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Introduction

Welcome to the GBC (Generalized Behavior Cloning) Framework! 🤖

GBC logo

Unlocking the full potential of humanoid robots—enabling them to move and act as naturally as humans—is one of the greatest challenges in robotics today. Traditional control methods often struggle with generalization, while creating robust, human-like behaviors from scratch requires immense effort.

Overview​

GBC is a large-scale, open-source framework designed to solve this problem. Built on IsaacLab and PyTorch, it provides a comprehensive platform for imitation and reinforcement learning (IL+RL) that empowers you to bring any humanoid robot to life.

More than just a training framework, GBC establishes a unified data specification and extensible architecture that opens up vast research possibilities. By standardizing how motion data, robot configurations, and learning algorithms interact, GBC creates a foundation for diverse research directions—from multi-robot coordination to novel locomotion strategies, from adaptive behavior synthesis to cross-morphology transfer learning.

Our framework is built to tackle the core challenges of generalized imitation learning head-on:

  • The Data Problem: How do you get high-quality motion data for robots with different shapes and sizes (i.e., heterogeneous morphologies)?

  • The Training Problem: How do you train a policy that not only mimics motions accurately but is also robust enough for real-world deployment?

  • The Research Extensibility Problem: How do you build a platform that can support diverse research directions while maintaining consistency and reproducibility?

GBC provides a unified, powerful solution.

Why Choose GBC? 🎯​

Proven Performance​

  • Multi-Robot Support: Successfully tested on diverse humanoid platforms, not restricted to only Unitree G1, but also several full-sized humanoids
  • Latest Physics Engine Support: Built on latest Isaac Sim environment, to narrow the sim-to-real gap
  • Research-Grade: Built on state-of-the-art algorithms with peer-reviewed methodologies

Research-Oriented Design​

  • Unified Data Specification: Standardized data formats enable seamless integration of diverse motion sources and robot configurations
  • Extensible Architecture: Modular design supports rapid prototyping of new algorithms and research directions
  • Cross-Platform Compatibility: Consistent interfaces across different simulators and hardware platforms
  • Reproducible Research: Standardized evaluation metrics and experimental protocols ensure reliable comparisons

Developer-Friendly​

  • Modular Design: Easy to extend and customize for your specific robot and use case
  • Comprehensive Documentation: Detailed API references, tutorials, and examples
  • Active Community: Open-source with ongoing development and support

Industry-Leading Technology​

  • GPU-Accelerated: Train policies 10-100x faster than traditional methods
  • Transformer-Based: Leverage the latest advances in deep learning architecture
  • End-to-End Pipeline: From raw motion data to deployed policy in a unified framework
  • Research Ecosystem: Foundation for building specialized tools and applications on top of standardized components

Key Features 🌟​

GBC offers a complete end-to-end solution for humanoid robot learning and a standardized foundation for advanced robotics research:

🔄 Universal Motion Retargeting Pipeline​

Effortlessly convert human motion capture (MoCap) data—from sources like AMASS or even live streams—into physically plausible, high-quality reference motions for any humanoid robot. Our novel differentiable IK network automates this complex process, saving you significant engineering overhead.

🧠 Advanced IL+RL Algorithms​

At the heart of GBC is a library of cutting-edge learning algorithms, including our powerful DAgger-MMPPO. Featuring a novel Multi-Modal Transformer (MMTransformer) backbone, our approach effectively learns the relationship between reference motions and the robot's own state. The resulting policies can flawlessly imitate complex actions and intelligently follow velocity commands when no reference is available.

⚡ High-Performance Training Environment​

As an extension of IsaacLab, GBC leverages GPU acceleration for massively parallel and computationally efficient training. With support for curriculum learning, domain randomization, and physics-based assistance, you can train robust policies faster than ever. Simply provide your robot's configuration file to get started.

📋 Standardized Data Specification & Research Ecosystem​

GBC establishes unified data formats and interface standards that enable researchers to build upon a common foundation. This standardization facilitates:

  • Cross-study comparisons with consistent evaluation protocols
  • Rapid prototyping of new algorithms and approaches
  • Multi-robot applications spanning different morphologies and platforms
  • Specialized research tools that leverage the common data specification
  • Collaborative development with shared components and reproducible results

Getting Started 🚀​

Whether you're a researcher exploring the frontiers of AI, a student learning the ropes of robot control, or a developer aiming to deploy policies on physical hardware, GBC provides both the immediate tools for success and the foundational platform for innovation.

Ready to begin? Follow our step-by-step tutorials:

  1. 📦 Installation - Set up your environment
  2. 🗂️ Data Preparation - Prepare motion datasets
  3. 🤖 Task Configuration and Training - Configure your tasks and begin training
  4. 🎯 Validation & Deployment - Test and deploy

Exploring Advanced Research? Check out our Extensions Gallery to see how GBC's standardized framework enables diverse research applications and specialized tools built on our unified foundation.

Let's bring your humanoid robots to life and unlock new frontiers in robotics research! ✨