title: Risk-aware control.
abstract: Human movement differs from robot control because of its flexibility in unknown environments, robustness to perturbation, and tolerance of unknown parameters and unpredictable variability. We propose a new theory, risk-aware control, in which movement is governed by estimates of risk based on uncertainty about the current state and knowledge of the cost of errors. We demonstrate the existence of a feedback control law that implements risk-aware control and show that this control law can be directly implemented by populations of spiking neurons. Simulated examples of risk-aware control for time-varying cost functions as well as learning of unknown dynamics in a stochastic risky environment are provided.
authors.affiliation: Departments of Biomedical Engineering, Neurology, and Biokinesiology, University of Southern California, Los Angeles, CA 90089, U.S.A. Terry@Sangerlab.net.
authors.forename: Terence D
authors.initials: TD
authors.lastname: Sanger
mesh_terms: D000200:Action Potentials / Q000502:physiology; D001364:Awareness; D003198:Computer Simulation; D025461:Feedback, Physiological; D006801:Humans; D007858:Learning; D008959:Models, Neurological; D009068:Movement / Q000502:physiology*; D009474:Neurons / Q000502:physiology; D012306:Risk; D012371:Robotics; D035501:Uncertainty