Dynamic Gait

Dynamic gait research focuses on enabling robots to move robustly and efficiently across varied terrains and with diverse payloads. Current efforts concentrate on developing adaptive control algorithms, often leveraging model predictive control (MPC), reinforcement learning (RL), and data-driven approaches like Koopman operator theory, to generate and stabilize gaits in real-time. These advancements are crucial for improving the performance and reliability of legged robots in challenging environments, with applications ranging from search and rescue to industrial automation. The integration of multimodal sensing (e.g., vision and proprioception) and the development of unified gait representations are also key themes, aiming for more versatile and energy-efficient locomotion.