Distributed Deep Reinforcement Learning based Indoor Visual Navigation
Shih–Hsi Hsu, Shao–Hung Chan, Ping–Tsang Wu, Kun Xiao, Li–Chen Fu
Abstract
Recently, as the rise of deep reinforcement learning, it not only can help the robot to convert the complicated environment scene to motor control command directly but also can accomplish the navigation task properly. In this paper, we propose a novel structure, where the objective is to achieve navigation in large-scale indoor complex environment without pre-constructed map. Generally, it requires good understanding of such indoor environment to make complex spatial perception possible, especially when the indoor space consists of many walls and doors which might block the view of robot leading to complex navigation path. By the proposed distributed deep reinforcement learning in different local regions, our method can achieve indoor visual navigation in the aforementioned large-scale environment without extra map information and human instruction. In the experiments, we validate our proposed method by conducting highly promising navigation tasks both in simulation and real environments.
BibTeX
@inproceedings{iros2018_distributeddeepr,
title = {Distributed Deep Reinforcement Learning based Indoor Visual Navigation},
author = {Shih–Hsi Hsu and Shao–Hung Chan and Ping–Tsang Wu and Kun Xiao and Li–Chen Fu},
booktitle = {IROS 2018},
year = {2018}
}