智源具身框架Thor开源：迈向类人级全身控制，在强对抗中“站稳脚跟”

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].