視覚運動ポリシーのsim-to-real転送のためのモジュラーディープQネットワーク
Modular Deep Q Networks for Sim-to-real Transfer of Visuo-motor Policies
知覚と制御の間にボトルネックを設けて別々に学習し、統合・微調整することで、シミュレーションで学習した視覚誘導ロボットポリシーを実機へ高精度に転送する手法を提案した。
著者: Fangyi Zhang, Jürgen Leitner, Michael Milford, Peter Corke
分類: cs.RO, cs.AI, cs.CV, cs.LG, cs.SY, eess.SY
原文アブストラクト
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.
