Two degree-of-freedom robotic eye: design, modeling, and learning-based control in foveation and smooth pursuit

Abstract

With increasing ocular motility disorders affecting human eye movement, the need to understand the biomechanics of the human eye rises constantly. A robotic eye system that physically mimics the human eye can serve as a useful tool for biomedical researchers to obtain an intuitive understanding of the functions and defects of the extraocular muscles and the eye. This paper presents the design, modeling, and control of a two degree-of-freedom (2-DOF) robotic eye, driven by artificial muscles, in particular, made of super-coiled polymers (SCPs). Considering the highly nonlinear dynamics of the robotic eye system, this paper applies deep deterministic policy gradient (DDPG), a machine learning algorithm to solve the control design problem in foveation and smooth pursuit of the robotic eye. To the best of our knowledge, this paper presents the first modeling effort to establish the dynamics of a robotic eye driven by SCP actuators, as well as the first control design effort for robotic eyes using a DDPG-based control strategy. A linear quadratic regulator-type reward function is proposed to achieve a balance between system performances (convergence speed and tracking accuracy) and control efforts. Simulation results are presented to demonstrate the effectiveness of the proposed control strategy for the 2-DOF robotic eye.