Motion Controller

Motion control research focuses on developing algorithms and systems to precisely and reliably direct the movement of robots and simulated characters. Current efforts explore diverse approaches, including model-based controllers (e.g., using kinematic models and LQR), biologically-inspired methods like central pattern generators (CPGs), and data-driven techniques such as reinforcement learning and imitation learning, often combined with retrieval-augmented architectures. These advancements are crucial for improving robot dexterity, locomotion robustness across varied terrains, and the realism of computer-generated animations, impacting fields from robotics and manufacturing to computer graphics and virtual reality.