Whole Body Locomotion

Whole-body locomotion research focuses on enabling robots, particularly legged robots, to move and manipulate objects effectively in complex environments. Current efforts concentrate on developing robust and efficient control algorithms, including model predictive control (MPC) variants like sampling-based and centroidal approaches, and reinforcement learning methods often enhanced by hierarchical structures or CPG integration. These advancements are crucial for creating more adaptable and versatile robots for applications ranging from search and rescue to industrial automation and assistive technologies, driving progress in both robotics and control theory.