Robust Adaptive Dynamic Programming as A Theory of Sensorimotor Control

Many tasks that humans perform in our everyday life involve different sources of uncertainties. However, it is interesting and surprising to notice how the central nervous system (CNS) can gracefully coordinate our movements to deal with these uncertainties. This chapter studies sensorimotor control with static and dynamic uncertainties under the framework of robust adaptive dynamic programming (RADP). The linear version of RADP is extended for stochastic systems by taking into account signal‐dependent noise, and the proposed method is applied to study the sensorimotor control problem with both static and dynamic uncertainties. Results presented in the chapter suggest that the CNS may use RADP‐like learning strategy to coordinate movements and to achieve successful adaptation in the presence of static and/or dynamic uncertainties. In the absence of dynamic uncertainties, the learning strategy reduces to an ADP‐like mechanism.