Whole Body Control

Whole-body control aims to coordinate the movement of all a robot's components—legs, arms, torso—for complex tasks, improving dexterity, stability, and interaction with the environment. Current research emphasizes learning-based approaches, such as reinforcement learning and imitation learning, often coupled with model predictive control or optimization techniques to handle constraints and achieve efficient, robust control. This field is crucial for advancing robotics in areas like human-robot collaboration, mobile manipulation, and locomotion in challenging environments, impacting both scientific understanding of complex control systems and the development of practical robotic applications.