Sensorimotor Control

Sensorimotor control studies how the nervous system integrates sensory information to produce coordinated movements, aiming to understand how brains plan and execute actions efficiently and robustly. Current research emphasizes developing biologically plausible computational models, including neural networks employing Kalman filtering and reinforcement learning algorithms, to simulate various aspects of sensorimotor control, such as multi-modal sensory fusion and adaptive mechanisms like tunable damping. These advancements are improving our understanding of both animal locomotion and informing the design of more robust and adaptable robots, particularly in challenging or uncertain environments.