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Modular Deep Q Networks for Sim-to-real Transfer of Visuo-motor Policies (1610.06781v4)

Published 21 Oct 2016 in cs.RO, cs.AI, cs.CV, cs.LG, and cs.SY

Abstract: While deep learning has had significant successes in computer vision thanks to the abundance of visual data, collecting sufficiently large real-world datasets for robot learning can be costly. To increase the practicality of these techniques on real robots, we propose a modular deep reinforcement learning method capable of transferring models trained in simulation to a real-world robotic task. We introduce a bottleneck between perception and control, enabling the networks to be trained independently, but then merged and fine-tuned in an end-to-end manner to further improve hand-eye coordination. On a canonical, planar visually-guided robot reaching task a fine-tuned accuracy of 1.6 pixels is achieved, a significant improvement over naive transfer (17.5 pixels), showing the potential for more complicated and broader applications. Our method provides a technique for more efficient learning and transfer of visuo-motor policies for real robotic systems without relying entirely on large real-world robot datasets.

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Authors (4)
  1. Fangyi Zhang (17 papers)
  2. Michael Milford (145 papers)
  3. Peter Corke (49 papers)
  4. Jürgen Leitner (21 papers)
Citations (38)

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