Core Insights - Humanoid robots are gaining unprecedented attention as multifunctional platforms for complex motion control, human-robot interaction, and general physical intelligence, yet achieving efficient whole-body control remains a fundamental challenge [1][2] - The Behavior Foundation Model (BFM) has emerged to address limitations of existing controllers by leveraging large-scale pre-training to learn reusable skills and broad behavioral priors, enabling zero-shot or rapid adaptation to various downstream tasks [1][2] Summary by Sections Evolution of Humanoid Whole-Body Control Algorithms - The evolution of humanoid whole-body control algorithms is categorized into three stages: model-based controllers, learning-based task-specific controllers, and behavior foundation models [5][7][8][9] Behavior Foundation Model (BFM) - BFM is defined as a special type of foundational model aimed at controlling agent behavior in dynamic environments, rooted in principles of general foundational models and trained on large-scale behavioral data [13] - BFM methods are classified into three categories: goal-conditioned learning, intrinsic reward-driven learning, and forward-backward representation learning [14] Applications and Limitations of BFM - BFM has potential applications in various fields, including humanoid robotics, virtual agents in gaming, industrial 5.0, and medical assistance robots, enabling rapid adaptation and enhanced interaction [36][37] - Limitations include challenges in sim-to-real transfer, data bottlenecks, and the need for more generalized architectures to facilitate cross-platform skill transfer [39][40] Future Research Opportunities - Future research opportunities include addressing sim-to-real challenges, enhancing data quality and quantity, developing multimodal BFMs, and establishing standardized evaluation mechanisms for BFM [39][41] Ethical and Safety Considerations - Ethical issues arise from the potential for biased behavior encoding and privacy concerns, necessitating frameworks for data governance and real-time behavior monitoring [42] - Safety mechanisms are required to mitigate risks associated with sensor interference and multi-modal attacks, emphasizing the need for robust technical and ethical safeguards [43]