斯坦福最新的全身运控方案，跨地形泛化！

Core Insights - The article discusses the challenges and advancements in humanoid robot locomotion, emphasizing the need for multi-limb coordination to navigate complex environments effectively [2][3][5]. Research Background and Core Challenges - Traditional humanoid robot movement focuses primarily on legged locomotion, but real-world scenarios require the use of additional body parts for stability and support [2]. - The research identifies two main challenges in humanoid robot locomotion: rich contact motion planning and robust control in complex environments, and the need for flexible skill switching across different terrains [3][5]. Core Methodology - A hierarchical framework combining physics-based keyframe animation and reinforcement learning is proposed, consisting of four main components: keyframe generation, policy training, skill selection, and hierarchical execution [4][5]. Keyframe Motion Generation - The study utilizes a GUI tool based on the MuJoCo physics engine to create keyframe animations that encode human movement knowledge while addressing physical realism and manual tuning costs [7]. - The limitations of keyframes include their open-loop nature, necessitating reinforcement learning to develop adaptive motion tracking strategies [8]. Motion Tracking Strategies - Strategies are categorized into three types, ensuring seamless transitions between four standard postures (standing, crawling, prone, supine) [9]. - The reward function for training includes components for tracking accuracy, energy efficiency, and preventing premature termination of training [10]. Visual Skill Classifier - The system employs a visual skill classifier to autonomously select appropriate movement skills based on environmental perception, categorizing skills into movement, transition, and terrain-specific skills [11]. Hierarchical Policy Execution - The framework separates visual planning from low-level control, enhancing robustness and real-time responsiveness [12]. Experimental Validation - Data collection involved real-world testing with a robot equipped with dual fisheye cameras, and the model was trained using a ResNet classifier to balance computational efficiency and geometric feature capture [15]. - The system demonstrated zero-shot transfer success across various obstacle configurations, validating the effectiveness of the motion tracking strategies [18][23]. Conclusion and Future Directions - The research presents a hybrid framework of keyframes and reinforcement learning, achieving humanoid robot mobility in complex terrains and demonstrating zero-shot transfer capabilities [28]. - Future work may focus on automating keyframe design, improving motion quality through advanced interpolation methods, and optimizing contact dynamics modeling to enhance performance in contact-rich tasks [28].