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.

翻译：人形机器人作为复杂运动控制、人机交互和通用物理智能的多功能平台正受到广泛关注。然而，由于复杂的动力学特性、欠驱动特性以及多样化的任务需求，在人形机器人中实现高效的全身控制仍然是一个根本性挑战。尽管基于学习的控制器在复杂任务中展现出潜力，但其对新场景依赖劳动密集且成本高昂的重新训练，限制了实际应用。为应对这些限制，行为基础模型作为一种新范式应运而生，它利用大规模预训练来学习可重用的基础技能和广泛的行为先验，从而能够对广泛的下游任务实现零样本或快速适应。本文对人形机器人全身控制的行为基础模型进行了全面综述，追溯了其在不同预训练流程中的发展。此外，我们讨论了实际应用、当前局限、紧迫挑战和未来机遇，将行为基础模型定位为实现可扩展和通用型人形机器人智能的关键途径。最后，我们提供了一个精心策划且定期更新的行为基础模型论文与项目集合，以促进后续研究，该资源可在 https://github.com/yuanmingqi/awesome-bfm-papers 获取。