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.
@article{arxiv.2506.20487,
title = {A Survey of Behavior Foundation Model: Next-Generation Whole-Body Control System of Humanoid Robots},
author = {Mingqi Yuan and Tao Yu and Wenqi Ge and Xiuyong Yao and Huijiang Wang and Jiayu Chen and Bo Li and Wei Zhang and Wenjun Zeng and Hua Chen and Xin Jin},
journal= {arXiv preprint arXiv:2506.20487},
year = {2026}
}