Deficit of the extraocular muscle is known as a key cause of ocular motility disorders that affect eye movement and complicate daily activities of millions of people in the US. A physical model mimicking the biomechanics of the oculomotor plant can improve the understanding of functionality and control of extraocular muscles and provide a tool for researchers to gain insights into binocular misalignment. This paper will present, for the first time, the design and development of a robotic eye system driven by antagonistic super coiled polymer (SCP) based artificial muscles and the motion control design by leveraging machine learning techniques. The dynamic model of the robotic eye will be presented. Deep reinforcement learning is used for control design of the robotic eye system, demonstrated by simulation of one-dimensional foveation control.
- Dynamic Systems and Control Division
Foveation Control of a Robotic Eye Using Deep Reinforcement Learning
- Views Icon Views
- Share Icon Share
- Search Site
Rajendran, SK, Wei, Q, & Zhang, F. "Foveation Control of a Robotic Eye Using Deep Reinforcement Learning." Proceedings of the ASME 2018 Dynamic Systems and Control Conference. Volume 1: Advances in Control Design Methods; Advances in Nonlinear Control; Advances in Robotics; Assistive and Rehabilitation Robotics; Automotive Dynamics and Emerging Powertrain Technologies; Automotive Systems; Bio Engineering Applications; Bio-Mechatronics and Physical Human Robot Interaction; Biomedical and Neural Systems; Biomedical and Neural Systems Modeling, Diagnostics, and Healthcare. Atlanta, Georgia, USA. September 30–October 3, 2018. V001T04A018. ASME. https://doi.org/10.1115/DSCC2018-9209
Download citation file: