Core Viewpoint - Humanoid robots are gaining unprecedented attention as multifunctional platforms for complex motion control, human-robot interaction, and general physical intelligence, but achieving efficient whole-body control remains a fundamental challenge [1][2]. Group 1: Overview of Behavior Foundation Model (BFM) - The article discusses the emergence of Behavior Foundation Model (BFM) as a solution to the limitations of traditional controllers, enabling zero-shot or rapid adaptation to various downstream tasks through large-scale pre-training [1][2]. - 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 like GPT-4 and CLIP, utilizing large-scale behavior data for pre-training [12][13]. Group 2: Evolution of Humanoid Whole-Body Control Algorithms - The evolution of humanoid whole-body control algorithms is summarized in three stages: model-based controllers, learning-based task-specific controllers, and behavior foundation models [4][6][7]. - Model-based controllers rely heavily on physical models and require complex manual design, while learning-based controllers exhibit poor generalization across tasks [6][7][8]. Group 3: BFM Methodology and Algorithms - The article categorizes current BFM construction methods into three types: goal-conditioned learning, intrinsic reward-driven learning, and forward-backward representation learning [13]. - A notable example of a goal-conditioned learning method is MaskedMimic, which learns foundational motor skills through motion tracking and supports seamless task switching [18][20]. Group 4: 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 to diverse tasks [31][33]. - However, BFM faces limitations such as difficulties in sim-to-real transfer, where discrepancies between simulation and real-world dynamics hinder practical deployment [32][34]. Group 5: Future Research Opportunities and Risks - Future research opportunities include integrating multimodal inputs, developing advanced machine learning systems, and establishing standardized evaluation mechanisms for BFM [36][38]. - Risks associated with BFM include ethical concerns regarding training data biases, data bottlenecks, and the need for robust safety mechanisms to ensure reliability in open environments [36][39].