Biomimetic Perception Learning for Human Sensorimotor Control

Abstract

We present a simulation framework for biomimetic human perception and sensorimotor control. It features a biomechanically simulated, musculoskeletal human model actuated by numerous skeletal muscles, with two human-like eyes whose retinas have spatially nonuniform distributions of photoreceptors. Our prototype sensorimotor system for this model incorporates a set of 20 automatically-trained, deep neural networks (DNNs), half of which are neuromuscular DNN controllers comprising its motor subsystem, while the other half are devoted to visual perception. Within the sensory subsystem, which continuously operates on the retinal photoreceptor outputs, 2 DNNs drive eye and head movements, while 8 DNNs extract the sensory information needed to control the arms and legs. Exclusively by means of its egocentric, active visual perception, our biomechanical virtual human learns efficient, online visuomotor control of its eyes, head, and four limbs to perform tasks involving the foveation and visual pursuit of target objects coupled with visually-guided reaching actions to intercept the moving targets.