Dynamic Locomotion

Dynamic locomotion research focuses on enabling robots to move robustly and efficiently in diverse environments, encompassing walking, running, jumping, and manipulation. Current efforts concentrate on integrating model-based planning (e.g., using Linear Inverted Pendulum models) with reinforcement learning to achieve agile and adaptable control, often incorporating advanced perception techniques like visual-inertial odometry and terrain mapping. These advancements are crucial for creating more versatile robots capable of navigating complex terrains and performing complex tasks in real-world settings, impacting fields like robotics, automation, and even biomechanics through the study of animal locomotion.