Locomotion Control

Locomotion control research focuses on enabling robots to move effectively and adapt to diverse environments, aiming to create robust and agile robotic systems. Current efforts concentrate on integrating model-based planning (e.g., using Linear Inverted Pendulum models) with model-free reinforcement learning, leveraging sensor fusion (vision and inertial data) for terrain awareness, and developing novel control architectures like multi-brain collaborative systems and RL-augmented MPC. These advancements are improving robot performance in challenging terrains and tasks, with applications ranging from legged robots navigating uneven surfaces to exoskeletons assisting human locomotion.