Title: Predictive principles of motor behavior: costs, controllers, and learning rules

Abstract: The best current robots still fall short of the efficiency and safety guarantees exhibited by human motor behavior. One way to understand this superior performance is to develop computational models that predict how humans select, execute, and learn everyday movements. My talk will highlight the predictive principles of safe and efficient motor behavior we've uncovered over the years: the cost functions, controller structures, and learning rules. These principles will provide a blueprint for engineering human-like performance in wearable and autonomous robots.

Nidhi Seethapathi is a Frederick A. (1971) and Carole J. Middleton Career Development Assistant Professor in Brain and Cognitive Sciences and Electrical Engineering and Computer Science at MIT. Her group builds predictive models to help understand human sensorimotor control using a combination of theory, computational modeling, and experiments. Previously, she worked as a postdoctoral researcher at University of Pennsylvania developing data-driven tools for neuromotor rehabilitation. She obtained her PhD in Mechanical Engineering from The Ohio State University, where she was a Schlumberger Foundation Faculty for the Future Fellow.