Dynamic Movement

Dynamic movement research focuses on enabling robots to perform agile and versatile actions, mirroring the athleticism of animals and humans. Current efforts leverage reinforcement learning, often coupled with model-based methods like impedance matching and inverse kinematics, to achieve complex maneuvers such as running jumps, bipedal walking, and even multi-ball juggling. These advancements rely on sophisticated control architectures and increasingly detailed datasets capturing dynamic human and animal motion, improving both the accuracy and robustness of robotic control. The resulting improvements in robotic dexterity have significant implications for applications ranging from assistive robotics to sports analytics.