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FOVEATION CONTROL OF A ROBOTIC EYE USING DEEP REINFORCEMENT LEARNING

机译:基于深度强化学习的机器人眼的运动控制

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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.
机译:眼外肌的缺乏被认为是导致眼动能力异常的主要原因,眼动能力异常会影响眼球运动并使美国数百万人的日常活动复杂化。模仿动眼植物生物力学的物理模型可以增进对功能和眼外肌控制的理解,并为研究人员提供一种了解双眼错位的工具。本文将首次展示由对抗性超卷曲聚合物(SCP)人工肌肉驱动的机器人眼系统的设计和开发,以及利用机器学习技术进行运动控制的设计。将介绍机械人眼睛的动态模型。深度强化学习用于机器人眼系统的控制设计,通过一维偏心控制的仿真进行了演示。

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