Robust Adaptive Dynamic Programming for Sensorimotor Control with Signal-Dependent Noise

As human beings, we coordinate movements and interact with our environment through sensory information and motor adaptation in our daily lives. Many characteristics of these interactions can be studied using optimization-based models, which assume that the precise knowledge of both the sensorimotor system and its interacting environment is available for the central nervous system (CNS). However, when static and dynamic uncertainties are present, the previously developed optimization models may fail to explain how the CNS can adapt to the uncertainties and still coordinate the movement. In this paper, we attempt to propose a novel computational mechanism for sensorimotor control from a perspective of robust adaptive dynamic programming (RADP). It is suggested that, instead of identifying the system dynamics of both the motor system and the environment, the CNS computes iteratively a robust optimal control policy for movement, using the real-time sensory data. With the help of numerical analysis and simulations, it is observed that the proposed model can reproduce movement trajectories which are consistent with experimental data. Consequently, we conjecture that, in order to achieve successful adaptation, this RADP-type mechanism may be used by the CNS of humans to coordinate movements in the presence of static/dynamic arising in the sensorimotor system.