Emergence of Human Oculomotor Behavior in a Cable-Driven Biomimetic Robotic Eye Using Optimal Control

Abstract

Using robotic models to test theories on the behavior of humans or animals can help understand many aspects of intelligence and control in natural systems. In this paper, we study how model-based optimal control principles can explain stereotyped human oculomotor behaviors, through simulations in a realistic model of the human eye with a cable-driven actuation system that mimics the six degrees of freedom of the extraocular muscles. Previous works have only addressed systems with 1-3 degrees-of-freedom. The current paper is a first study on a six-muscle system design, which introduces novel challenges to address. We propose nonlinear optimal control techniques to optimize the accuracy, energy, and duration of eye movement trajectories. We use a recurrent neural network that learns to emulate the nonlinear system dynamics from recorded sample trajectories. <p xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">We focus on the generation of rapid saccadic eye movements with fully unconstrained kinematics and corresponding control signals for the six cables that simultaneously satisfied the proposed optimization criteria. We show that realistic three-dimensional rotational kinematics and dynamics, as seen in human saccades, emerged from our model. Interestingly, just as in the primate oculomotor system, the six cables organized themselves into appropriate antagonistic muscle pairs without slack.