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arXiv · 2506.20487

A Survey of Behavior Foundation Model: Next-Generation Whole-Body Control System of Humanoid Robots

Mingqi Yuan·Tao Yu·Wenqi Ge·Xiuyong Yao·Huijiang Wang·Jiayu Chen·Bo Li·Wei Zhang·Wenjun Zeng·Hua Chen·Xin Jin
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Abstract

Humanoid robots are drawing significant attention as versatile platforms for complex motor control, human-robot interaction, and general-purpose physical intelligence. However, achieving efficient whole-body control (WBC) in humanoids remains a fundamental challenge due to sophisticated dynamics, underactuation, and diverse task requirements. While learning-based controllers have shown promise for complex tasks, their reliance on labor-intensive and costly retraining for new scenarios limits real-world applicability. To address these limitations, behavior(al) foundation models (BFMs) have emerged as a new paradigm that leverages large-scale pre-training to learn reusable primitive skills and broad behavioral priors, enabling zero-shot or rapid adaptation to a wide range of downstream tasks. In this paper, we present a comprehensive overview of BFMs for humanoid WBC, tracing their development across diverse pre-training pipelines. Furthermore, we discuss real-world applications, current limitations, urgent challenges, and future opportunities, positioning BFMs as a key approach toward scalable and general-purpose humanoid intelligence. Finally, we provide a curated and regularly updated collection of BFM papers and projects to facilitate more subsequent research, which is available at https://github.com/yuanmingqi/awesome-bfm-papers.

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Mingqi Yuan, Tao Yu, Wenqi Ge, Xiuyong Yao, Huijiang Wang, Jiayu Chen, Bo Li, Wei Zhang, Wenjun Zeng, Hua Chen, Xin Jin. 2025-06-25. A Survey of Behavior Foundation Model: Next-Generation Whole-Body Control System of Humanoid Robots. https://arxiv.org/abs/2506.20487

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