Locomotion Behavior

Locomotion behavior research focuses on understanding and replicating efficient and adaptable movement in robots and animals, aiming to improve robot control and diagnose animal health. Current research employs reinforcement learning algorithms, often incorporating energy-efficient reward functions and leveraging deep learning for pose estimation and gait analysis from video data. These advancements are improving robot locomotion capabilities across diverse terrains and informing the development of automated systems for animal health monitoring, with applications ranging from agriculture to search and rescue.