A Robot Exploration Strategy Based on Q-learning Network

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)

37 Scopus Citations
View graph of relations

Author(s)

  • Lei Tai
  • Ming LIU

Detail(s)

Original languageEnglish
Title of host publication2016 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages57-62
ISBN (Print)9781467389594
Publication statusPublished - 6 Jun 2016

Conference

TitleThe 2016 IEEE International Conference on Real-time Computing and Robotics
PlaceCambodia
Period6 - 10 June 2016

Abstract

This paper introduces a reinforcement learning method for exploring a corridor environment with the depth information from an RGB-D sensor only. The robot controller achieves obstacle avoidance ability by pre-training of feature maps using the depth information. The system is based on the recent Deep Q-Network (DQN) framework where a convolution neural network structure was adopted in the Q-value estimation of the Q-learning method. We separate the DQN into a supervised deep learning structure and a Q-learning network. The experiments of a Turtlebot in the Gazebo simulation environment show the robustness to different kinds of corridor environments. All of the experiments use the same pre-training deep learning structure. Note that the robot is traveling in environments which are different from the pre-training environment. It is the first time that raw sensor information is used to build such an exploring strategy for robotics by reinforcement learning.

Citation Format(s)

A Robot Exploration Strategy Based on Q-learning Network. / Tai, Lei; LIU, Ming.

2016 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2016. Institute of Electrical and Electronics Engineers Inc., 2016. p. 57-62 7784001.

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)