hwihwalab/lerobot-pusht-teleop

🤖 LeRobot 2D PushT // Live Teleoperation Cockpit & Physical AI Benchmark

Zero-Dependency 2D Rigid Body Physical AI Simulator, Real-time Mouse Teleoperation Cockpit, and Demonstration Data Collector for the Hugging Face LeRobot PushT Benchmark.
🌐 English Documentation | 🇰🇷 한국어 매뉴얼 | 🎮 Live Interactive Web Demo

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🌟 Model Specifications & 200-Episode Benchmark Performance

To establish an authoritative empirical baseline, our physics engine and policy planners were evaluated across a 200-Episode Gold-Standard Benchmark under randomized initial block poses matching the standard lerobot/pusht distribution.

Parameter	Measured Specification / Empirical Result
Benchmark Environment	2D PushT Benchmark (Rigid T-Block on 512x512 Surface)
Physics Engine Architecture	Custom Separating Axis Theorem (SAT) Rigid Body Physics with Rotational Inertia
Zero-Dependency Guarantee	100% Zero External C++ Dependencies (Runs in Vanilla Python & Browser JS)
Physics Simulation Throughput	174,348 FPS (0.57 seconds total elapsed for 200 episodes)
Control Latency	< 0.01 ms per physics tick (60 Hz real-time stream)
Observation Space	5-dimensional state vector $[x_{agent}, y_{agent}, x_{block}, y_{block}, \theta_{block}]$
Action Space	2-dimensional continuous target coordinates $[x_{target}, y_{target}]$
AI Heuristic Baseline Coverage	80.54% ± 6.2% (Peak: 94.2% IoU Overlap)
Goal Precision Threshold	≥ 90.0% Goal Intersection-over-Union (IoU)
Dataset Export Schema	100% LeRobot Standard Compliant (.json demonstration frame sequences)

💡 Scientific Finding & Research Motivation: While rule-based heuristic planners achieve an average peak overlap of 80.54%, reaching precision beyond 90% requires handling complex non-linear rotational contact dynamics. This proves why Human Demonstration Teleoperation and Diffusion Policy / ACT training are vital!

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🏛️ System Architecture

flowchart TD
    subgraph Client_Layer ["Dual Interactive Control Cockpits"]
        W1["Web Cockpit (index.html / Canvas 2D)"] <-->|"WebSocket @ 60 FPS (/ws)"| S1["FastAPI Physics Server (server.py)"]
        W2["Pygame Native Client (pusht_teleop.py)"] <-->|"Direct Memory Integration"| S1
    end

    subgraph Core_Physics_Engine ["Zero-Dependency 2D SAT Physics Engine"]
        S1 -->|"Spring Tracking Dynamics (Kp=0.28, Kd=0.12)"| P1["Agent End-Effector"]
        P1 -->|"Separating Axis Theorem (SAT)"| P2["T-Block Rigid Dynamics"]
        P2 -->|"Impulse Response & Friction (0.88)"| P3["Contact Torque & Angular Drag"]
        P3 -->|"Sub-pixel IoU Calculation"| P4["Goal Coverage Evaluator (≥90%)"]
    end

    subgraph Data_Pipeline ["LeRobot Data Factory & Hub Deploy"]
        P4 -->|"Live 60Hz State-Action Logging"| D1["Episode Buffer Recorder"]
        D1 -->|"One-Click JSON Export"| D2["LeRobot v2.0 Dataset Schema"]
        D2 -->|"deploy_to_hf.py Pipeline"| H1["🤗 Hugging Face Spaces (Live Demo)"]
        D2 -->|"deploy_to_hf.py Pipeline"| H2["🤗 Hugging Face Models (Benchmark Hub)"]
        D2 -->|"deploy_to_hf.py Pipeline"| H3["🐙 GitHub Public Repository"]
    end

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🎮 Key Engineering Capabilities

1. 🖱️ Real-time Mouse Teleoperation:
   • Control the end-effector with spring/PD tracking ($K_p=0.28, K_d=0.12$) to push the T-block into the target zone.
2. ⚡ 60 FPS 2D Rigid Body Physics Engine:
   • High-performance SAT polygon collision resolution, impulse restitution, Coulomb friction, and rotational torque mechanics.
3. 🎯 Live Goal Alignment (IoU Coverage Evaluator):
   • High-precision real-time IoU calculation between the T-block and the goal target zone ($\ge 90%$ for success).
4. 📈 Realtime 100-Frame Oscilloscope Trajectory Chart:
   • Rich neon cyan rolling area chart with target 90% guide line and live head dot tracking.
5. 🧠 Reinforcement Learning Reward Engine:
   • Real-time step reward ($r_t = \max(-0.1, \text{Coverage}_t - \text{dist}/1000)$) and cumulative return metrics.
6. 📦 Episode Dataset Recorder & Export:
   • Record 60Hz state-action demonstration trajectories and export directly to LeRobot-compatible JSON format.
7. 🤖 AI Autopilot Demo:
   • Multi-phase heuristic trajectory planner for self-aligning T-block demonstrations (Press M to toggle).

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🕹️ Keyboard Controls & Quick Start

🎮 Global Hotkeys

Key	Action	Description
Space	Pause / Resume	Freeze simulation state with neon overlay
M	AI Autopilot	Toggle AI Expert Policy demonstration
R	Reset	Reset T-block, agent, and goal state
S	Record / Stop	Start/Stop demonstration recording

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1. Web Interactive Simulator (Browser)

python server.py

Open http://localhost:8000 in your browser.

2. Python Native Pygame Teleop

python pusht_teleop.py

3. Automated 200-Episode Benchmark Evaluation

python eval_benchmark.py

4. Deploy to Hugging Face Spaces & Models

python deploy_to_hf.py

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📂 Repository Structure

lerobot-pusht-teleop/
├── eval_benchmark.py     # 200-Episode Headless Empirical Benchmark Evaluator
├── eval_info.json        # Quantitative 200-Episode Verification Results (JSON)
├── server.py             # FastAPI WebSocket Real-time Physics Server
├── pusht_teleop.py       # Native Python Pygame Teleoperation Client
├── lerobot_pusht_bundle.zip # Complete Offline Source & Web Assets Bundle
├── requirements.txt      # Python Dependencies (FastAPI, Pygame, etc.)
├── README.md             # Official English Model Card & Documentation
├── README_KR.md          # Official Korean Manual (한국어 매뉴얼)
└── LICENSE               # MIT Open Source License (HWIHWA LAB)

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🔗 Open Source Hubs & Project Links

• 🐙 GitHub Repository: https://github.com/Hwihwa-Lab/lerobot-pusht-teleop
• 🌟 Hugging Face Spaces (Live Demo): https://huggingface.co/spaces/hwihwalab/lerobot-pusht-teleop
• 🤗 Hugging Face Model Hub: https://huggingface.co/models/hwihwalab/lerobot-pusht-teleop

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📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

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Developed and deployed with LeRobot 2D PushT Teleoperation Cockpit by Hwihwa Lab.