単眼視による障害物回避のための深層強化学習
Towards Monocular Vision based Obstacle Avoidance through Deep Reinforcement Learning
単眼RGB画像のみから障害物を回避するため、Dueling構造とDouble-Qを組み合わせたD3QNを提案し、シミュレータで学習したモデルを実機ロボットへ直接転移できることを示した。
著者: Linhai Xie, Sen Wang, Andrew Markham, Niki Trigoni
分類: cs.RO
原文アブストラクト
Obstacle avoidance is a fundamental requirement for autonomous robots which operate in, and interact with, the real world. When perception is limited to monocular vision avoiding collision becomes significantly more challenging due to the lack of 3D information. Conventional path planners for obstacle avoidance require tuning a number of parameters and do not have the ability to directly benefit from large datasets and continuous use. In this paper, a dueling architecture based deep double-Q network (D3QN) is proposed for obstacle avoidance, using only monocular RGB vision. Based on the dueling and double-Q mechanisms, D3QN can efficiently learn how to avoid obstacles in a simulator even with very noisy depth information predicted from RGB image. Extensive experiments show that D3QN enables twofold acceleration on learning compared with a normal deep Q network and the models trained solely in virtual environments can be directly transferred to real robots, generalizing well to various new environments with previously unseen dynamic objects.
