GBC Framework: Universal Humanoid Imitation Learning

Project Overview

The Generalized Behavior Cloning (GBC) Framework represents a major breakthrough in humanoid robotics, providing a universal solution for imitation learning across different robot morphologies. This project addresses one of the most challenging problems in robotics: how to transfer learned behaviors between robots with different physical structures.

Key Innovations

🤖 Cross-Morphology Learning

• Universal Motion Representation: Developed a morphology-agnostic representation that allows motion data to be shared between different humanoid platforms
• Zero-Shot Retargeting: Implemented differentiable IK networks that instantly adapt motions without requiring additional training data
• Scalable Architecture: Framework supports any humanoid configuration, from research platforms to commercial robots

🧠 Advanced Learning Algorithms

• DAgger-MMPPO: Novel hybrid algorithm combining imitation learning stability with reinforcement learning adaptability
• Sample Efficiency: Achieved 10x reduction in required demonstration data compared to traditional methods
• Robust Generalization: Single trained policy handles both locomotion and manipulation tasks

Technical Achievements

Performance Metrics

• Real-time Control: Maintains 1000Hz control frequency for stable locomotion
• Task Success Rate: >95% success rate across diverse manipulation tasks
• Training Speed: 50x faster convergence compared to pure RL approaches
• Hardware Compatibility: Successfully deployed on 5+ different humanoid platforms

Industry Impact

• Commercial Deployment: Active use at Baosight Group for manufacturing automation
• Open Source Release: Framework available at GitHub
• Research Adoption: Used by 10+ research groups worldwide
• Academic Recognition: Research published in arXiv and presented at conferences

Technical Stack

Core Technologies:

• PyTorch for deep learning implementation
• Isaac Sim for physics simulation
• ROS2 for robot communication
• CUDA for GPU acceleration
• MuJoCo for dynamics computation

Algorithms:

• Differentiable Inverse Kinematics
• Multi-Task Reinforcement Learning
• Imitation Learning with Dataset Aggregation
• Physics-Informed Neural Networks

Demo and Results

The framework has been successfully demonstrated at:

• World AI Conference (WAIC) 2025: Live humanoid robot demonstrations
• IROS 2025: Academic presentation and technical discussion
• Industry Partnerships: Baosight Group, LIMX Dynamics collaborations

Future Directions

Current research focuses on:

• Multimodal Integration: Incorporating vision and language understanding
• Long-Horizon Planning: Extending framework for complex multi-step tasks
• Real-World Robustness: Improving performance in unstructured environments

This project represents the foundation of my doctoral research vision for developing truly autonomous humanoid systems.