POSTPONED | CSE Community Seminar

Abstract

The best current robots still fall short of the efficiency and safety guarantees exhibited by humans. One way to understand this superior performance is to develop computational models that predict how humans select, execute, and learn everyday movements. Despite this need, most of our current computational and theoretical understanding of human movement is limited to simple tasks or explanatory models with limited predictive breadth. My talk will highlight the predictive principles of safe and efficient motor behavior we’ve uncovered recently: the cost functions, controller structures, and learning rules. These principles will provide a blueprint for engineering human-like performance in wearable and autonomous robots.