Nate Hunt is a UC Berkeley graduate student interested in using a dynamical systems framework to understand movement in biological and engineered systems. His work investigates how neural, mechanical, and environmental systems interact in a way that leads to functional legged locomotion. He’s interested in closing the gap between animal and robot performance, and hopes to bring robots up to speed by enhancing their motor learning, which enables animals to improve their performance in dynamic and unpredictable environments. He envisions that motor learning capabilities will allow robotic systems to advance autonomously beyond the performance explicitly programmed by their designers. Nate has adopted fox squirrels as a model organism because of their unique combination of complex cognition and biomechanical athleticism. Through a combined approach that includes modeling, animal experiments, and robot design and testing, he hopes to discover deep principles of locomotion involving the interplay of biomechanics and cognition.