Learning Visually Guided Risk-Aware Reaching on a Robot Controlled by a GPU Spiking Neural Network

Abstract

Risk-aware control is a new type of robust nonlinear stochastic controller in which state variables are represented by time-varying probability densities and the desired trajectory is replaced by a cost function that specifies both the goals of movement and the potential risks associated with deviations. Efficient implementation is possible using the theory of Stochastic Dynamic Operators (SDO), because for most physical systems the SDO operators are near-diagonal and can thus be implemented using distributed computation. I show such an implementation using 4.3 million spiking neurons simulated in real-time on a GPU. I demonstrate successful control of a commercial desktop robot for a visually-guided reaching task, and I show that the operators can be learned during repetitive practice using a recursive learning rule.