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This paper introduces a machine learning based system for controlling arobotic manipulator with visual perception only. The capability to autonomouslylearn robot controllers solely from raw-pixel images and without any priorknowledge of configuration is shown for the first time. We build upon thesuccess of recent deep reinforcement learning and develop a system for learningtarget reaching with a three-joint robot manipulator using external visualobservation. A Deep Q Network (DQN) was demonstrated to perform target reachingafter training in simulation. Transferring the network to real hardware andreal observation in a naive approach failed, but experiments show that thenetwork works when replacing camera images with synthetic images.

Towards Vision-Based Deep Reinforcement Learning for Robotic Motion Control