Hierarchical optimal control of complex dynamics - new algorithms and models of sensorimotor function

University Of California-San Diego, La Jolla CA

Investigators

Abstract

Proposal Number: ECS-0524761 Proposal Title: Hierarchical optimal control of complex dynamics - new algorithms and models of sensorimotor function PI Name: Todorov, Emanuel PI Institution: University of California-San Diego Intellectual Merit: The PI plans to use and integrate more recent methods of adaptive dynamic programming or "reinforcement learning," and apply them to the modeling and control of biomechanical systems like human arm movement. Reinforcement learning methods have been applied before in biology and in the study of arm movement, but past studies have mainly relied on old, simple mathematical structures which do not scale well to high degrees of complexity in space and time. This project will make a unique effort to reach out, integrate and use more advanced methods. The effort to understand the mathematical, functional basis of effective decision and control in the brain is perhaps one of the most important, fundamental challenges before science in general. Broader Benefits: Cross-disciplinary communication between the most advanced areas of technology and the serious study of intelligence in the brain is still far less than it could be. If successful, this project could have a major impact on the unification of knowledge across disciplines. Aggressive education and dissemination are a natural part of the effort to heal the gap between disciplines related to these scientific goals. Better understanding of biomechanical issues may also have important benefits both in medicine and in robots.