一套通用框架，让不同机器人能模仿人类动作
GBC: Generalized Behavior-Cloning Framework for Whole-Body Humanoid Imitation
- 用可微逆运动学自动适配人体动捕数据到任意人形机器人
- 新算法结合Transformer，实现高保真、强泛化的人类动作模仿
- 开源平台支持一键部署，适合机器人研发与仿生控制研究
构建类人机器人面临根本性挑战：数据处理与学习算法通常无法跨不同机器人形态通用。本文提出通用行为克隆（GBC）框架，提供从人体动作到机器人执行的端到端统一解决方案。GBC通过三项协同创新实现：首先，自适应数据流水线利用可微逆运动学网络，将任意人体动捕数据自动重定向至任意人形机器人；在此基础上，基于MMTransformer架构的新型DAgger-MMPPO算法，学习出鲁棒且高保真的模仿策略；最后，整个框架以高效开源平台形式发布，基于Isaac Lab，仅需简单配置脚本即可部署全流程。我们在多个异构人形机器人上训练策略，验证了GBC在多种新动作上的优异表现与良好迁移能力。本工作首次建立可实用的通用化人形机器人控制器生成路径。
原文摘要 · Abstract (English)
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.
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