LeRobot Dataset Tool and Visualizer is a web application for interactive exploration and visualization of robotics datasets, particularly those in the LeRobot format. It enables users to browse, view, and analyze episodes from large-scale robotics datasets, combining synchronized video playback with rich, interactive data graphs.

This tool is designed to help robotics researchers and practitioners quickly inspect and understand large, complex datasets. It fetches dataset metadata and episode data (including video and sensor/telemetry data), and provides a unified interface for:

• Navigating between organizations, datasets, and episodes
• Watching episode videos
• Exploring synchronized time-series data with interactive charts
• Analyzing action quality and identifying problematic episodes
• Visualizing robot poses in 3D using URDF models
• Paginating through large datasets efficiently

• Dataset & Episode Navigation: Quickly jump between organizations, datasets, and episodes using a sidebar and navigation controls.
• Synchronized Video & Data: Video playback is synchronized with interactive data graphs for detailed inspection of sensor and control signals.
• Overview Panel: At-a-glance summary of dataset metadata, camera info, and episode details.
• Statistics Panel: Dataset-level statistics including episode count, total recording time, frames-per-second, and an episode-length histogram.
• Action Insights Panel: Data-driven analysis tools to guide training configuration — includes autocorrelation, state-action alignment, speed distribution, and cross-episode variance heatmap.
• Filtering Panel: Identify and flag problematic episodes (low movement, jerky motion, outlier length) for removal. Exports flagged episode IDs as a ready-to-run LeRobot CLI command.
• 3D URDF Viewer: Visualize robot joint poses frame-by-frame in an interactive 3D scene, with end-effector trail rendering. Supports SO-100, SO-101, and OpenArm bimanual robots.
• Annotations Panel: Hand-edit the v3.1 language schema (language_persistent + language_events) — subtask, plan, memory, interjection + paired speech, and VQA atoms with bounding-box / keypoint / count / attribute / spatial answers. VQA bboxes and keypoints render as overlays on the video player; drag or click on a camera to draw new ones. Backed by an optional FastAPI service (in backend/) for parquet rewrites and HF Hub push.
• Efficient Data Loading: Uses parquet and JSON loading for large dataset support, with pagination, chunking, and lazy-loaded panels for fast initial load.
• Responsive UI: Built with React, Next.js, and Tailwind CSS for a fast, modern user experience.

• Next.js (App Router)
• React
• Recharts (for data visualization)
• Three.js + @react-three/fiber + @react-three/drei (for 3D URDF visualization)
• urdf-loader (for parsing URDF robot models)
• hyparquet (for reading Parquet files)
• Tailwind CSS (styling)

This project uses Bun as its package manager. If you don't have it installed:

# Install Bun
curl -fsSL https://bun.sh/install | bash

Install dependencies:

Run the development server:

Open http://localhost:3000 with your browser to see the result.

You can start editing the page by modifying src/app/page.tsx or other files in the src/ directory. The app supports hot-reloading for rapid development.

# Build for production
bun run build

# Start production server
bun start

# Run linter
bun run lint

# Format code
bun run format

• DATASET_URL: (optional) Base URL for dataset hosting (defaults to HuggingFace Datasets).
• NEXT_PUBLIC_ANNOTATE_BACKEND_URL: (optional) URL of the FastAPI annotation backend (backend/app.py). When set, the Annotations tab can save edits and rewrite parquet shards / push to the Hub. When unset the tab is read/edit only with sessionStorage persistence.

The Annotations tab edits LeRobot v3.1 language atoms — language_persistent (broadcast subtask/plan/memory) and language_events (per-frame interjection / vqa / speech) — and renders existing bbox/keypoint atoms over the video player. Edits live in sessionStorage by default; to write the new columns into data/chunk-*/file-*.parquet (matching the writer in lerobot#3471) and push the result to the Hub, run the bundled FastAPI service:

# 1. install + start the backend (port 7861 by default)
cd backend
python -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt
uvicorn app:app --port 7861 --reload

# 2. start the visualizer with the backend URL configured
cd ..
NEXT_PUBLIC_ANNOTATE_BACKEND_URL=http://127.0.0.1:7861 bun run dev

The backend exposes:

• POST /api/dataset/load — load a dataset by repo_id or local_path
• GET /api/episodes/{ep}/atoms — list atoms for an episode
• POST /api/episodes/{ep}/atoms — replace atoms (event timestamps are snapped to exact source-frame timestamps before persisting)
• GET /api/episodes/{ep}/frame_timestamps — used client-side for snapping
• POST /api/export — rewrite parquet with the new language columns plus the dataset-level tools column (drops legacy subtask_index)
• POST /api/push_to_hub — export and push to a target repo

This application can be deployed using Docker with bun for optimal performance and self-contained builds.

docker build -t lerobot-visualizer .

docker run -p 7860:7860 lerobot-visualizer

The application will be available at http://localhost:7860.

docker run -p 7860:7860 -e DATASET_URL=your-url lerobot-visualizer

Contributions, bug reports, and feature requests are welcome! Please open an issue or submit a pull request.

The app was orignally created by @Mishig25 and taken from this PR #1055