Ling, F., Jiménez-Rodríguez, A. and Prescott, T.J. orcid.org/0000-0003-4927-5390 (2020) Obstacle avoidance using stereo vision and deep reinforcement learning in an animal-like robot. In: 2019 IEEE International Conference on Robotics and Biomimetics (ROBIO). IEEE International Conference on Robotics and Biomimetics (ROBIO), 06-08 Dec 2019, Dali, China. IEEE , pp. 71-76. ISBN 9781728163222
Abstract
Obstacle avoidance is a fundamental behavior required to achieve safety and stability in both animals and robots. Many animals perceive and safely navigate their environment using two eyes with overlapping visual fields, allowing the use of stereopsis to compute distances to surfaces and to support collision avoidance. In this paper we develop an obstacle avoidance behavior for the biomimetic robot MiRo that combines stereo vision with deep reinforcement learning. We further show that avoidance strategies, learned for a simulated robot and environment, can be effectively transferred to a physical robot.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Reproduced in accordance with the publisher's self-archiving policy. |
Keywords: | Reinforcement Learning; Deep Q Network; Stereo Vision; Obstacle Avoidance; MiRo Robot, Animal-like Robot |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 13 Mar 2020 16:06 |
Last Modified: | 20 Jan 2021 01:38 |
Status: | Published |
Publisher: | IEEE |
Refereed: | Yes |
Identification Number: | 10.1109/ROBIO49542.2019.8961639 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:158352 |