920 pixels) real-world indoor, fixed camera.
 
 Note: The datasets used for this benchmark need to be image datasets, not video datasets.
 
@@ -179,7 +179,7 @@ python benchmark/video/run_video_benchmark.py \
     --output-dir outputs/video_benchmark \
     --repo-ids \
         lerobot/pusht_image \
-        aliberts/aloha_mobile_shrimp_image \
+        lerobot/aloha_mobile_shrimp_image \
     --vcodec libx264 libx265 \
     --pix-fmt yuv444p yuv420p \
     --g 2 20 None \
@@ -203,9 +203,9 @@ python benchmark/video/run_video_benchmark.py \
     --output-dir outputs/video_benchmark \
     --repo-ids \
         lerobot/pusht_image \
-        aliberts/aloha_mobile_shrimp_image \
-        aliberts/paris_street \
-        aliberts/kitchen \
+        lerobot/aloha_mobile_shrimp_image \
+        lerobot/paris_street \
+        lerobot/kitchen \
     --vcodec libx264 libx265 \
     --pix-fmt yuv444p yuv420p \
     --g 1 2 3 4 5 6 10 15 20 40 None \
@@ -221,9 +221,9 @@ python benchmark/video/run_video_benchmark.py \
     --output-dir outputs/video_benchmark \
     --repo-ids \
         lerobot/pusht_image \
-        aliberts/aloha_mobile_shrimp_image \
-        aliberts/paris_street \
-        aliberts/kitchen \
+        lerobot/aloha_mobile_shrimp_image \
+        lerobot/paris_street \
+        lerobot/kitchen \
     --vcodec libsvtav1 \
     --pix-fmt yuv420p \
     --g 1 2 3 4 5 6 10 15 20 40 None \
@@ -252,37 +252,37 @@ Since we're using av1 encoding, we're choosing the `pyav` decoder as `video_read
 
 These tables show the results for `g=2` and `crf=30`, using `timestamps-modes=6_frames` and `backend=pyav`
 
-| video_images_size_ratio            | vcodec     | pix_fmt |           |           |           |
-| ---------------------------------- | ---------- | ------- | --------- | --------- | --------- |
-|                                    | libx264    |         | libx265   |           | libsvtav1 |
-| repo_id                            | yuv420p    | yuv444p | yuv420p   | yuv444p   | yuv420p   |
-| lerobot/pusht_image                | **16.97%** | 17.58%  | 18.57%    | 18.86%    | 22.06%    |
-| aliberts/aloha_mobile_shrimp_image | 2.14%      | 2.11%   | 1.38%     | **1.37%** | 5.59%     |
-| aliberts/paris_street              | 2.12%      | 2.13%   | **1.54%** | **1.54%** | 4.43%     |
-| aliberts/kitchen                   | 1.40%      | 1.39%   | **1.00%** | **1.00%** | 2.52%     |
+| video_images_size_ratio           | vcodec     | pix_fmt |           |           |           |
+| --------------------------------- | ---------- | ------- | --------- | --------- | --------- |
+|                                   | libx264    |         | libx265   |           | libsvtav1 |
+| repo_id                           | yuv420p    | yuv444p | yuv420p   | yuv444p   | yuv420p   |
+| lerobot/pusht_image               | **16.97%** | 17.58%  | 18.57%    | 18.86%    | 22.06%    |
+| lerobot/aloha_mobile_shrimp_image | 2.14%      | 2.11%   | 1.38%     | **1.37%** | 5.59%     |
+| lerobot/paris_street              | 2.12%      | 2.13%   | **1.54%** | **1.54%** | 4.43%     |
+| lerobot/kitchen                   | 1.40%      | 1.39%   | **1.00%** | **1.00%** | 2.52%     |
 
-| video_images_load_time_ratio       | vcodec  | pix_fmt |          |         |           |
-| ---------------------------------- | ------- | ------- | -------- | ------- | --------- |
-|                                    | libx264 |         | libx265  |         | libsvtav1 |
-| repo_id                            | yuv420p | yuv444p | yuv420p  | yuv444p | yuv420p   |
-| lerobot/pusht_image                | 6.45    | 5.19    | **1.90** | 2.12    | 2.47      |
-| aliberts/aloha_mobile_shrimp_image | 11.80   | 7.92    | 0.71     | 0.85    | **0.48**  |
-| aliberts/paris_street              | 2.21    | 2.05    | 0.36     | 0.49    | **0.30**  |
-| aliberts/kitchen                   | 1.46    | 1.46    | 0.28     | 0.51    | **0.26**  |
+| video_images_load_time_ratio      | vcodec  | pix_fmt |          |         |           |
+| --------------------------------- | ------- | ------- | -------- | ------- | --------- |
+|                                   | libx264 |         | libx265  |         | libsvtav1 |
+| repo_id                           | yuv420p | yuv444p | yuv420p  | yuv444p | yuv420p   |
+| lerobot/pusht_image               | 6.45    | 5.19    | **1.90** | 2.12    | 2.47      |
+| lerobot/aloha_mobile_shrimp_image | 11.80   | 7.92    | 0.71     | 0.85    | **0.48**  |
+| lerobot/paris_street              | 2.21    | 2.05    | 0.36     | 0.49    | **0.30**  |
+| lerobot/kitchen                   | 1.46    | 1.46    | 0.28     | 0.51    | **0.26**  |
 
-|                                    |          | vcodec   | pix_fmt      |          |           |              |
-| ---------------------------------- | -------- | -------- | ------------ | -------- | --------- | ------------ |
-|                                    |          | libx264  |              | libx265  |           | libsvtav1    |
-| repo_id                            | metric   | yuv420p  | yuv444p      | yuv420p  | yuv444p   | yuv420p      |
-| lerobot/pusht_image                | avg_mse  | 2.90E-04 | **2.03E-04** | 3.13E-04 | 2.29E-04  | 2.19E-04     |
-|                                    | avg_psnr | 35.44    | 37.07        | 35.49    | **37.30** | 37.20        |
-|                                    | avg_ssim | 98.28%   | **98.85%**   | 98.31%   | 98.84%    | 98.72%       |
-| aliberts/aloha_mobile_shrimp_image | avg_mse  | 2.76E-04 | 2.59E-04     | 3.17E-04 | 3.06E-04  | **1.30E-04** |
-|                                    | avg_psnr | 35.91    | 36.21        | 35.88    | 36.09     | **40.17**    |
-|                                    | avg_ssim | 95.19%   | 95.18%       | 95.00%   | 95.05%    | **97.73%**   |
-| aliberts/paris_street              | avg_mse  | 6.89E-04 | 6.70E-04     | 4.03E-03 | 4.02E-03  | **3.09E-04** |
-|                                    | avg_psnr | 33.48    | 33.68        | 32.05    | 32.15     | **35.40**    |
-|                                    | avg_ssim | 93.76%   | 93.75%       | 89.46%   | 89.46%    | **95.46%**   |
-| aliberts/kitchen                   | avg_mse  | 2.50E-04 | 2.24E-04     | 4.28E-04 | 4.18E-04  | **1.53E-04** |
-|                                    | avg_psnr | 36.73    | 37.33        | 36.56    | 36.75     | **39.12**    |
-|                                    | avg_ssim | 95.47%   | 95.58%       | 95.52%   | 95.53%    | **96.82%**   |
+|                                   |          | vcodec   | pix_fmt      |          |           |              |
+| --------------------------------- | -------- | -------- | ------------ | -------- | --------- | ------------ |
+|                                   |          | libx264  |              | libx265  |           | libsvtav1    |
+| repo_id                           | metric   | yuv420p  | yuv444p      | yuv420p  | yuv444p   | yuv420p      |
+| lerobot/pusht_image               | avg_mse  | 2.90E-04 | **2.03E-04** | 3.13E-04 | 2.29E-04  | 2.19E-04     |
+|                                   | avg_psnr | 35.44    | 37.07        | 35.49    | **37.30** | 37.20        |
+|                                   | avg_ssim | 98.28%   | **98.85%**   | 98.31%   | 98.84%    | 98.72%       |
+| lerobot/aloha_mobile_shrimp_image | avg_mse  | 2.76E-04 | 2.59E-04     | 3.17E-04 | 3.06E-04  | **1.30E-04** |
+|                                   | avg_psnr | 35.91    | 36.21        | 35.88    | 36.09     | **40.17**    |
+|                                   | avg_ssim | 95.19%   | 95.18%       | 95.00%   | 95.05%    | **97.73%**   |
+| lerobot/paris_street              | avg_mse  | 6.89E-04 | 6.70E-04     | 4.03E-03 | 4.02E-03  | **3.09E-04** |
+|                                   | avg_psnr | 33.48    | 33.68        | 32.05    | 32.15     | **35.40**    |
+|                                   | avg_ssim | 93.76%   | 93.75%       | 89.46%   | 89.46%    | **95.46%**   |
+| lerobot/kitchen                   | avg_mse  | 2.50E-04 | 2.24E-04     | 4.28E-04 | 4.18E-04  | **1.53E-04** |
+|                                   | avg_psnr | 36.73    | 37.33        | 36.56    | 36.75     | **39.12**    |
+|                                   | avg_ssim | 95.47%   | 95.58%       | 95.52%   | 95.53%    | **96.82%**   |

docs/source/_toctree.yml

@@ -27,8 +27,6 @@
     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

@@ -1,425 +0,0 @@
-# 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/earthrover_mini_plus.mdx

@@ -185,7 +185,7 @@ echo $HF_USER
 Use the standard recording command:
 
 ```bash
-python src/lerobot/scripts/lerobot_record.py \
+lerobot-record \
     --robot.type=earthrover_mini_plus \
     --teleop.type=keyboard_rover \
     --dataset.repo_id=your_username/dataset_name \

docs/source/hope_jr.mdx

@@ -224,7 +224,7 @@ lerobot-record \
     --teleop.port=/dev/tty.usbmodem1201 \
     --teleop.id=right \
     --teleop.side=right \
-    --dataset.repo_id=nepyope/hand_record_test_with_video_data \
+    --dataset.repo_id=<USER>/hand_record_test_with_video_data \
     --dataset.single_task="Hand recording test with video data" \
     --dataset.num_episodes=1 \
     --dataset.episode_time_s=5 \
@@ -241,7 +241,7 @@ lerobot-replay \
     --robot.port=/dev/tty.usbmodem58760432281 \
     --robot.id=right \
     --robot.side=right \
-    --dataset.repo_id=nepyope/hand_record_test_with_camera \
+    --dataset.repo_id=<USER>/hand_record_test_with_camera \
     --dataset.episode=0
 ```
 
@@ -249,13 +249,13 @@ lerobot-replay \
 
 ```bash
 lerobot-train \
-  --dataset.repo_id=nepyope/hand_record_test_with_video_data \
+  --dataset.repo_id=<USER>/hand_record_test_with_video_data \
   --policy.type=act \
   --output_dir=outputs/train/hopejr_hand \
   --job_name=hopejr \
   --policy.device=mps \
   --wandb.enable=true \
-  --policy.repo_id=nepyope/hand_test_policy
+  --policy.repo_id=<USER>/hand_test_policy
 ```
 
 ### Evaluate
@@ -270,7 +270,7 @@ lerobot-record \
   --robot.side=right \
   --robot.cameras='{"main": {"type": "opencv", "index_or_path": 0, "width": 640, "height": 480, "fps": 30}}' \
   --display_data=false \
-  --dataset.repo_id=nepyope/eval_hopejr \
+  --dataset.repo_id=<USER>/eval_hopejr \
   --dataset.single_task="Evaluate hopejr hand policy" \
   --dataset.num_episodes=10 \
   --policy.path=outputs/train/hopejr_hand/checkpoints/last/pretrained_model

docs/source/installation.mdx

@@ -1,13 +1,15 @@
 # Installation
 
-## Install [`miniforge`](https://conda-forge.org/download/)
+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/)
 
 ```bash
 wget "https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-$(uname)-$(uname -m).sh"
 bash Miniforge3-$(uname)-$(uname -m).sh
 ```
 
-## Environment Setup
+## Step 2: Environment Setup
 
 Create a virtual environment with Python 3.10, using conda:
 
@@ -38,7 +40,7 @@ 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`.
 
-## Install LeRobot 🤗
+## Step 3: Install LeRobot 🤗
 
 ### From Source
 

docs/source/pi0.mdx

@@ -60,7 +60,7 @@ policy.type=pi0
 For training π₀, you can use the standard LeRobot training script with the appropriate configuration:
 
 ```bash
-python src/lerobot/scripts/lerobot_train.py \
+lerobot-train \
     --dataset.repo_id=your_dataset \
     --policy.type=pi0 \
     --output_dir=./outputs/pi0_training \

docs/source/pi05.mdx

@@ -56,7 +56,7 @@ policy.type=pi05
 Here's a complete training command for finetuning the base π₀.₅ model on your own dataset:
 
 ```bash
-python src/lerobot/scripts/lerobot_train.py\
+lerobot-train \
     --dataset.repo_id=your_dataset \
     --policy.type=pi05 \
     --output_dir=./outputs/pi05_training \

docs/source/sarm.mdx

@@ -269,7 +269,7 @@ This generates visualizations showing video frames with subtask boundaries overl
 Train with **no annotations** - uses linear progress from 0 to 1:
 
 ```bash
-python src/lerobot/scripts/lerobot_train.py \
+lerobot-train \
   --dataset.repo_id=your-username/your-dataset \
   --policy.type=sarm \
   --policy.annotation_mode=single_stage \
@@ -288,7 +288,7 @@ python src/lerobot/scripts/lerobot_train.py \
 Train with **dense annotations only** (sparse auto-generated):
 
 ```bash
-python src/lerobot/scripts/lerobot_train.py \
+lerobot-train \
   --dataset.repo_id=your-username/your-dataset \
   --policy.type=sarm \
   --policy.annotation_mode=dense_only \
@@ -307,7 +307,7 @@ python src/lerobot/scripts/lerobot_train.py \
 Train with **both sparse and dense annotations**:
 
 ```bash
-python src/lerobot/scripts/lerobot_train.py \
+lerobot-train \
   --dataset.repo_id=your-username/your-dataset \
   --policy.type=sarm \
   --policy.annotation_mode=dual \
@@ -468,7 +468,7 @@ This script:
 Once you have the progress file, train your policy with RA-BC weighting. The progress file is auto-detected from the dataset path (`sarm_progress.parquet`). Currently PI0, PI0.5 and SmolVLA are supported with RA-BC:
 
 ```bash
-python src/lerobot/scripts/lerobot_train.py \
+lerobot-train \
   --dataset.repo_id=your-username/your-dataset \
   --policy.type=pi0 \
   --use_rabc=true \

docs/source/unitree_g1.mdx

@@ -216,7 +216,7 @@ lerobot-teleoperate \
 ### Record Dataset in Simulation
 
 ```bash
-python -m lerobot.scripts.lerobot_record \
+lerobot-record \
     --robot.type=unitree_g1 \
     --robot.is_simulation=true \
     --robot.cameras='{"global_view": {"type": "zmq", "server_address": "localhost", "port": 5555, "camera_name": "head_camera", "width": 640, "height": 480, "fps": 30}}' \
@@ -266,7 +266,7 @@ lerobot-teleoperate \
 ### Record Dataset on Real Robot
 
 ```bash
-python -m lerobot.scripts.lerobot_record \
+lerobot-record \
     --robot.type=unitree_g1 \
     --robot.is_simulation=false \
     --robot.cameras='{"global_view": {"type": "zmq", "server_address": "172.18.129.215", "port": 5555, "camera_name": "head_camera", "width": 640, "height": 480, "fps": 30}}' \

docs/source/using_dataset_tools.mdx

@@ -12,6 +12,7 @@ LeRobot provides several utilities for manipulating datasets:
 4. **Add Features** - Add new features to a dataset
 5. **Remove Features** - Remove features from a dataset
 6. **Convert to Video** - Convert image-based datasets to video format for efficient storage
+7. **Show the Info of Datasets** - Show the summary of datasets information such as number of episode etc.
 
 The core implementation is in `lerobot.datasets.dataset_tools`.
 An example script detailing how to use the tools API is available in `examples/dataset/use_dataset_tools.py`.
@@ -156,6 +157,30 @@ lerobot-edit-dataset \
 
 **Note:** The resulting dataset will be a proper LeRobotDataset with all cameras encoded as videos in the `videos/` directory, with parquet files containing only metadata (no raw image data). All episodes, stats, and tasks are preserved.
 
+### Show the information of datasets
+
+Show the information of datasets such as number of episode, number of frame, File size and so on.
+No change will be made to the dataset
+
+```bash
+
+# Show dataset information without feature details
+lerobot-edit-dataset \
+    --repo_id lerobot/pusht_image \
+    --operation.type info \
+
+# Show dataset information with feature details
+lerobot-edit-dataset \
+    --repo_id lerobot/pusht_image \
+    --operation.type info \
+    --operation.show_features true
+
+```
+
+**Parameters:**
+
+- `parameters`: The flag to control show or no show dataset information with feature details.(default=false)
+
 ### Push to Hub
 
 Add the `--push_to_hub true` flag to any command to automatically upload the resulting dataset to the Hugging Face Hub:

docs/source/walloss.mdx

@@ -45,7 +45,7 @@ policy.type=wall_x
 For training WallX, you can use the standard LeRobot training script with the appropriate configuration:
 
 ```bash
-python src/lerobot/scripts/lerobot_train.py \
+lerobot-train \
     --dataset.repo_id=your_dataset \
     --policy.type=wall_x \
     --output_dir=./outputs/wallx_training \

docs/source/xvla.mdx

@@ -154,7 +154,7 @@ lerobot-train \
 
 ```bash
 lerobot-train \
-  --dataset.repo_id=pepijn223/bimanual-so100-handover-cube \
+  --dataset.repo_id=<USER>/bimanual-so100-handover-cube \
   --output_dir=./outputs/xvla_bimanual \
   --job_name=xvla_so101_training \
   --policy.path="lerobot/xvla-base" \

examples/backward_compatibility/replay.py

@@ -22,7 +22,7 @@ lerobot-replay \
     --robot.type=so100_follower \
     --robot.port=/dev/tty.usbmodem58760431541 \
     --robot.id=black \
-    --dataset.repo_id=aliberts/record-test \
+    --dataset.repo_id=<USER>/record-test \
     --dataset.episode=2
 ```
 """

examples/port_datasets/slurm_mirror_dataset.py

@@ -0,0 +1,726 @@
+#!/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.
+
+"""
+Mirror a bimanual dataset in parallel with DataTrove + SLURM, then double it.
+
+Workflow:
+1) Split source episodes across `num_shards` ranks and mirror each shard in parallel.
+2) Aggregate mirrored shards into one mirrored dataset.
+3) Aggregate [original, mirrored] into a final doubled dataset.
+
+Example:
+python examples/port_datasets/slurm_mirror_dataset.py \
+  --repo-id=pepijn/openarm_bimanual \
+  --output-repo-id=pepijn/openarm_bimanual_doubled \
+  --partition=hopper-cpu \
+  --num-shards=256 \
+  --workers=64 \
+  --cpus-per-task=8 \
+  --mem-per-cpu=4G
+"""
+
+import argparse
+import copy
+import logging
+import shutil
+from pathlib import Path
+from typing import Any
+
+import numpy as np
+from datatrove.executor import LocalPipelineExecutor
+from datatrove.executor.slurm import SlurmPipelineExecutor
+from datatrove.pipeline.base import PipelineStep
+
+from lerobot.datasets.aggregate import aggregate_datasets
+from lerobot.datasets.lerobot_dataset import LeRobotDataset, LeRobotDatasetMetadata
+from lerobot.datasets.utils import DEFAULT_FEATURES
+from lerobot.utils.constants import HF_LEROBOT_HOME
+from lerobot.utils.utils import init_logging
+
+logger = logging.getLogger(__name__)
+
+
+OPENARM_MIRRORING_MASK = {
+    "joint_1": -1,
+    "joint_2": -1,
+    "joint_3": -1,
+    "joint_4": 1,
+    "joint_5": -1,
+    "joint_6": -1,
+    "joint_7": -1,
+    "gripper": 1,
+}
+
+
+def get_mirroring_mask(robot_type: str | None) -> dict[str, int]:
+    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:
+    value = name.replace("left_", "LEFT_PLACEHOLDER_")
+    value = value.replace("right_", "left_")
+    value = value.replace("LEFT_PLACEHOLDER_", "right_")
+    return value
+
+
+def mirror_feature_names(names: list[str]) -> tuple[list[str], dict[int, int]]:
+    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 _get_axis_names(feature: dict[str, Any]) -> list[str] | None:
+    names = feature.get("names")
+    if isinstance(names, list):
+        return names
+    if isinstance(names, dict):
+        axes = names.get("axes")
+        if isinstance(axes, list):
+            return axes
+    return None
+
+
+def _to_numpy(value: Any) -> Any:
+    if isinstance(value, np.ndarray):
+        return value
+    if hasattr(value, "detach"):
+        return value.detach().cpu().numpy()
+    if hasattr(value, "cpu") and hasattr(value, "numpy"):
+        return value.cpu().numpy()
+    if hasattr(value, "numpy"):
+        return value.numpy()
+    return value
+
+
+def apply_mirroring_mask(value: float, axis_name: str, mirroring_mask: dict[str, int]) -> float:
+    if axis_name.startswith("left_") or axis_name.startswith("right_"):
+        axis_name = axis_name.split("_", 1)[1]
+    joint_name = axis_name.split(".")[0]
+    return value * mirroring_mask.get(joint_name, 1)
+
+
+def mirror_vector_feature(
+    value: Any,
+    feature: dict[str, Any],
+    mirroring_mask: dict[str, int],
+) -> Any:
+    array = _to_numpy(value)
+    if not isinstance(array, np.ndarray) or array.ndim != 1:
+        return array
+
+    names = _get_axis_names(feature)
+    if names is None or len(names) != len(array):
+        return array
+
+    mirrored_names, index_mapping = mirror_feature_names(names)
+    mirrored = np.zeros_like(array)
+    for old_idx, new_idx in index_mapping.items():
+        mirrored[new_idx] = apply_mirroring_mask(array[old_idx], mirrored_names[new_idx], mirroring_mask)
+    return mirrored
+
+
+def flip_horizontal(value: Any, expected_shape: list[int] | tuple[int, ...]) -> Any:
+    array = _to_numpy(value)
+    if not isinstance(array, np.ndarray) or array.ndim != 3:
+        return array
+
+    expected_shape = tuple(expected_shape)
+    if array.shape == expected_shape:
+        return np.flip(array, axis=1).copy()  # HWC
+
+    if len(expected_shape) == 3:
+        c, h, w = expected_shape
+        if array.shape == (c, h, w):
+            return np.flip(array, axis=2).copy()  # CHW
+
+    # Conservative fallback for unexpected layouts.
+    return np.flip(array, axis=-1).copy()
+
+
+def build_mirrored_features(features: dict[str, dict[str, Any]]) -> dict[str, dict[str, Any]]:
+    mirrored = {}
+    for key, feature in features.items():
+        new_key = swap_left_right_name(key)
+        new_feature = copy.deepcopy(feature)
+        names = new_feature.get("names")
+        if isinstance(names, list):
+            new_feature["names"] = [swap_left_right_name(name) for name in names]
+        elif isinstance(names, dict) and isinstance(names.get("axes"), list):
+            new_feature["names"]["axes"] = [swap_left_right_name(name) for name in names["axes"]]
+        mirrored[new_key] = new_feature
+    return mirrored
+
+
+def build_mirrored_frame(
+    item: dict[str, Any],
+    source_features: dict[str, dict[str, Any]],
+    mirroring_mask: dict[str, int],
+) -> dict[str, Any]:
+    frame = {}
+    for key, feature in source_features.items():
+        if key in DEFAULT_FEATURES:
+            continue
+
+        value = item[key]
+        if key in {"action", "observation.state"}:
+            value = mirror_vector_feature(value, feature, mirroring_mask)
+        elif feature["dtype"] in {"video", "image"}:
+            value = flip_horizontal(value, feature["shape"])
+        else:
+            value = _to_numpy(value)
+
+        frame[swap_left_right_name(key)] = value
+
+    frame["task"] = item["task"]
+    if "timestamp" in item:
+        ts = _to_numpy(item["timestamp"])
+        frame["timestamp"] = float(ts.item() if hasattr(ts, "item") else ts)
+    return frame
+
+
+def _resolve_source_root(repo_id: str, root: Path | None) -> Path:
+    source_meta = LeRobotDatasetMetadata(repo_id=repo_id, root=root)
+    return source_meta.root
+
+
+def _get_work_dir(output_repo_id: str, work_dir: Path | None) -> Path:
+    if work_dir is not None:
+        return work_dir
+    safe_name = output_repo_id.replace("/", "__")
+    return HF_LEROBOT_HOME / "_mirror_work" / safe_name
+
+
+def _get_shard_root(work_dir: Path, world_size: int, rank: int) -> Path:
+    return work_dir / "mirrored_shards" / f"world_{world_size}_rank_{rank}"
+
+
+def _is_valid_dataset_root(root: Path) -> bool:
+    return (root / "meta" / "info.json").exists()
+
+
+def mirror_shard(
+    repo_id: str,
+    source_root: Path,
+    mirrored_repo_id: str,
+    shard_root: Path,
+    rank: int,
+    world_size: int,
+    vcodec: str,
+    overwrite: bool,
+) -> None:
+    source_dataset = LeRobotDataset(repo_id=repo_id, root=source_root)
+    selected_episodes = list(range(rank, source_dataset.meta.total_episodes, world_size))
+
+    if len(selected_episodes) == 0:
+        logger.info("Rank %s has no episodes assigned. Skipping.", rank)
+        return
+
+    if shard_root.exists():
+        if overwrite:
+            shutil.rmtree(shard_root)
+        elif _is_valid_dataset_root(shard_root):
+            logger.info("Rank %s shard already exists at %s. Skipping.", rank, shard_root)
+            return
+        else:
+            raise RuntimeError(
+                f"Shard root {shard_root} exists but is not a valid dataset. Use --overwrite to recreate."
+            )
+
+    mirroring_mask = get_mirroring_mask(source_dataset.meta.robot_type)
+    mirrored_features = build_mirrored_features(source_dataset.meta.features)
+
+    shard_repo_name = f"{mirrored_repo_id}_world_{world_size}_rank_{rank}"
+    mirrored_dataset = LeRobotDataset.create(
+        repo_id=shard_repo_name,
+        root=shard_root,
+        fps=source_dataset.meta.fps,
+        features=mirrored_features,
+        robot_type=source_dataset.meta.robot_type,
+        use_videos=len(source_dataset.meta.video_keys) > 0,
+        vcodec=vcodec,
+    )
+    mirrored_dataset.meta.update_chunk_settings(
+        chunks_size=source_dataset.meta.chunks_size,
+        data_files_size_in_mb=source_dataset.meta.data_files_size_in_mb,
+        video_files_size_in_mb=source_dataset.meta.video_files_size_in_mb,
+    )
+
+    logger.info(
+        "Rank %s processing %s episodes into shard %s",
+        rank,
+        len(selected_episodes),
+        shard_root,
+    )
+    for source_ep_idx in selected_episodes:
+        episode = source_dataset.meta.episodes[source_ep_idx]
+        start_idx = int(episode["dataset_from_index"])
+        end_idx = int(episode["dataset_to_index"])
+
+        for frame_idx in range(start_idx, end_idx):
+            item = source_dataset[frame_idx]
+            mirrored_frame = build_mirrored_frame(
+                item=item,
+                source_features=source_dataset.meta.features,
+                mirroring_mask=mirroring_mask,
+            )
+            mirrored_dataset.add_frame(mirrored_frame)
+
+        mirrored_dataset.save_episode()
+
+    mirrored_dataset.finalize()
+
+
+class MirrorDatasetShards(PipelineStep):
+    def __init__(
+        self,
+        repo_id: str,
+        source_root: Path,
+        mirrored_repo_id: str,
+        work_dir: Path,
+        vcodec: str,
+        overwrite: bool,
+    ):
+        super().__init__()
+        self.repo_id = repo_id
+        self.source_root = source_root
+        self.mirrored_repo_id = mirrored_repo_id
+        self.work_dir = work_dir
+        self.vcodec = vcodec
+        self.overwrite = overwrite
+
+    def run(self, data=None, rank: int = 0, world_size: int = 1):
+        init_logging()
+        shard_root = _get_shard_root(self.work_dir, world_size, rank)
+        mirror_shard(
+            repo_id=self.repo_id,
+            source_root=self.source_root,
+            mirrored_repo_id=self.mirrored_repo_id,
+            shard_root=shard_root,
+            rank=rank,
+            world_size=world_size,
+            vcodec=self.vcodec,
+            overwrite=self.overwrite,
+        )
+
+
+def make_mirror_executor(
+    repo_id: str,
+    source_root: Path,
+    mirrored_repo_id: str,
+    work_dir: Path,
+    logs_dir: Path,
+    job_name: str,
+    num_shards: int,
+    workers: int,
+    partition: str,
+    cpus_per_task: int,
+    mem_per_cpu: str,
+    time_limit: str,
+    vcodec: str,
+    overwrite: bool,
+    slurm: bool,
+):
+    kwargs = {
+        "pipeline": [
+            MirrorDatasetShards(
+                repo_id=repo_id,
+                source_root=source_root,
+                mirrored_repo_id=mirrored_repo_id,
+                work_dir=work_dir,
+                vcodec=vcodec,
+                overwrite=overwrite,
+            ),
+        ],
+        "logging_dir": str(logs_dir / job_name),
+    }
+
+    if slurm:
+        if partition is None:
+            raise ValueError("`--partition` is required when `--slurm 1`.")
+        kwargs.update(
+            {
+                "job_name": job_name,
+                "tasks": num_shards,
+                "workers": workers,
+                "time": time_limit,
+                "partition": partition,
+                "cpus_per_task": cpus_per_task,
+                "sbatch_args": {"mem-per-cpu": mem_per_cpu},
+            }
+        )
+        return SlurmPipelineExecutor(**kwargs)
+
+    kwargs.update({"tasks": num_shards, "workers": 1})
+    return LocalPipelineExecutor(**kwargs)
+
+
+class AggregateMirroredShardsStep(PipelineStep):
+    def __init__(
+        self,
+        mirrored_repo_id: str,
+        mirrored_root: Path,
+        work_dir: Path,
+        num_shards: int,
+        overwrite: bool,
+    ):
+        super().__init__()
+        self.mirrored_repo_id = mirrored_repo_id
+        self.mirrored_root = mirrored_root
+        self.work_dir = work_dir
+        self.num_shards = num_shards
+        self.overwrite = overwrite
+
+    def run(self, data=None, rank: int = 0, world_size: int = 1):
+        init_logging()
+        if rank != 0:
+            logger.info("Skipping rank %s for aggregate mirrored step", rank)
+            return
+        aggregate_mirrored_shards(
+            mirrored_repo_id=self.mirrored_repo_id,
+            mirrored_root=self.mirrored_root,
+            work_dir=self.work_dir,
+            num_shards=self.num_shards,
+            overwrite=self.overwrite,
+        )
+
+
+class BuildDoubledDatasetStep(PipelineStep):
+    def __init__(
+        self,
+        source_repo_id: str,
+        source_root: Path,
+        mirrored_repo_id: str,
+        mirrored_root: Path,
+        output_repo_id: str,
+        output_root: Path,
+        overwrite: bool,
+    ):
+        super().__init__()
+        self.source_repo_id = source_repo_id
+        self.source_root = source_root
+        self.mirrored_repo_id = mirrored_repo_id
+        self.mirrored_root = mirrored_root
+        self.output_repo_id = output_repo_id
+        self.output_root = output_root
+        self.overwrite = overwrite
+
+    def run(self, data=None, rank: int = 0, world_size: int = 1):
+        init_logging()
+        if rank != 0:
+            logger.info("Skipping rank %s for build doubled step", rank)
+            return
+        build_doubled_dataset(
+            source_repo_id=self.source_repo_id,
+            source_root=self.source_root,
+            mirrored_repo_id=self.mirrored_repo_id,
+            mirrored_root=self.mirrored_root,
+            output_repo_id=self.output_repo_id,
+            output_root=self.output_root,
+            overwrite=self.overwrite,
+        )
+
+
+class PushDoubledDatasetStep(PipelineStep):
+    def __init__(
+        self,
+        output_repo_id: str,
+        output_root: Path,
+    ):
+        super().__init__()
+        self.output_repo_id = output_repo_id
+        self.output_root = output_root
+
+    def run(self, data=None, rank: int = 0, world_size: int = 1):
+        init_logging()
+        if rank != 0:
+            logger.info("Skipping rank %s for push step", rank)
+            return
+        logger.info("Pushing doubled dataset to hub: %s", self.output_repo_id)
+        LeRobotDataset(self.output_repo_id, root=self.output_root).push_to_hub()
+
+
+def make_single_task_executor(
+    step: PipelineStep,
+    logs_dir: Path,
+    job_name: str,
+    partition: str | None,
+    cpus_per_task: int,
+    mem_per_cpu: str,
+    time_limit: str,
+    slurm: bool,
+    depends: SlurmPipelineExecutor | None = None,
+):
+    kwargs = {"pipeline": [step], "logging_dir": str(logs_dir / job_name)}
+    if slurm:
+        if partition is None:
+            raise ValueError("`--partition` is required when `--slurm 1`.")
+        kwargs.update(
+            {
+                "job_name": job_name,
+                "tasks": 1,
+                "workers": 1,
+                "time": time_limit,
+                "partition": partition,
+                "cpus_per_task": cpus_per_task,
+                "sbatch_args": {"mem-per-cpu": mem_per_cpu},
+                "depends": depends,
+            }
+        )
+        return SlurmPipelineExecutor(**kwargs)
+
+    kwargs.update({"tasks": 1, "workers": 1})
+    return LocalPipelineExecutor(**kwargs)
+
+
+def aggregate_mirrored_shards(
+    mirrored_repo_id: str,
+    mirrored_root: Path,
+    work_dir: Path,
+    num_shards: int,
+    overwrite: bool,
+):
+    if mirrored_root.exists():
+        if overwrite:
+            shutil.rmtree(mirrored_root)
+        elif _is_valid_dataset_root(mirrored_root):
+            logger.info("Mirrored dataset already exists at %s. Skipping aggregation.", mirrored_root)
+            return
+        else:
+            raise RuntimeError(
+                f"Mirrored root {mirrored_root} exists but is not a valid dataset. Use --overwrite to recreate."
+            )
+
+    shard_repo_ids = []
+    shard_roots = []
+    for rank in range(num_shards):
+        shard_root = _get_shard_root(work_dir, num_shards, rank)
+        if _is_valid_dataset_root(shard_root):
+            shard_repo_ids.append(f"{mirrored_repo_id}_world_{num_shards}_rank_{rank}")
+            shard_roots.append(shard_root)
+
+    if len(shard_repo_ids) == 0:
+        raise RuntimeError("No mirrored shards were produced. Nothing to aggregate.")
+
+    logger.info("Aggregating %s mirrored shards into %s", len(shard_repo_ids), mirrored_root)
+    aggregate_datasets(
+        repo_ids=shard_repo_ids,
+        roots=shard_roots,
+        aggr_repo_id=mirrored_repo_id,
+        aggr_root=mirrored_root,
+    )
+
+
+def build_doubled_dataset(
+    source_repo_id: str,
+    source_root: Path,
+    mirrored_repo_id: str,
+    mirrored_root: Path,
+    output_repo_id: str,
+    output_root: Path,
+    overwrite: bool,
+):
+    if output_root.exists():
+        if overwrite:
+            shutil.rmtree(output_root)
+        elif _is_valid_dataset_root(output_root):
+            logger.info("Doubled dataset already exists at %s. Skipping final aggregation.", output_root)
+            return
+        else:
+            raise RuntimeError(
+                f"Output root {output_root} exists but is not a valid dataset. Use --overwrite to recreate."
+            )
+
+    logger.info("Aggregating source + mirrored into doubled dataset at %s", output_root)
+    aggregate_datasets(
+        repo_ids=[source_repo_id, mirrored_repo_id],
+        roots=[source_root, mirrored_root],
+        aggr_repo_id=output_repo_id,
+        aggr_root=output_root,
+    )
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    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="Final doubled dataset repo id.")
+    parser.add_argument("--root", type=Path, default=None, help="Root path of source dataset.")
+    parser.add_argument(
+        "--output-root",
+        type=Path,
+        default=None,
+        help="Root path where final doubled dataset is written.",
+    )
+    parser.add_argument(
+        "--work-dir",
+        type=Path,
+        default=None,
+        help="Intermediate directory for mirrored shards and mirrored aggregate dataset.",
+    )
+    parser.add_argument("--logs-dir", type=Path, required=True, help="DataTrove logs path.")
+    parser.add_argument("--job-name", type=str, default="mirror_dataset", help="SLURM job name.")
+    parser.add_argument("--num-shards", type=int, default=256, help="Number of DataTrove tasks/ranks.")
+    parser.add_argument(
+        "--workers",
+        type=int,
+        default=64,
+        help="Max concurrent DataTrove workers on SLURM.",
+    )
+    parser.add_argument("--partition", type=str, default=None, help="SLURM partition (e.g. hopper-cpu).")
+    parser.add_argument("--cpus-per-task", type=int, default=8, help="CPU count per SLURM task.")
+    parser.add_argument("--mem-per-cpu", type=str, default="4G", help="Memory per CPU for SLURM task.")
+    parser.add_argument("--time", type=str, default="24:00:00", help="SLURM time limit.")
+    parser.add_argument("--vcodec", type=str, default="libsvtav1", help="Video codec for output videos.")
+    parser.add_argument(
+        "--slurm",
+        type=int,
+        default=1,
+        help="Use SLURM executor. Set 0 for local sequential debugging.",
+    )
+    parser.add_argument("--overwrite", action="store_true", help="Delete existing intermediate/final outputs.")
+    parser.add_argument(
+        "--push-to-hub",
+        action="store_true",
+        help="Push final doubled dataset to Hugging Face Hub after completion.",
+    )
+    args = parser.parse_args()
+
+    init_logging()
+    slurm = args.slurm == 1
+
+    source_root = _resolve_source_root(args.repo_id, args.root)
+    output_root = args.output_root if args.output_root is not None else HF_LEROBOT_HOME / args.output_repo_id
+    work_dir = _get_work_dir(args.output_repo_id, args.work_dir)
+    mirrored_repo_id = f"{args.output_repo_id}_mirrored"
+    mirrored_root = work_dir / "mirrored_aggregate"
+
+    work_dir.mkdir(parents=True, exist_ok=True)
+    args.logs_dir.mkdir(parents=True, exist_ok=True)
+
+    mirror_executor = make_mirror_executor(
+        repo_id=args.repo_id,
+        source_root=source_root,
+        mirrored_repo_id=mirrored_repo_id,
+        work_dir=work_dir,
+        logs_dir=args.logs_dir,
+        job_name=args.job_name,
+        num_shards=args.num_shards,
+        workers=args.workers,
+        partition=args.partition,
+        cpus_per_task=args.cpus_per_task,
+        mem_per_cpu=args.mem_per_cpu,
+        time_limit=args.time,
+        vcodec=args.vcodec,
+        overwrite=args.overwrite,
+        slurm=slurm,
+    )
+    if slurm:
+        aggregate_executor = make_single_task_executor(
+            step=AggregateMirroredShardsStep(
+                mirrored_repo_id=mirrored_repo_id,
+                mirrored_root=mirrored_root,
+                work_dir=work_dir,
+                num_shards=args.num_shards,
+                overwrite=args.overwrite,
+            ),
+            logs_dir=args.logs_dir,
+            job_name=f"{args.job_name}_aggregate_mirrored",
+            partition=args.partition,
+            cpus_per_task=args.cpus_per_task,
+            mem_per_cpu=args.mem_per_cpu,
+            time_limit=args.time,
+            slurm=True,
+            depends=mirror_executor,
+        )
+        build_executor = make_single_task_executor(
+            step=BuildDoubledDatasetStep(
+                source_repo_id=args.repo_id,
+                source_root=source_root,
+                mirrored_repo_id=mirrored_repo_id,
+                mirrored_root=mirrored_root,
+                output_repo_id=args.output_repo_id,
+                output_root=output_root,
+                overwrite=args.overwrite,
+            ),
+            logs_dir=args.logs_dir,
+            job_name=f"{args.job_name}_build_doubled",
+            partition=args.partition,
+            cpus_per_task=args.cpus_per_task,
+            mem_per_cpu=args.mem_per_cpu,
+            time_limit=args.time,
+            slurm=True,
+            depends=aggregate_executor,
+        )
+
+        final_executor: SlurmPipelineExecutor | LocalPipelineExecutor = build_executor
+        push_executor = None
+        if args.push_to_hub:
+            push_executor = make_single_task_executor(
+                step=PushDoubledDatasetStep(
+                    output_repo_id=args.output_repo_id,
+                    output_root=output_root,
+                ),
+                logs_dir=args.logs_dir,
+                job_name=f"{args.job_name}_push",
+                partition=args.partition,
+                cpus_per_task=args.cpus_per_task,
+                mem_per_cpu=args.mem_per_cpu,
+                time_limit=args.time,
+                slurm=True,
+                depends=build_executor,
+            )
+            final_executor = push_executor
+
+        final_executor.run()
+        logger.info(
+            "Submitted SLURM chain. job_ids: mirror=%s aggregate=%s doubled=%s push=%s",
+            mirror_executor.job_id,
+            aggregate_executor.job_id,
+            build_executor.job_id,
+            push_executor.job_id if push_executor is not None else None,
+        )
+        return
+
+    mirror_executor.run()
+    aggregate_mirrored_shards(
+        mirrored_repo_id=mirrored_repo_id,
+        mirrored_root=mirrored_root,
+        work_dir=work_dir,
+        num_shards=args.num_shards,
+        overwrite=args.overwrite,
+    )
+    build_doubled_dataset(
+        source_repo_id=args.repo_id,
+        source_root=source_root,
+        mirrored_repo_id=mirrored_repo_id,
+        mirrored_root=mirrored_root,
+        output_repo_id=args.output_repo_id,
+        output_root=output_root,
+        overwrite=args.overwrite,
+    )
+    if args.push_to_hub:
+        logger.info("Pushing doubled dataset to hub: %s", args.output_repo_id)
+        LeRobotDataset(args.output_repo_id, root=output_root).push_to_hub()
+
+
+if __name__ == "__main__":
+    main()

examples/rtc/eval_dataset.py

@@ -27,8 +27,8 @@ measuring consistency and ground truth alignment.
 Usage:
     # Basic usage with smolvla policy
     uv run python examples/rtc/eval_dataset.py \
-        --policy.path=helper2424/smolvla_check_rtc_last3 \
-        --dataset.repo_id=helper2424/check_rtc \
+        --policy.path=<USER>/smolvla_check_rtc_last3 \
+        --dataset.repo_id=<USER>/check_rtc \
         --rtc.execution_horizon=8 \
         --device=mps \
         --rtc.max_guidance_weight=10.0 \
@@ -58,16 +58,16 @@ Usage:
         --device=cuda
 
     uv run python examples/rtc/eval_dataset.py \
-        --policy.path=lipsop/reuben_pi0 \
-        --dataset.repo_id=ReubenLim/so101_cube_in_cup \
+        --policy.path=<USER>/reuben_pi0 \
+        --dataset.repo_id=<USER>/so101_cube_in_cup \
         --rtc.execution_horizon=8 \
         --device=cuda
 
     # With torch.compile for faster inference (PyTorch 2.0+)
     # Note: CUDA graphs disabled by default due to in-place ops in denoising loop
     uv run python examples/rtc/eval_dataset.py \
-        --policy.path=helper2424/smolvla_check_rtc_last3 \
-        --dataset.repo_id=helper2424/check_rtc \
+        --policy.path=<USER>/smolvla_check_rtc_last3 \
+        --dataset.repo_id=<USER>/check_rtc \
         --rtc.execution_horizon=8 \
         --device=mps \
         --use_torch_compile=true \
@@ -75,8 +75,8 @@ Usage:
 
     # With torch.compile on CUDA (CUDA graphs disabled by default)
     uv run python examples/rtc/eval_dataset.py \
-        --policy.path=helper2424/smolvla_check_rtc_last3 \
-        --dataset.repo_id=helper2424/check_rtc \
+        --policy.path=<USER>/smolvla_check_rtc_last3 \
+        --dataset.repo_id=<USER>/check_rtc \
         --rtc.execution_horizon=8 \
         --device=cuda \
         --use_torch_compile=true \
@@ -84,8 +84,8 @@ Usage:
 
     # Enable CUDA graphs (advanced - may cause tensor aliasing errors)
     uv run python examples/rtc/eval_dataset.py \
-        --policy.path=helper2424/smolvla_check_rtc_last3 \
-        --dataset.repo_id=helper2424/check_rtc \
+        --policy.path=<USER>/smolvla_check_rtc_last3 \
+        --dataset.repo_id=<USER>/check_rtc \
         --use_torch_compile=true \
         --torch_compile_backend=inductor \
         --torch_compile_mode=max-autotune \

examples/rtc/eval_with_real_robot.py

@@ -28,7 +28,7 @@ For simulation environments, see eval_with_simulation.py
 Usage:
     # Run RTC with Real robot with RTC
     uv run examples/rtc/eval_with_real_robot.py \
-        --policy.path=helper2424/smolvla_check_rtc_last3 \
+        --policy.path=<USER>/smolvla_check_rtc_last3 \
         --policy.device=mps \
         --rtc.enabled=true \
         --rtc.execution_horizon=20 \
@@ -41,7 +41,7 @@ Usage:
 
     # Run RTC with Real robot without RTC
     uv run examples/rtc/eval_with_real_robot.py \
-        --policy.path=helper2424/smolvla_check_rtc_last3 \
+        --policy.path=<USER>/smolvla_check_rtc_last3 \
         --policy.device=mps \
         --rtc.enabled=false \
         --robot.type=so100_follower \
@@ -53,7 +53,7 @@ Usage:
 
     # Run RTC with Real robot with pi0.5 policy
     uv run examples/rtc/eval_with_real_robot.py \
-        --policy.path=helper2424/pi05_check_rtc \
+        --policy.path=<USER>/pi05_check_rtc \
         --policy.device=mps \
         --rtc.enabled=true \
         --rtc.execution_horizon=20 \

pyproject.toml

@@ -59,7 +59,7 @@ keywords = ["lerobot", "huggingface", "robotics",  "machine learning", "artifici
 dependencies = [
 
     # Hugging Face dependencies
-    "datasets>=4.0.0,<4.2.0",
+    "datasets>=4.0.0,<5.0.0",
     "diffusers>=0.27.2,<0.36.0",
     "huggingface-hub[hf-transfer,cli]>=0.34.2,<0.36.0",
     "accelerate>=1.10.0,<2.0.0",
@@ -76,9 +76,9 @@ dependencies = [
     "pyserial>=3.5,<4.0",
     "wandb>=0.24.0,<0.25.0",
 
-    "torch>=2.2.1,<2.8.0", # TODO: Bumb dependency
-    "torchcodec>=0.2.1,<0.6.0; sys_platform != 'win32' and (sys_platform != 'linux' or (platform_machine != 'aarch64' and platform_machine != 'arm64' and platform_machine != 'armv7l')) and (sys_platform != 'darwin' or platform_machine != 'x86_64')", # TODO: Bumb dependency
-    "torchvision>=0.21.0,<0.23.0", # TODO: Bumb dependency
+    "torch>=2.2.1,<2.11.0", # TODO: Bump dependency
+    "torchcodec>=0.2.1,<0.11.0; sys_platform != 'win32' and (sys_platform != 'linux' or (platform_machine != 'aarch64' and platform_machine != 'arm64' and platform_machine != 'armv7l')) and (sys_platform != 'darwin' or platform_machine != 'x86_64')", # TODO: Bump dependency
+    "torchvision>=0.21.0,<0.26.0", # TODO: Bump dependency
 
     "draccus==0.10.0", # TODO: Remove ==
     "gymnasium>=1.1.1,<2.0.0",
@@ -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, Cv2Rotation
+from .configs import CameraConfig, ColorMode, Cv2Backends, Cv2Rotation
 from .utils import make_cameras_from_configs

src/lerobot/cameras/camera.py

@@ -150,7 +150,7 @@ class Camera(abc.ABC):
         """
         pass
 
-    def read_latest(self, max_age_ms: int = 1000) -> NDArray[Any]:
+    def read_latest(self, max_age_ms: int = 500) -> NDArray[Any]:
         """Return the most recent frame captured immediately (Peeking).
 
         This method is non-blocking and returns whatever is currently in the

src/lerobot/cameras/configs.py

@@ -25,6 +25,10 @@ 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
@@ -32,6 +36,25 @@ 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

@@ -32,10 +32,11 @@ 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.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError
+from lerobot.utils.decorators import check_if_already_connected, check_if_not_connected
+from lerobot.utils.errors import DeviceNotConnectedError
 
 from ..camera import Camera
-from ..utils import get_cv2_backend, get_cv2_rotation
+from ..utils import get_cv2_rotation
 from .configuration_opencv import ColorMode, OpenCVCameraConfig
 
 # NOTE(Steven): The maximum opencv device index depends on your operating system. For instance,
@@ -117,7 +118,7 @@ class OpenCVCamera(Camera):
         self.new_frame_event: Event = Event()
 
         self.rotation: int | None = get_cv2_rotation(config.rotation)
-        self.backend: int = get_cv2_backend()
+        self.backend: int = config.backend
 
         if self.height and self.width:
             self.capture_width, self.capture_height = self.width, self.height
@@ -132,6 +133,7 @@ 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.
@@ -148,8 +150,6 @@ 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.
@@ -178,6 +178,7 @@ 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.
@@ -197,8 +198,6 @@ 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:
@@ -348,6 +347,7 @@ 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.
@@ -374,9 +374,6 @@ 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.")
 
@@ -490,6 +487,7 @@ 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.
@@ -512,8 +510,6 @@ 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.")
@@ -533,7 +529,8 @@ class OpenCVCamera(Camera):
 
         return frame
 
-    def read_latest(self, max_age_ms: int = 1000) -> NDArray[Any]:
+    @check_if_not_connected
+    def read_latest(self, max_age_ms: int = 500) -> NDArray[Any]:
         """Return the most recent frame captured immediately (Peeking).
 
         This method is non-blocking and returns whatever is currently in the
@@ -548,8 +545,6 @@ 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, Cv2Rotation
+from ..configs import CameraConfig, ColorMode, Cv2Backends, Cv2Rotation
 
-__all__ = ["OpenCVCameraConfig", "ColorMode", "Cv2Rotation"]
+__all__ = ["OpenCVCameraConfig", "ColorMode", "Cv2Rotation", "Cv2Backends"]
 
 
 @CameraConfig.register_subclass("opencv")
@@ -50,6 +50,7 @@ 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.
@@ -62,22 +63,12 @@ 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:
-        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."
-            )
+        self.color_mode = ColorMode(self.color_mode)
+        self.rotation = Cv2Rotation(self.rotation)
+        self.backend = Cv2Backends(self.backend)
 
         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

@@ -74,7 +74,4 @@ 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."
             )
 
-        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."
-            )
+        self.color_mode = ColorMode(self.color_mode)

src/lerobot/cameras/reachy2_camera/reachy2_camera.py

@@ -32,6 +32,7 @@ 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:
@@ -123,6 +124,7 @@ 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.
@@ -136,9 +138,6 @@ 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.")
 
@@ -184,6 +183,7 @@ class Reachy2Camera(Camera):
 
         return frame
 
+    @check_if_not_connected
     def async_read(self, timeout_ms: float = 200) -> NDArray[Any]:
         """
         Same as read()
@@ -197,12 +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()
 
-    def read_latest(self, max_age_ms: int = 1000) -> NDArray[Any]:
+    @check_if_not_connected
+    def read_latest(self, max_age_ms: int = 500) -> NDArray[Any]:
         """Return the most recent frame captured immediately (Peeking).
 
         This method is non-blocking and returns whatever is currently in the
@@ -219,8 +218,6 @@ 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.")
@@ -233,6 +230,7 @@ class Reachy2Camera(Camera):
 
         return self.latest_frame
 
+    @check_if_not_connected
     def disconnect(self) -> None:
         """
         Stops the background read thread (if running).
@@ -240,8 +238,6 @@ 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

@@ -30,7 +30,8 @@ try:
 except Exception as e:
     logging.info(f"Could not import realsense: {e}")
 
-from lerobot.utils.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError
+from lerobot.utils.decorators import check_if_already_connected, check_if_not_connected
+from lerobot.utils.errors import DeviceNotConnectedError
 
 from ..camera import Camera
 from ..configs import ColorMode
@@ -152,6 +153,7 @@ 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.
@@ -169,8 +171,6 @@ 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()
@@ -290,6 +290,7 @@ 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.
 
@@ -299,8 +300,6 @@ 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.")
@@ -320,6 +319,7 @@ 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.
@@ -345,9 +345,6 @@ 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.")
 
@@ -374,6 +371,7 @@ 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.
@@ -403,9 +401,6 @@ 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.")
 
@@ -534,6 +529,7 @@ 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.
@@ -556,8 +552,6 @@ 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.")
@@ -578,7 +572,8 @@ class RealSenseCamera(Camera):
         return frame
 
     # NOTE(Steven): Missing implementation for depth for now
-    def read_latest(self, max_age_ms: int = 1000) -> NDArray[Any]:
+    @check_if_not_connected
+    def read_latest(self, max_age_ms: int = 500) -> NDArray[Any]:
         """Return the most recent (color) frame captured immediately (Peeking).
 
         This method is non-blocking and returns whatever is currently in the
@@ -593,8 +588,6 @@ 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

@@ -60,20 +60,8 @@ class RealSenseCameraConfig(CameraConfig):
     warmup_s: int = 1
 
     def __post_init__(self) -> None:
-        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."
-            )
+        self.color_mode = ColorMode(self.color_mode)
+        self.rotation = Cv2Rotation(self.rotation)
 
         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

@@ -14,7 +14,6 @@
 # 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
@@ -68,14 +67,3 @@ 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

@@ -34,7 +34,8 @@ import cv2
 import numpy as np
 from numpy.typing import NDArray
 
-from lerobot.utils.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError
+from lerobot.utils.decorators import check_if_already_connected, check_if_not_connected
+from lerobot.utils.errors import DeviceNotConnectedError
 
 from ..camera import Camera
 from ..configs import ColorMode
@@ -104,6 +105,7 @@ 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.
 
@@ -111,8 +113,6 @@ 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}...")
 
@@ -211,6 +211,7 @@ 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.
@@ -228,9 +229,6 @@ 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.")
 
@@ -301,6 +299,7 @@ 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.
@@ -317,8 +316,6 @@ 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.")
@@ -335,6 +332,7 @@ 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).
 
@@ -350,8 +348,6 @@ 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

@@ -32,10 +32,7 @@ class ZMQCameraConfig(CameraConfig):
     warmup_s: int = 1
 
     def __post_init__(self) -> None:
-        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."
-            )
+        self.color_mode = ColorMode(self.color_mode)
 
         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

@@ -1,50 +0,0 @@
-#!/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

@@ -1,52 +0,0 @@
-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

@@ -1,74 +0,0 @@
-#!/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

@@ -1,561 +0,0 @@
-#!/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/lerobot_dataset.py

@@ -59,7 +59,6 @@ from lerobot.datasets.utils import (
     load_stats,
     load_subtasks,
     load_tasks,
-    load_tasks_high_level,
     update_chunk_file_indices,
     validate_episode_buffer,
     validate_frame,
@@ -164,7 +163,6 @@ 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)
@@ -522,7 +520,6 @@ class LeRobotDatasetMetadata:
         _validate_feature_names(features)
 
         obj.tasks = None
-        obj.tasks_high_level = None
         obj.subtasks = None
         obj.episodes = None
         obj.stats = None
@@ -659,7 +656,7 @@ class LeRobotDataset(torch.utils.data.Dataset):
             repo_id (str): This is the repo id that will be used to fetch the dataset. Locally, the dataset
                 will be stored under root/repo_id.
             root (Path | None, optional): Local directory to use for downloading/writing files. You can also
-                set the LEROBOT_HOME environment variable to point to a different location. Defaults to
+                set the HF_LEROBOT_HOME environment variable to point to a different location. Defaults to
                 '~/.cache/huggingface/lerobot'.
             episodes (list[int] | None, optional): If specified, this will only load episodes specified by
                 their episode_index in this list. Defaults to None.
@@ -1070,17 +1067,7 @@ 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)
-            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
+            video_frames = self._query_videos(query_timestamps, ep_idx)
             item = {**video_frames, **item}
 
         if self.image_transforms is not None:
@@ -1091,14 +1078,6 @@ 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

@@ -62,8 +62,6 @@ 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"
@@ -124,19 +122,9 @@ def load_nested_dataset(
         raise FileNotFoundError(f"Provided directory does not contain any parquet file: {pq_dir}")
 
     with SuppressProgressBars():
-        # When no filtering needed, Dataset uses memory-mapped loading for efficiency
-        # PyArrow loads the entire dataset into memory
-        if episodes is None:
-            return Dataset.from_parquet([str(path) for path in paths], features=features)
-
-        arrow_dataset = pa_ds.dataset(paths, format="parquet")
-        filter_expr = pa_ds.field("episode_index").isin(episodes)
-        table = arrow_dataset.to_table(filter=filter_expr)
-
-        if features is not None:
-            table = table.cast(features.arrow_schema)
-
-        return Dataset(table)
+        # We use .from_parquet() memory-mapped loading for efficiency
+        filters = pa_ds.field("episode_index").isin(episodes) if episodes is not None else None
+        return Dataset.from_parquet([str(path) for path in paths], filters=filters, features=features)
 
 
 def get_parquet_num_frames(parquet_path: str | Path) -> int:
@@ -355,20 +343,6 @@ 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/datasets/v30/convert_dataset_v21_to_v30.py

@@ -529,7 +529,7 @@ if __name__ == "__main__":
         type=str,
         required=True,
         help="Repository identifier on Hugging Face: a community or a user name `/` the name of the dataset "
-        "(e.g. `lerobot/pusht`, `cadene/aloha_sim_insertion_human`).",
+        "(e.g. `lerobot/pusht`, `<USER>/aloha_sim_insertion_human`).",
     )
     parser.add_argument(
         "--branch",

src/lerobot/envs/libero.py

@@ -112,6 +112,7 @@ 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",
@@ -145,7 +146,9 @@ 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._init_state_id = self.episode_index  # tie each sub-env to a fixed init state
+        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._env = self._make_envs_task(task_suite, self.task_id)
         default_steps = 500
@@ -295,7 +298,8 @@ 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])
+            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
 
         # 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.
@@ -373,6 +377,7 @@ 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(groups)
+        self.group_names = list(self.groups)
         self.current_group = self.group_names[0]
 
         if not bus.is_connected:
@@ -230,18 +230,20 @@ 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 groups.values() for m in motors
+            m: bus.read("Present_Position", m, normalize=False)
+            for motors in self.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 groups.values() for m in ms)
+        label_pad = max(self.font.size(m)[0] for ms in self.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(groups[self.current_group]) + 40
+        height = self.base_y + PADDING_Y * len(self.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

@@ -23,6 +23,7 @@ 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:
@@ -36,7 +37,6 @@ 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
 
@@ -155,6 +155,7 @@ 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.
@@ -162,10 +163,6 @@ 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
@@ -211,6 +208,9 @@ 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
 
@@ -246,6 +246,7 @@ 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.
@@ -253,8 +254,6 @@ 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:
@@ -283,6 +282,10 @@ 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)
@@ -341,6 +344,10 @@ 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)
 
@@ -356,6 +363,10 @@ 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 = []
@@ -394,10 +405,13 @@ class DamiaoMotorsBus(MotorsBusBase):
         Returns:
             Dictionary mapping recv_id to CAN message
         """
-        responses = {}
+        responses: dict[int, can.Message] = {}
         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
@@ -461,6 +475,9 @@ 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)
@@ -488,6 +505,9 @@ 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)
@@ -562,10 +582,9 @@ 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)
@@ -595,6 +614,7 @@ 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,
@@ -605,8 +625,6 @@ 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)
@@ -656,6 +674,10 @@ 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)
@@ -678,10 +700,12 @@ class DamiaoMotorsBus(MotorsBusBase):
             else:
                 logger.warning(f"Packet drop: {motor} (ID: 0x{recv_id:02X}). Using last known state.")
 
-    def sync_write(self, data_name: str, values: Value | dict[str, Value]) -> None:
+    @check_if_not_connected
+    def sync_write(self, data_name: str, values: 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():
@@ -690,6 +714,8 @@ 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)
@@ -732,9 +758,9 @@ class DamiaoMotorsBus(MotorsBusBase):
 
     def record_ranges_of_motion(
         self,
-        motors: NameOrID | list[NameOrID] | None = None,
+        motors: str | list[str] | None = None,
         display_values: bool = True,
-    ) -> tuple[dict[NameOrID, Value], dict[NameOrID, Value]]:
+    ) -> tuple[dict[str, Value], dict[str, 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=drive_modes[motor],
-                homing_offset=offsets[motor],
-                range_min=mins[motor],
-                range_max=maxes[motor],
+                drive_mode=int(drive_modes[motor]),
+                homing_offset=int(offsets[motor]),
+                range_min=int(mins[motor]),
+                range_max=int(maxes[motor]),
             )
 
         return calibration
@@ -198,7 +198,7 @@ class DynamixelMotorsBus(SerialMotorsBus):
         if cache:
             self.calibration = calibration_dict
 
-    def disable_torque(self, motors: str | list[str] | None = None, num_retry: int = 0) -> None:
+    def disable_torque(self, motors: int | 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: str | list[str] | None = None, num_retry: int = 0) -> None:
+    def enable_torque(self, motors: int | 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 = {}
+        half_turn_homings: dict[NameOrID, Value] = {}
         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
+            return None
 
         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__(
+        self.port_handler.setPacketTimeout = patch_setPacketTimeout.__get__(  # type: ignore[method-assign]
             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=offsets[motor],
-                range_min=mins[motor],
-                range_max=maxes[motor],
+                homing_offset=int(offsets[motor]),
+                range_min=int(mins[motor]),
+                range_max=int(maxes[motor]),
             )
 
         return calibration
@@ -284,7 +284,7 @@ class FeetechMotorsBus(SerialMotorsBus):
         On Feetech Motors:
         Present_Position = Actual_Position - Homing_Offset
         """
-        half_turn_homings = {}
+        half_turn_homings: dict[NameOrID, Value] = {}
         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: str | list[str] | None = None, num_retry: int = 0) -> None:
+    def disable_torque(self, motors: int | 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: str | list[str] | None = None, num_retry: int = 0) -> None:
+    def enable_torque(self, motors: int | 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 = {}
+        data_list: dict[int, int] = {}
 
         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
+            return None
 
         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

@@ -23,6 +23,7 @@ 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
@@ -93,7 +94,7 @@ class MotorsBusBase(abc.ABC):
         pass
 
     @abc.abstractmethod
-    def sync_write(self, data_name: str, values: Value | dict[str, Value]) -> None:
+    def sync_write(self, data_name: str, values: dict[str, Value]) -> None:
         """Write values to multiple motors."""
         pass
 
@@ -179,15 +180,16 @@ class Motor:
 
 
 class PortHandler(Protocol):
-    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
+    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 openPort(self): ...
     def closePort(self): ...
@@ -240,19 +242,22 @@ 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):
-    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
+    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: PortHandler, ph: PacketHandler, start_address: int, data_length: int
+    ) -> None: ...
     def makeParam(self): ...
     def addParam(self, id): ...
     def removeParam(self, id): ...
@@ -265,15 +270,17 @@ class GroupSyncRead(Protocol):
 
 
 class GroupSyncWrite(Protocol):
-    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
+    port: str
+    ph: PortHandler
+    start_address: int
+    data_length: int
+    is_param_changed: bool
+    param: list
+    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): ...
@@ -400,7 +407,7 @@ class SerialMotorsBus(MotorsBusBase):
         else:
             raise TypeError(f"'{motor}' should be int, str.")
 
-    def _get_motor_model(self, motor: NameOrID) -> int:
+    def _get_motor_model(self, motor: NameOrID) -> str:
         if isinstance(motor, str):
             return self.motors[motor].model
         elif isinstance(motor, int):
@@ -408,17 +415,19 @@ class SerialMotorsBus(MotorsBusBase):
         else:
             raise TypeError(f"'{motor}' should be int, str.")
 
-    def _get_motors_list(self, motors: str | list[str] | None) -> list[str]:
+    def _get_motors_list(self, motors: NameOrID | Sequence[NameOrID] | None) -> list[str]:
         if motors is None:
             return list(self.motors)
         elif isinstance(motors, str):
             return [motors]
-        elif isinstance(motors, list):
-            return motors.copy()
+        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]
         else:
             raise TypeError(motors)
 
-    def _get_ids_values_dict(self, values: Value | dict[str, Value] | None) -> list[str]:
+    def _get_ids_values_dict(self, values: Value | dict[str, Value] | None) -> dict[int, Value]:
         if isinstance(values, (int | float)):
             return dict.fromkeys(self.ids, values)
         elif isinstance(values, dict):
@@ -640,18 +649,19 @@ class SerialMotorsBus(MotorsBusBase):
         pass
 
     @abc.abstractmethod
-    def enable_torque(self, motors: str | list[str] | None = None, num_retry: int = 0) -> None:
+    def enable_torque(self, motors: int | str | list[str] | None = None, num_retry: int = 0) -> None:
         """Enable torque on selected motors.
 
         Args:
-            motor (int): Same semantics as :pymeth:`disable_torque`. Defaults to `None`.
+            motors (int | str | list[str] | None, optional): 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: int | str | list[str] | None = None):
+    def torque_disabled(self, motors: str | list[str] | None = None):
         """Context-manager that guarantees torque is re-enabled.
 
         This helper is useful to temporarily disable torque when configuring motors.
@@ -728,24 +738,19 @@ class SerialMotorsBus(MotorsBusBase):
         """
         pass
 
-    def reset_calibration(self, motors: NameOrID | list[NameOrID] | None = None) -> None:
+    def reset_calibration(self, motors: NameOrID | Sequence[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 | list[NameOrID] | None, optional): Selection of motors. `None` (default)
+            motors (NameOrID | Sequence[NameOrID] | None, optional): Selection of motors. `None` (default)
                 resets every motor.
         """
-        if motors is None:
-            motors = list(self.motors)
-        elif isinstance(motors, (str | int)):
-            motors = [motors]
-        elif not isinstance(motors, list):
-            raise TypeError(motors)
+        motor_names = self._get_motors_list(motors)
 
-        for motor in motors:
+        for motor in motor_names:
             model = self._get_motor_model(motor)
             max_res = self.model_resolution_table[model] - 1
             self.write("Homing_Offset", motor, 0, normalize=False)
@@ -754,7 +759,9 @@ class SerialMotorsBus(MotorsBusBase):
 
         self.calibration = {}
 
-    def set_half_turn_homings(self, motors: NameOrID | list[NameOrID] | None = None) -> dict[NameOrID, Value]:
+    def set_half_turn_homings(
+        self, motors: NameOrID | Sequence[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
@@ -764,17 +771,12 @@ class SerialMotorsBus(MotorsBusBase):
             motors (NameOrID | list[NameOrID] | None, optional): Motors to adjust. Defaults to all motors (`None`).
 
         Returns:
-            dict[NameOrID, Value]: Mapping *motor → written homing offset*.
+            dict[str, Value]: Mapping *motor name → written homing offset*.
         """
-        if motors is None:
-            motors = list(self.motors)
-        elif isinstance(motors, (str | int)):
-            motors = [motors]
-        elif not isinstance(motors, list):
-            raise TypeError(motors)
+        motor_names = self._get_motors_list(motors)
 
-        self.reset_calibration(motors)
-        actual_positions = self.sync_read("Present_Position", motors, normalize=False)
+        self.reset_calibration(motor_names)
+        actual_positions = self.sync_read("Present_Position", motor_names, normalize=False)
         homing_offsets = self._get_half_turn_homings(actual_positions)
         for motor, offset in homing_offsets.items():
             self.write("Homing_Offset", motor, offset)
@@ -786,8 +788,8 @@ class SerialMotorsBus(MotorsBusBase):
         pass
 
     def record_ranges_of_motion(
-        self, motors: NameOrID | list[NameOrID] | None = None, display_values: bool = True
-    ) -> tuple[dict[NameOrID, Value], dict[NameOrID, Value]]:
+        self, motors: NameOrID | Sequence[NameOrID] | None = None, display_values: bool = True
+    ) -> tuple[dict[str, Value], dict[str, 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
@@ -799,30 +801,25 @@ class SerialMotorsBus(MotorsBusBase):
             display_values (bool, optional): When `True` (default) a live table is printed to the console.
 
         Returns:
-            tuple[dict[NameOrID, Value], dict[NameOrID, Value]]: Two dictionaries *mins* and *maxes* with the
+            tuple[dict[str, Value], dict[str, Value]]: Two dictionaries *mins* and *maxes* with the
                 extreme values observed for each motor.
         """
-        if motors is None:
-            motors = list(self.motors)
-        elif isinstance(motors, (str | int)):
-            motors = [motors]
-        elif not isinstance(motors, list):
-            raise TypeError(motors)
+        motor_names = self._get_motors_list(motors)
 
-        start_positions = self.sync_read("Present_Position", motors, normalize=False)
+        start_positions = self.sync_read("Present_Position", motor_names, 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", motors, normalize=False)
+            positions = self.sync_read("Present_Position", motor_names, 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 motors:
+                for motor in motor_names:
                     print(f"{motor:<15} | {mins[motor]:>6} | {positions[motor]:>6} | {maxes[motor]:>6}")
 
             if enter_pressed():
@@ -830,9 +827,9 @@ class SerialMotorsBus(MotorsBusBase):
 
             if display_values and not user_pressed_enter:
                 # Move cursor up to overwrite the previous output
-                move_cursor_up(len(motors) + 3)
+                move_cursor_up(len(motor_names) + 3)
 
-        same_min_max = [motor for motor in motors if mins[motor] == maxes[motor]]
+        same_min_max = [motor for motor in motor_names 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)}")
 
@@ -955,12 +952,12 @@ class SerialMotorsBus(MotorsBusBase):
             if raise_on_error:
                 raise ConnectionError(self.packet_handler.getTxRxResult(comm))
             else:
-                return
+                return None
         if self._is_error(error):
             if raise_on_error:
                 raise RuntimeError(self.packet_handler.getRxPacketError(error))
             else:
-                return
+                return None
 
         return model_number
 
@@ -1007,12 +1004,13 @@ 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)
 
-        id_value = self._decode_sign(data_name, {id_: value})
+        decoded = self._decode_sign(data_name, {id_: value})
 
         if normalize and data_name in self.normalized_data:
-            id_value = self._normalize(id_value)
+            normalized = self._normalize(decoded)
+            return normalized[id_]
 
-        return id_value[id_]
+        return decoded[id_]
 
     def _read(
         self,
@@ -1023,7 +1021,7 @@ class SerialMotorsBus(MotorsBusBase):
         num_retry: int = 0,
         raise_on_error: bool = True,
         err_msg: str = "",
-    ) -> tuple[int, int]:
+    ) -> tuple[int, int, int]:
         if length == 1:
             read_fn = self.packet_handler.read1ByteTxRx
         elif length == 2:
@@ -1073,13 +1071,14 @@ 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:
-            value = self._unnormalize({id_: value})[id_]
+            int_value = self._unnormalize({id_: value})[id_]
 
-        value = self._encode_sign(data_name, {id_: value})[id_]
+        int_value = self._encode_sign(data_name, {id_: int_value})[id_]
 
-        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)
+        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)
 
     def _write(
         self,
@@ -1113,7 +1112,7 @@ class SerialMotorsBus(MotorsBusBase):
     def sync_read(
         self,
         data_name: str,
-        motors: str | list[str] | None = None,
+        motors: NameOrID | Sequence[NameOrID] | None = None,
         *,
         normalize: bool = True,
         num_retry: int = 0,
@@ -1122,7 +1121,7 @@ class SerialMotorsBus(MotorsBusBase):
 
         Args:
             data_name (str): Register name.
-            motors (str | list[str] | None, optional): Motors to query. `None` (default) reads every motor.
+            motors (NameOrID | Sequence[NameOrID] | 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`.
 
@@ -1143,16 +1142,17 @@ 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."
-        ids_values, _ = self._sync_read(
+        raw_ids_values, _ = self._sync_read(
             addr, length, ids, num_retry=num_retry, raise_on_error=True, err_msg=err_msg
         )
 
-        ids_values = self._decode_sign(data_name, ids_values)
+        decoded = self._decode_sign(data_name, raw_ids_values)
 
         if normalize and data_name in self.normalized_data:
-            ids_values = self._normalize(ids_values)
+            normalized = self._normalize(decoded)
+            return {self._id_to_name(id_): value for id_, value in normalized.items()}
 
-        return {self._id_to_name(id_): value for id_, value in ids_values.items()}
+        return {self._id_to_name(id_): value for id_, value in decoded.items()}
 
     def _sync_read(
         self,
@@ -1224,21 +1224,24 @@ class SerialMotorsBus(MotorsBusBase):
             num_retry (int, optional): Retry attempts.  Defaults to `0`.
         """
 
-        ids_values = self._get_ids_values_dict(values)
-        models = [self._id_to_model(id_) for id_ in ids_values]
+        raw_ids_values = self._get_ids_values_dict(values)
+        models = [self._id_to_model(id_) for id_ in raw_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:
-            ids_values = self._unnormalize(ids_values)
+            int_ids_values = self._unnormalize(raw_ids_values)
 
-        ids_values = self._encode_sign(data_name, ids_values)
+        int_ids_values = self._encode_sign(data_name, int_ids_values)
 
-        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)
+        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
+        )
 
     def _sync_write(
         self,

src/lerobot/policies/factory.py

@@ -34,7 +34,6 @@ 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
@@ -391,13 +390,6 @@ 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:

src/lerobot/policies/pi05_full/README.md

@@ -1,49 +0,0 @@
-# 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

@@ -1,21 +0,0 @@
-#!/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

@@ -1,50 +0,0 @@
-#!/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

@@ -1,52 +0,0 @@
-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

@@ -1,49 +0,0 @@
-#!/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

@@ -1,183 +0,0 @@
-#!/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

@@ -1,92 +0,0 @@
-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

@@ -1,194 +0,0 @@
-#!/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/sarm/compute_rabc_weights.py

@@ -27,18 +27,18 @@ Usage:
     # Full RA-BC computation with visualizations
     python src/lerobot/policies/sarm/compute_rabc_weights.py \\
         --dataset-repo-id lerobot/aloha_sim_insertion_human \\
-        --reward-model-path pepijn223/sarm_single_uni4
+        --reward-model-path <USER>/sarm_single_uni4
 
     # Faster computation with stride (compute every 5 frames, interpolate the rest)
     python src/lerobot/policies/sarm/compute_rabc_weights.py \\
         --dataset-repo-id lerobot/aloha_sim_insertion_human \\
-        --reward-model-path pepijn223/sarm_single_uni4 \\
+        --reward-model-path <USER>/sarm_single_uni4 \\
         --stride 5
 
     # Visualize predictions only (no RA-BC computation)
     python src/lerobot/policies/sarm/compute_rabc_weights.py \\
         --dataset-repo-id lerobot/aloha_sim_insertion_human \\
-        --reward-model-path pepijn223/sarm_single_uni4 \\
+        --reward-model-path <USER>/sarm_single_uni4 \\
         --visualize-only \\
         --num-visualizations 5
 
@@ -714,12 +714,12 @@ Examples:
     # Full RA-BC computation with visualizations
     python src/lerobot/policies/sarm/compute_rabc_weights.py \\
         --dataset-repo-id lerobot/aloha_sim_insertion_human \\
-        --reward-model-path pepijn223/sarm_single_uni4
+        --reward-model-path <USER>/sarm_single_uni4
 
     # Visualize predictions only (no RA-BC computation)
     python src/lerobot/policies/sarm/compute_rabc_weights.py \\
         --dataset-repo-id lerobot/aloha_sim_insertion_human \\
-        --reward-model-path pepijn223/sarm_single_uni4 \\
+        --reward-model-path <USER>/sarm_single_uni4 \\
         --visualize-only \\
         --num-visualizations 10
         """,

src/lerobot/policies/smolvla/modeling_smolvla.py

@@ -30,7 +30,7 @@ Example of finetuning the smolvla pretrained model (`smolvla_base`):
 ```bash
 lerobot-train \
 --policy.path=lerobot/smolvla_base \
---dataset.repo_id=danaaubakirova/svla_so100_task1_v3 \
+--dataset.repo_id=<USER>/svla_so100_task1_v3 \
 --batch_size=64 \
 --steps=200000
 ```
@@ -40,7 +40,7 @@ and an action expert.
 ```bash
 lerobot-train \
 --policy.type=smolvla \
---dataset.repo_id=danaaubakirova/svla_so100_task1_v3 \
+--dataset.repo_id=<USER>/svla_so100_task1_v3 \
 --batch_size=64 \
 --steps=200000
 ```
@@ -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()
+        loss_dict["losses_after_forward"] = losses.clone().mean().item()
 
         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()
+            loss_dict["losses_after_in_ep_bound"] = losses.clone().mean().item()
 
         # Remove padding
         losses = losses[:, :, : self.config.max_action_dim]
-        loss_dict["losses_after_rm_padding"] = losses.clone()
+        loss_dict["losses_after_rm_padding"] = losses.clone().mean().item()
 
         if reduction == "none":
             # Return per-sample losses (B,) by averaging over time and action dims

src/lerobot/processor/__init__.py

@@ -44,6 +44,7 @@ from .hil_processor import (
     AddTeleopActionAsComplimentaryDataStep,
     AddTeleopEventsAsInfoStep,
     GripperPenaltyProcessorStep,
+    GymHILAdapterProcessorStep,
     ImageCropResizeProcessorStep,
     InterventionActionProcessorStep,
     RewardClassifierProcessorStep,
@@ -87,6 +88,7 @@ __all__ = [
     "DoneProcessorStep",
     "EnvAction",
     "EnvTransition",
+    "GymHILAdapterProcessorStep",
     "GripperPenaltyProcessorStep",
     "hotswap_stats",
     "IdentityProcessorStep",

src/lerobot/processor/converters.py

@@ -171,11 +171,9 @@ 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, **index_key, **task_index_key, **episode_index_key, **user_prompt_key, **subtask_key}
+    return {**pad_keys, **task_key, **subtask_key, **index_key, **task_index_key, **episode_index_key}
 
 
 def create_transition(

src/lerobot/processor/env_processor.py

@@ -17,7 +17,7 @@ from dataclasses import dataclass
 
 import torch
 
-from lerobot.configs.types import PipelineFeatureType, PolicyFeature
+from lerobot.configs.types import FeatureType, 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 != PipelineFeatureType.STATE:
+            if ft != FeatureType.STATE:
                 new_features[ft] = feats.copy()
 
         # rebuild STATE features
@@ -100,13 +100,11 @@ class LiberoProcessorStep(ObservationProcessorStep):
 
         # add our new flattened state
         state_feats[OBS_STATE] = PolicyFeature(
-            key=OBS_STATE,
+            type=FeatureType.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[PipelineFeatureType.STATE] = state_feats
+        new_features[FeatureType.STATE] = state_feats
 
         return new_features
 

src/lerobot/processor/gym_action_processor.py

@@ -20,6 +20,7 @@ from lerobot.configs.types import PipelineFeatureType, PolicyFeature
 
 from .converters import to_tensor
 from .core import EnvAction, EnvTransition, PolicyAction
+from .hil_processor import TELEOP_ACTION_KEY
 from .pipeline import ActionProcessorStep, ProcessorStep, ProcessorStepRegistry
 
 
@@ -89,6 +90,13 @@ class Numpy2TorchActionProcessorStep(ProcessorStep):
             torch_action = to_tensor(action, dtype=None)  # Preserve original dtype
             new_transition[TransitionKey.ACTION] = torch_action
 
+        complementary_data = new_transition.get(TransitionKey.COMPLEMENTARY_DATA, {})
+        if TELEOP_ACTION_KEY in complementary_data:
+            teleop_action = complementary_data[TELEOP_ACTION_KEY]
+            if isinstance(teleop_action, EnvAction):
+                complementary_data[TELEOP_ACTION_KEY] = to_tensor(teleop_action)
+            new_transition[TransitionKey.COMPLEMENTARY_DATA] = complementary_data
+
         return new_transition
 
     def transform_features(

src/lerobot/processor/hil_processor.py

@@ -312,6 +312,37 @@ class TimeLimitProcessorStep(TruncatedProcessorStep):
         return features
 
 
+@ProcessorStepRegistry.register("gym_hil_adapter_processor")
+class GymHILAdapterProcessorStep(ProcessorStep):
+    """
+    Adapts the output of the `gym-hil` environment to the format expected by `lerobot` processors.
+
+    This step normalizes the `transition` object by:
+    1. Copying `teleop_action` from `info` to `complementary_data`.
+    2. Copying `is_intervention` from `info` (using the string key) to `info` (using the enum key).
+    """
+
+    def __call__(self, transition: EnvTransition) -> EnvTransition:
+        info = transition.get(TransitionKey.INFO, {})
+        complementary_data = transition.get(TransitionKey.COMPLEMENTARY_DATA, {})
+
+        if TELEOP_ACTION_KEY in info:
+            complementary_data[TELEOP_ACTION_KEY] = info[TELEOP_ACTION_KEY]
+
+        if "is_intervention" in info:
+            info[TeleopEvents.IS_INTERVENTION] = info["is_intervention"]
+
+        transition[TransitionKey.INFO] = info
+        transition[TransitionKey.COMPLEMENTARY_DATA] = complementary_data
+
+        return transition
+
+    def transform_features(
+        self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]]
+    ) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]:
+        return features
+
+
 @dataclass
 @ProcessorStepRegistry.register("gripper_penalty_processor")
 class GripperPenaltyProcessorStep(ProcessorStep):

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

@@ -37,9 +37,6 @@ 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
 
@@ -144,32 +141,6 @@ 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.
@@ -198,16 +169,16 @@ class TokenizerProcessorStep(ObservationProcessorStep):
 
     def observation(self, observation: RobotObservation) -> RobotObservation:
         """
-        Tokenizes the task description and user_prompt (if available) and adds them to the observation dictionary.
+        Tokenizes the task description and adds it to the observation dictionary.
 
-        This method retrieves the task and user_prompt, tokenizes them, moves the resulting tensors to the
+        This method retrieves the task, tokenizes it, 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 attention masks.
+            The updated observation dictionary including token IDs and an attention mask.
         """
         task = self.get_task(self.transition)
         if task is None:
@@ -233,45 +204,11 @@ 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 = {
@@ -383,28 +320,6 @@ 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
 
 
@@ -658,4 +573,4 @@ class ActionTokenizerProcessorStep(ActionProcessorStep):
         Returns:
             The updated dictionary of policy features.
         """
-        return features
+        return features

src/lerobot/rl/gym_manipulator.py

@@ -36,6 +36,7 @@ from lerobot.processor import (
     DeviceProcessorStep,
     EnvTransition,
     GripperPenaltyProcessorStep,
+    GymHILAdapterProcessorStep,
     ImageCropResizeProcessorStep,
     InterventionActionProcessorStep,
     MapDeltaActionToRobotActionStep,
@@ -379,6 +380,7 @@ def make_processors(
         ]
 
         env_pipeline_steps = [
+            GymHILAdapterProcessorStep(),
             Numpy2TorchActionProcessorStep(),
             VanillaObservationProcessorStep(),
             AddBatchDimensionProcessorStep(),
@@ -608,7 +610,14 @@ def control_loop(
 
     dataset = None
     if cfg.mode == "record":
-        action_features = teleop_device.action_features
+        if teleop_device:
+            action_features = teleop_device.action_features
+        else:
+            action_features = {
+                "dtype": "float32",
+                "shape": (4,),
+                "names": ["delta_x", "delta_y", "delta_z", "gripper"],
+            }
         features = {
             ACTION: action_features,
             REWARD: {"dtype": "float32", "shape": (1,), "names": None},
@@ -656,7 +665,7 @@ def control_loop(
         # Create a neutral action (no movement)
         neutral_action = torch.tensor([0.0, 0.0, 0.0], dtype=torch.float32)
         if use_gripper:
-            neutral_action = torch.cat([neutral_action, torch.tensor([1.0])])  # Gripper stay
+            neutral_action = torch.cat([neutral_action, torch.tensor([0.0])])  # Gripper stay
 
         # Use the new step function
         transition = step_env_and_process_transition(
@@ -725,6 +734,8 @@ def control_loop(
         precise_sleep(max(dt - (time.perf_counter() - step_start_time), 0.0))
 
     if dataset is not None and cfg.dataset.push_to_hub:
+        logging.info("Finalizing dataset before pushing to hub")
+        dataset.finalize()
         logging.info("Pushing dataset to hub")
         dataset.push_to_hub()
 

src/lerobot/robots/bi_openarm_follower/bi_openarm_follower.py

@@ -19,6 +19,7 @@ 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
@@ -112,6 +113,7 @@ 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)
@@ -133,6 +135,7 @@ 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 = {}
 
@@ -146,6 +149,7 @@ class BiOpenArmFollower(Robot):
 
         return obs_dict
 
+    @check_if_not_connected
     def send_action(
         self,
         action: RobotAction,
@@ -170,6 +174,7 @@ 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

@@ -19,6 +19,7 @@ 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
@@ -96,6 +97,7 @@ 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)
@@ -116,6 +118,7 @@ class BiSOFollower(Robot):
         self.left_arm.setup_motors()
         self.right_arm.setup_motors()
 
+    @check_if_not_connected
     def get_observation(self) -> RobotObservation:
         obs_dict = {}
 
@@ -129,6 +132,7 @@ class BiSOFollower(Robot):
 
         return obs_dict
 
+    @check_if_not_connected
     def send_action(self, action: RobotAction) -> RobotAction:
         # Remove "left_" prefix
         left_action = {
@@ -148,6 +152,7 @@ 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/hope_jr/hope_jr_arm.py

@@ -140,7 +140,7 @@ class HopeJrArm(Robot):
         # Capture images from cameras
         for cam_key, cam in self.cameras.items():
             start = time.perf_counter()
-            obs_dict[cam_key] = cam.async_read()
+            obs_dict[cam_key] = cam.read_latest()
             dt_ms = (time.perf_counter() - start) * 1e3
             logger.debug(f"{self} read {cam_key}: {dt_ms:.1f}ms")
 

src/lerobot/robots/hope_jr/hope_jr_hand.py

@@ -171,7 +171,7 @@ class HopeJrHand(Robot):
         # Capture images from cameras
         for cam_key, cam in self.cameras.items():
             start = time.perf_counter()
-            obs_dict[cam_key] = cam.async_read()
+            obs_dict[cam_key] = cam.read_latest()
             dt_ms = (time.perf_counter() - start) * 1e3
             logger.debug(f"{self} read {cam_key}: {dt_ms:.1f}ms")
 

src/lerobot/robots/koch_follower/koch_follower.py

@@ -193,7 +193,7 @@ class KochFollower(Robot):
         # Capture images from cameras
         for cam_key, cam in self.cameras.items():
             start = time.perf_counter()
-            obs_dict[cam_key] = cam.async_read()
+            obs_dict[cam_key] = cam.read_latest()
             dt_ms = (time.perf_counter() - start) * 1e3
             logger.debug(f"{self} read {cam_key}: {dt_ms:.1f}ms")
 

src/lerobot/robots/lekiwi/lekiwi.py

@@ -360,7 +360,7 @@ class LeKiwi(Robot):
         # Capture images from cameras
         for cam_key, cam in self.cameras.items():
             start = time.perf_counter()
-            obs_dict[cam_key] = cam.async_read()
+            obs_dict[cam_key] = cam.read_latest()
             dt_ms = (time.perf_counter() - start) * 1e3
             logger.debug(f"{self} read {cam_key}: {dt_ms:.1f}ms")
 

src/lerobot/robots/omx_follower/omx_follower.py

@@ -176,7 +176,7 @@ class OmxFollower(Robot):
         # Capture images from cameras
         for cam_key, cam in self.cameras.items():
             start = time.perf_counter()
-            obs_dict[cam_key] = cam.async_read()
+            obs_dict[cam_key] = cam.read_latest()
             dt_ms = (time.perf_counter() - start) * 1e3
             logger.debug(f"{self} read {cam_key}: {dt_ms:.1f}ms")
 

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.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError
+from lerobot.utils.decorators import check_if_already_connected, check_if_not_connected
 
 from ..robot import Robot
 from ..utils import ensure_safe_goal_position
@@ -119,6 +119,7 @@ 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.
@@ -126,8 +127,6 @@ 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}...")
@@ -219,6 +218,7 @@ 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.
@@ -228,9 +228,6 @@ 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()
@@ -244,7 +241,7 @@ class OpenArmFollower(Robot):
         # Capture images from cameras
         for cam_key, cam in self.cameras.items():
             start = time.perf_counter()
-            obs_dict[cam_key] = cam.async_read()
+            obs_dict[cam_key] = cam.read_latest()
             dt_ms = (time.perf_counter() - start) * 1e3
             logger.debug(f"{self} read {cam_key}: {dt_ms:.1f}ms")
 
@@ -253,6 +250,7 @@ class OpenArmFollower(Robot):
 
         return obs_dict
 
+    @check_if_not_connected
     def send_action(
         self,
         action: RobotAction,
@@ -272,8 +270,6 @@ 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")}
 
@@ -333,10 +329,9 @@ 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/reachy2/robot_reachy2.py

@@ -180,7 +180,7 @@ class Reachy2Robot(Robot):
 
         # Capture images from cameras
         for cam_key, cam in self.cameras.items():
-            obs_dict[cam_key] = cam.async_read()
+            obs_dict[cam_key] = cam.read_latest()
 
         return obs_dict
 

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 = False
+    use_degrees: bool = True
 
 
 @RobotConfig.register_subclass("so101_follower")

src/lerobot/robots/so_follower/so_follower.py

@@ -187,7 +187,7 @@ class SOFollower(Robot):
         # Capture images from cameras
         for cam_key, cam in self.cameras.items():
             start = time.perf_counter()
-            obs_dict[cam_key] = cam.async_read()
+            obs_dict[cam_key] = cam.read_latest()
             dt_ms = (time.perf_counter() - start) * 1e3
             logger.debug(f"{self} read {cam_key}: {dt_ms:.1f}ms")
 

src/lerobot/robots/unitree_g1/unitree_g1.py

@@ -324,7 +324,7 @@ class UnitreeG1(Robot):
 
         # Cameras - read images from ZMQ cameras
         for cam_name, cam in self._cameras.items():
-            obs[cam_name] = cam.async_read()
+            obs[cam_name] = cam.read_latest()
 
         return obs
 

src/lerobot/scripts/lerobot_dataset_viz.py

@@ -47,16 +47,14 @@ local$ rerun lerobot_pusht_episode_0.rrd
 ```
 
 - Visualize data stored on a distant machine through streaming:
-(You need to forward the websocket port to the distant machine, with
-`ssh -L 9087:localhost:9087 username@remote-host`)
 ```
 distant$ lerobot-dataset-viz \
     --repo-id lerobot/pusht \
     --episode-index 0 \
     --mode distant \
-    --ws-port 9087
+    --grpc-port 9876
 
-local$ rerun ws://localhost:9087
+local$ rerun rerun+http://IP:GRPC_PORT/proxy
 ```
 
 """
@@ -75,6 +73,7 @@ import tqdm
 
 from lerobot.datasets.lerobot_dataset import LeRobotDataset
 from lerobot.utils.constants import ACTION, DONE, OBS_STATE, REWARD
+from lerobot.utils.utils import init_logging
 
 
 def to_hwc_uint8_numpy(chw_float32_torch: torch.Tensor) -> np.ndarray:
@@ -93,10 +92,11 @@ def visualize_dataset(
     num_workers: int = 0,
     mode: str = "local",
     web_port: int = 9090,
-    ws_port: int = 9087,
+    grpc_port: int = 9876,
     save: bool = False,
     output_dir: Path | None = None,
     display_compressed_images: bool = False,
+    **kwargs,
 ) -> Path | None:
     if save:
         assert output_dir is not None, (
@@ -126,7 +126,9 @@ def visualize_dataset(
     gc.collect()
 
     if mode == "distant":
-        rr.serve_web_viewer(open_browser=False, web_port=web_port)
+        server_uri = rr.serve_grpc(grpc_port=grpc_port)
+        logging.info(f"Connect to a Rerun Server: rerun rerun+http://IP:{grpc_port}/proxy")
+        rr.serve_web_viewer(open_browser=False, web_port=web_port, connect_to=server_uri)
 
     logging.info("Logging to Rerun")
 
@@ -226,7 +228,7 @@ def main():
             "Mode of viewing between 'local' or 'distant'. "
             "'local' requires data to be on a local machine. It spawns a viewer to visualize the data locally. "
             "'distant' creates a server on the distant machine where the data is stored. "
-            "Visualize the data by connecting to the server with `rerun ws://localhost:PORT` on the local machine."
+            "Visualize the data by connecting to the server with `rerun rerun+http://IP:GRPC_PORT/proxy` on the local machine."
         ),
     )
     parser.add_argument(
@@ -238,8 +240,13 @@ def main():
     parser.add_argument(
         "--ws-port",
         type=int,
-        default=9087,
-        help="Web socket port for rerun.io when `--mode distant` is set.",
+        help="deprecated, please use --grpc-port instead.",
+    )
+    parser.add_argument(
+        "--grpc-port",
+        type=int,
+        default=9876,
+        help="gRPC port for rerun.io when `--mode distant` is set.",
     )
     parser.add_argument(
         "--save",
@@ -265,9 +272,7 @@ def main():
 
     parser.add_argument(
         "--display-compressed-images",
-        type=bool,
-        required=True,
-        default=False,
+        action="store_true",
         help="If set, display compressed images in Rerun instead of uncompressed ones.",
     )
 
@@ -277,6 +282,14 @@ def main():
     root = kwargs.pop("root")
     tolerance_s = kwargs.pop("tolerance_s")
 
+    if kwargs["ws_port"] is not None:
+        logging.warning(
+            "--ws-port is deprecated and will be removed in future versions. Please use --grpc-port instead."
+        )
+        logging.warning("Setting grpc_port to ws_port value.")
+        kwargs["grpc_port"] = kwargs.pop("ws_port")
+
+    init_logging()
     logging.info("Loading dataset")
     dataset = LeRobotDataset(repo_id, episodes=[args.episode_index], root=root, tolerance_s=tolerance_s)
 

src/lerobot/scripts/lerobot_edit_dataset.py

@@ -24,96 +24,112 @@ When new_repo_id is specified, creates a new dataset.
 Usage Examples:
 
 Delete episodes 0, 2, and 5 from a dataset:
-    python -m lerobot.scripts.lerobot_edit_dataset \
+    lerobot-edit-dataset \
         --repo_id lerobot/pusht \
         --operation.type delete_episodes \
         --operation.episode_indices "[0, 2, 5]"
 
 Delete episodes and save to a new dataset:
-    python -m lerobot.scripts.lerobot_edit_dataset \
+    lerobot-edit-dataset \
         --repo_id lerobot/pusht \
         --new_repo_id lerobot/pusht_filtered \
         --operation.type delete_episodes \
         --operation.episode_indices "[0, 2, 5]"
 
 Split dataset by fractions:
-    python -m lerobot.scripts.lerobot_edit_dataset \
+    lerobot-edit-dataset \
         --repo_id lerobot/pusht \
         --operation.type split \
         --operation.splits '{"train": 0.8, "val": 0.2}'
 
 Split dataset by episode indices:
-    python -m lerobot.scripts.lerobot_edit_dataset \
+    lerobot-edit-dataset \
         --repo_id lerobot/pusht \
         --operation.type split \
         --operation.splits '{"train": [0, 1, 2, 3], "val": [4, 5]}'
 
 Split into more than two splits:
-    python -m lerobot.scripts.lerobot_edit_dataset \
+    lerobot-edit-dataset \
         --repo_id lerobot/pusht \
         --operation.type split \
         --operation.splits '{"train": 0.6, "val": 0.2, "test": 0.2}'
 
 Merge multiple datasets:
-    python -m lerobot.scripts.lerobot_edit_dataset \
+    lerobot-edit-dataset \
         --repo_id lerobot/pusht_merged \
         --operation.type merge \
         --operation.repo_ids "['lerobot/pusht_train', 'lerobot/pusht_val']"
 
 Remove camera feature:
-    python -m lerobot.scripts.lerobot_edit_dataset \
+    lerobot-edit-dataset \
         --repo_id lerobot/pusht \
         --operation.type remove_feature \
         --operation.feature_names "['observation.images.top']"
 
 Modify tasks - set a single task for all episodes (WARNING: modifies in-place):
-    python -m lerobot.scripts.lerobot_edit_dataset \
+    lerobot-edit-dataset \
         --repo_id lerobot/pusht \
         --operation.type modify_tasks \
         --operation.new_task "Pick up the cube and place it"
 
 Modify tasks - set different tasks for specific episodes (WARNING: modifies in-place):
-    python -m lerobot.scripts.lerobot_edit_dataset \
+    lerobot-edit-dataset \
         --repo_id lerobot/pusht \
         --operation.type modify_tasks \
         --operation.episode_tasks '{"0": "Task A", "1": "Task B", "2": "Task A"}'
 
 Modify tasks - set default task with overrides for specific episodes (WARNING: modifies in-place):
-    python -m lerobot.scripts.lerobot_edit_dataset \
+    lerobot-edit-dataset \
         --repo_id lerobot/pusht \
         --operation.type modify_tasks \
         --operation.new_task "Default task" \
         --operation.episode_tasks '{"5": "Special task for episode 5"}'
 
 Convert image dataset to video format and save locally:
-    python -m lerobot.scripts.lerobot_edit_dataset \
+    lerobot-edit-dataset \
         --repo_id lerobot/pusht_image \
         --operation.type convert_image_to_video \
         --operation.output_dir /path/to/output/pusht_video
 
 Convert image dataset to video format and save with new repo_id:
-    python -m lerobot.scripts.lerobot_edit_dataset \
+    lerobot-edit-dataset \
         --repo_id lerobot/pusht_image \
         --new_repo_id lerobot/pusht_video \
         --operation.type convert_image_to_video
 
 Convert image dataset to video format and push to hub:
-    python -m lerobot.scripts.lerobot_edit_dataset \
+    lerobot-edit-dataset \
         --repo_id lerobot/pusht_image \
         --new_repo_id lerobot/pusht_video \
         --operation.type convert_image_to_video \
         --push_to_hub true
 
+Show dataset information:
+    lerobot-edit-dataset \
+        --repo_id lerobot/pusht_image \
+        --operation.type info \
+        --operation.show_features true
+
+Show dataset information without feature details:
+    lerobot-edit-dataset \
+        --repo_id lerobot/pusht_image \
+        --operation.type info \
+        --operation.show_features false
+
 Using JSON config file:
-    python -m lerobot.scripts.lerobot_edit_dataset \
+    lerobot-edit-dataset \
         --config_path path/to/edit_config.json
 """
 
+import abc
 import logging
 import shutil
+import sys
 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,
@@ -129,39 +145,46 @@ from lerobot.utils.utils import init_logging
 
 
 @dataclass
-class DeleteEpisodesConfig:
-    type: str = "delete_episodes"
+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):
     episode_indices: list[int] | None = None
 
 
+@OperationConfig.register_subclass("split")
 @dataclass
-class SplitConfig:
-    type: str = "split"
+class SplitConfig(OperationConfig):
     splits: dict[str, float | list[int]] | None = None
 
 
+@OperationConfig.register_subclass("merge")
 @dataclass
-class MergeConfig:
-    type: str = "merge"
+class MergeConfig(OperationConfig):
     repo_ids: list[str] | None = None
 
 
+@OperationConfig.register_subclass("remove_feature")
 @dataclass
-class RemoveFeatureConfig:
-    type: str = "remove_feature"
+class RemoveFeatureConfig(OperationConfig):
     feature_names: list[str] | None = None
 
 
+@OperationConfig.register_subclass("modify_tasks")
 @dataclass
-class ModifyTasksConfig:
-    type: str = "modify_tasks"
+class ModifyTasksConfig(OperationConfig):
     new_task: str | None = None
     episode_tasks: dict[str, str] | None = None
 
 
+@OperationConfig.register_subclass("convert_image_to_video")
 @dataclass
-class ConvertImageToVideoConfig:
-    type: str = "convert_image_to_video"
+class ConvertImageToVideoConfig(OperationConfig):
     output_dir: str | None = None
     vcodec: str = "libsvtav1"
     pix_fmt: str = "yuv420p"
@@ -174,17 +197,17 @@ class ConvertImageToVideoConfig:
     max_frames_per_batch: int | None = None
 
 
+@OperationConfig.register_subclass("info")
+@dataclass
+class InfoConfig(OperationConfig):
+    type: str = "info"
+    show_features: bool = False
+
+
 @dataclass
 class EditDatasetConfig:
     repo_id: str
-    operation: (
-        DeleteEpisodesConfig
-        | SplitConfig
-        | MergeConfig
-        | RemoveFeatureConfig
-        | ModifyTasksConfig
-        | ConvertImageToVideoConfig
-    )
+    operation: OperationConfig
     root: str | None = None
     new_repo_id: str | None = None
     push_to_hub: bool = False
@@ -433,6 +456,49 @@ def handle_convert_image_to_video(cfg: EditDatasetConfig) -> None:
         logging.info("Dataset saved locally (not pushed to hub)")
 
 
+def _get_dataset_size(repo_path):
+    import os
+
+    total = 0
+    with os.scandir(repo_path) as it:
+        for entry in it:
+            if entry.is_file():
+                total += entry.stat().st_size
+            elif entry.is_dir():
+                total += _get_dataset_size(entry.path)
+    return total
+
+
+def handle_info(cfg: EditDatasetConfig):
+    if not isinstance(cfg.operation, InfoConfig):
+        raise ValueError("Operation config must be InfoConfig")
+
+    dataset = LeRobotDataset(cfg.repo_id, root=cfg.root)
+    sys.stdout.write(f"======Info {dataset.meta.repo_id}\n")
+    sys.stdout.write(f"Repository ID: {dataset.meta.repo_id} \n")
+    sys.stdout.write(f"Total episode: {dataset.meta.total_episodes} \n")
+    sys.stdout.write(f"Total task: {dataset.meta.total_tasks} \n")
+    sys.stdout.write(f"Total frame(Actual Count): {dataset.meta.total_frames}({len(dataset)}) \n")
+    sys.stdout.write(
+        f"Average frame per episode: {dataset.meta.total_frames / dataset.meta.total_episodes:.1f}\n"
+    )
+    sys.stdout.write(
+        f"Average episode time(sec): {(dataset.meta.total_frames / dataset.meta.total_episodes) / dataset.meta.fps:.1f}\n"
+    )
+    sys.stdout.write(f"FPS: {dataset.meta.fps}\n")
+
+    total_file_size = _get_dataset_size(dataset.root)
+    sys.stdout.write(f"Size: {total_file_size / (1024 * 1024):.1f} MB\n")
+    if cfg.operation.show_features:
+        import json
+
+        feature_dump_str = json.dumps(
+            dataset.meta.features, ensure_ascii=False, indent=4, sort_keys=True, separators=(",", ": ")
+        )
+        sys.stdout.write("Features:\n")
+        sys.stdout.write(f"{feature_dump_str}\n")
+
+
 @parser.wrap()
 def edit_dataset(cfg: EditDatasetConfig) -> None:
     operation_type = cfg.operation.type
@@ -449,11 +515,11 @@ def edit_dataset(cfg: EditDatasetConfig) -> None:
         handle_modify_tasks(cfg)
     elif operation_type == "convert_image_to_video":
         handle_convert_image_to_video(cfg)
+    elif operation_type == "info":
+        handle_info(cfg)
     else:
-        raise ValueError(
-            f"Unknown operation type: {operation_type}\n"
-            f"Available operations: delete_episodes, split, merge, remove_feature, modify_tasks, convert_image_to_video"
-        )
+        available = ", ".join(OperationConfig.get_known_choices())
+        raise ValueError(f"Unknown operation: {operation_type}\nAvailable operations: {available}")
 
 
 def main() -> None:

src/lerobot/scripts/lerobot_record.py

@@ -398,7 +398,14 @@ def record_loop(
             )
 
         dt_s = time.perf_counter() - start_loop_t
-        precise_sleep(max(1 / fps - dt_s, 0.0))
+
+        sleep_time_s: float = 1 / fps - dt_s
+        if sleep_time_s < 0:
+            logging.warning(
+                f"Record loop is running slower ({1 / dt_s:.1f} Hz) than the target FPS ({fps} Hz). Dataset frames might be dropped and robot control might be unstable. Common causes are: 1) Camera FPS not keeping up 2) Policy inference taking too long 3) CPU starvation"
+            )
+
+        precise_sleep(max(sleep_time_s, 0.0))
 
         timestamp = time.perf_counter() - start_episode_t
 

src/lerobot/scripts/lerobot_replay.py

@@ -22,7 +22,7 @@ lerobot-replay \
     --robot.type=so100_follower \
     --robot.port=/dev/tty.usbmodem58760431541 \
     --robot.id=black \
-    --dataset.repo_id=aliberts/record-test \
+    --dataset.repo_id=<USER>/record-test \
     --dataset.episode=0
 ```
 

src/lerobot/scripts/lerobot_setup_can.py

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

src/lerobot/scripts/lerobot_train.py

@@ -338,21 +338,11 @@ 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=episode_indices_to_use,
+            episode_indices_to_use=dataset.episodes,
             drop_n_last_frames=cfg.policy.drop_n_last_frames,
             shuffle=True,
         )

src/lerobot/teleoperators/bi_openarm_leader/bi_openarm_leader.py

@@ -19,6 +19,7 @@ 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
@@ -88,6 +89,7 @@ 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)
@@ -109,6 +111,7 @@ 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 = {}
 
@@ -126,6 +129,7 @@ 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_not_connected
+from lerobot.utils.decorators import check_if_already_connected, check_if_not_connected
 
 from ..so_leader import SOLeader
 from ..teleoperator import Teleoperator
@@ -72,6 +72,7 @@ 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)
@@ -110,6 +111,7 @@ 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.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError
+from lerobot.utils.decorators import check_if_already_connected, check_if_not_connected
 
 from ..teleoperator import Teleoperator
 from .config_openarm_leader import OpenArmLeaderConfig
@@ -84,6 +84,7 @@ 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.
@@ -91,8 +92,6 @@ 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}...")
@@ -183,6 +182,7 @@ 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.
@@ -193,8 +193,6 @@ 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] = {}
 
@@ -214,10 +212,9 @@ 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 = False
+    use_degrees: bool = True
 
 
 @TeleoperatorConfig.register_subclass("so101_leader")

src/lerobot/utils/constants.py

@@ -26,9 +26,6 @@ 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"
@@ -92,101 +89,3 @@ 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.
-
-"""

src/lerobot/utils/robot_utils.py

@@ -16,14 +16,14 @@ import platform
 import time
 
 
-def precise_sleep(seconds: float, spin_threshold: float = 0.010, sleep_margin: float = 0.003):
+def precise_sleep(seconds: float, spin_threshold: float = 0.010, sleep_margin: float = 0.005):
     """
     Wait for `seconds` with better precision than time.sleep alone at the expense of more CPU usage.
 
     Parameters:
       - seconds: duration to wait
       - spin_threshold: if remaining <= spin_threshold -> spin; otherwise sleep (seconds). Default 10ms
-      - sleep_margin: when sleeping leave this much time before deadline to avoid oversleep. Default 3ms
+      - sleep_margin: when sleeping leave this much time before deadline to avoid oversleep. Default 5ms
 
     Note:
         The default parameters are chosen to prioritize timing accuracy over CPU usage for the common 30 FPS use case.

tests/optim/test_schedulers.py

@@ -11,6 +11,8 @@
 # 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 torch
+from packaging.version import Version
 from torch.optim.lr_scheduler import LambdaLR
 
 from lerobot.optim.schedulers import (
@@ -38,6 +40,10 @@ def test_diffuser_scheduler(optimizer):
         "last_epoch": 1,
         "lr_lambdas": [None],
     }
+
+    if Version(torch.__version__) >= Version("2.8"):
+        expected_state_dict["_is_initial"] = False
+
     assert scheduler.state_dict() == expected_state_dict
 
 
@@ -56,6 +62,10 @@ def test_vqbet_scheduler(optimizer):
         "last_epoch": 1,
         "lr_lambdas": [None],
     }
+
+    if Version(torch.__version__) >= Version("2.8"):
+        expected_state_dict["_is_initial"] = False
+
     assert scheduler.state_dict() == expected_state_dict
 
 
@@ -76,6 +86,10 @@ def test_cosine_decay_with_warmup_scheduler(optimizer):
         "last_epoch": 1,
         "lr_lambdas": [None],
     }
+
+    if Version(torch.__version__) >= Version("2.8"):
+        expected_state_dict["_is_initial"] = False
+
     assert scheduler.state_dict() == expected_state_dict
 
 

tests/robots/test_reachy2.py

@@ -142,6 +142,7 @@ def _make_reachy2_camera_mock(*args, **kwargs):
     cam.connect = MagicMock()
     cam.disconnect = MagicMock()
     cam.async_read = MagicMock(side_effect=lambda: np.zeros((height, width, 3), dtype=np.uint8))
+    cam.read_latest = MagicMock(side_effect=lambda: np.zeros((height, width, 3), dtype=np.uint8))
     return cam
 
 

tests/scripts/test_edit_dataset_parsing.py

@@ -0,0 +1,74 @@
+#!/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,
+    InfoConfig,
+    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),
+            ("info", InfoConfig),
+        ],
+    )
+    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),
+            ("info", InfoConfig),
+        ],
+    )
+    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