GBC: Generalized Behavior-Cloning Framework for Whole-Body Humanoid Imitation

TL;DR

This paper introduces GBC, a comprehensive framework that enables universal imitation learning for various humanoid robots by integrating adaptive data processing, a novel learning algorithm, and an open-source platform.

Contribution

GBC provides the first unified, end-to-end solution for general humanoid imitation, combining data retargeting, robust policy learning, and community deployment tools.

Findings

Effective transfer of motions across different humanoids.

High-fidelity imitation policies learned for multiple robots.

Open-source platform facilitates widespread adoption.

Abstract

The creation of human-like humanoid robots is hindered by a fundamental fragmentation: data processing and learning algorithms are rarely universal across different robot morphologies. This paper introduces the Generalized Behavior Cloning (GBC) framework, a comprehensive and unified solution designed to solve this end-to-end challenge. GBC establishes a complete pathway from human motion to robot action through three synergistic innovations. First, an adaptive data pipeline leverages a differentiable IK network to automatically retarget any human MoCap data to any humanoid. Building on this foundation, our novel DAgger-MMPPO algorithm with its MMTransformer architecture learns robust, high-fidelity imitation policies. To complete the ecosystem, the entire framework is delivered as an efficient, open-source platform based on Isaac Lab, empowering the community to deploy the full workflow via simple configuration scripts. We validate the power and generality of GBC by training policies on multiple heterogeneous humanoids, demonstrating excellent performance and transfer to novel motions. This work establishes the first practical and unified pathway for creating truly generalized humanoid controllers.