作者丨 智源硏究院 编辑丨具身智能之心 点击下方 卡片 ，关注" 具身智能之心 "公众号 >> 点击进入→ 具身 智能之心 技术交流群 更多干货，欢迎加入国内首个具身智能全栈学习社区 ： 具身智能之心知识星球 (戳我) ， 这里包含所有你想要的。 本文的主要作者来自于北京智源人工智能研究院和北京理工大学。 本文的第一作者为北京智源人工智能研究院实习生、北京理工大学硕士研究生李罡阳。 本文的通讯作者兼项目负责人为北京智源人工智能研究院研究员罗莎祁、王鑫龙。 论文链接：https://arxiv.org/abs/2510.26280 主页链接：https://baai-aether.github.io/baai-thor/ 还记得面对 1400kg 汽车仍然从从容容游刃有余的宇树 G1 机器人吗？其背后的核心控制方法，正是智源研究院（BAAI）的具身框架 Thor。 智源具身框架Thor: T owards Human-Level Wh O le-Body R eactions for Intense Contact-Rich Environments 这里的难点并不是让机器人简单模仿"拉"的动作，而在于它能否全 ...

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].