Towards vision-based deep reinforcement learning for robotic motion control
Zhang, Fangyi, Leitner, Jürgen, Milford, Michael, Upcroft, Ben, & Corke, Peter (2015) Towards vision-based deep reinforcement learning for robotic motion control. In Australasian Conference on Robotics and Automation 2015, 2-4 December 2015, Canberra, A.C.T.
This paper introduces a machine learning based system for controlling a robotic manipulator with visual perception only. The capability to autonomously learn robot controllers solely from raw-pixel images and without any prior knowledge of configuration is shown for the first time. We build upon the success of recent deep reinforcement learning and develop a system for learning target reaching with a three-joint robot manipulator using external visual observation. A Deep Q Network (DQN) was demonstrated to perform target reaching after training in simulation. Transferring the network to real hardware and real observation in a naive approach failed, but experiments show that the network works when replacing camera images with synthetic images.
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|Item Type:||Conference Paper|
|Keywords:||Robotic Manipulation, Motion Control, Target Reaching, Deep Reinforcement Learning, DQN|
|Subjects:||Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Adaptive Agents and Intelligent Robotics (080101)
Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > ELECTRICAL AND ELECTRONIC ENGINEERING (090600) > Control Systems Robotics and Automation (090602)
|Divisions:||Current > Research Centres > ARC Centre of Excellence for Robotic Vision
Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
|Facilities:||Science and Engineering Centre|
|Copyright Owner:||Copyright 2015 [Please consult the author]|
|Deposited On:||27 Jan 2016 22:37|
|Last Modified:||28 Jan 2016 22:00|
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