Core Viewpoint - The article discusses the development of the BAAI Thor framework, which aims to enhance humanoid robots' ability to perform complex physical interactions in real-world environments, achieving human-level whole-body reactions and dynamic stability [7][8][31]. Group 1: Challenges in Humanoid Robot Control - Humanoid robots face two main challenges in transitioning from performers to laborers: the lack of human-like reaction mechanisms and the complexity of high-dimensional coordination control [9]. - The absence of effective human-like reaction mechanisms limits robots' performance under high external forces, as they often rely on rigid resistance strategies that can lead to instability [9][10]. - The high-dimensional nature of the control problem complicates the optimization of control strategies, as it involves numerous degrees of freedom and strong coupling between joints, making learning and adaptation difficult [10][11]. Group 2: BAAI Thor Framework - The BAAI Thor framework integrates biomechanical principles with innovative network structures to enable humanoid robots to achieve coordinated and stable responses in high-intensity force interactions [8][12]. - The framework includes two core components: the Force Adaptive Trunk Tilt Reward (FAT2), which guides robots to adjust their posture based on external forces, and a decoupled network structure that addresses high-dimensional coordination challenges [13][17]. Group 3: Experimental Validation - The BAAI Thor framework was tested on the Yushu G1 robot, which successfully pulled a car weighing approximately 1400 kg, demonstrating its capability for whole-body coordination and dynamic balance under extreme loads [18][20]. - Thor outperformed various baseline algorithms in force interaction tasks, achieving a peak pulling force of 167.7 N, which is about 48% of the robot's weight, representing a 68.9% performance improvement over the best baseline method [26][30]. - Quantitative analysis indicated that the introduction of the FAT2 reward function significantly enhanced the robot's adaptive posture adjustment capabilities, contributing approximately 80%-90% of the performance gains [30].

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].