🦆 MicroDuck: Bipedal Robot Reinforcement Learning Policies & 3D Viewer

This repository contains pre-trained Deep Reinforcement Learning (RL) policy models, MuJoCo simulation assets, and the interactive 3D Web Viewer for the MicroDuck small bipedal robot.

🌐 Live Interactive 3D Demo

👉 Open MicroDuck 3D Web Viewer
(Play with the robot directly in your browser: forward walking, duck waddle, and physical soccer ball kicking!)

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📦 Repository Contents

• index.html: Standalone Interactive 3D Web Viewer & Soccer Simulator (Three.js)
• policies/: Pre-trained ONNX policy checkpoints
  • alpha_walking.onnx: Dynamic omnidirectional bipedal locomotion
  • alpha_stand.onnx: Stable posture hold and disturbance rejection
  • alpha_sitstand.onnx: Continuous sit and stand-up transition
  • alpha_ground_pick.onnx: Ground object picking motion
  • ball_kick_left.onnx: Precision left foot ball kick
  • roulade.onnx: Dynamic forward roll (roulade) motion recovery
• robot/: MuJoCo MJCF XML definitions and 3D mesh assets (scene.xml, scene_ball.xml, robot_allcollisions.xml, assets/*.stl)
• infer_engine.py: Standalone policy inference engine
• app.py: Offline simulation renderer

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🎮 Features

• 3D Robot Anatomy: Realistic duck bill, neck linkage, and webbed duck feet
• Soccer Ball Mechanics: Physical contact-based ball kicking & dribbling
• Real-time Control: Keyboard WASD/Arrow driving & motion toggling
• Reinforcement Learning: 50 Hz control frequency trained with MuJoCo

🚀 Quick Start

pip install mujoco onnxruntime numpy imageio