Introspective visuomotor control : exploiting uncertainty in deep visuomotor control for failure recovery

Hung, C.-M., Sun, L. orcid.org/0000-0002-0393-8665, Wu, Y. et al. (2 more authors) (2021) Introspective visuomotor control : exploiting uncertainty in deep visuomotor control for failure recovery. In: ICRA 2021 : IEEE International Conference on Robotics and Automation. 2021 IEEE International Conference on Robotics and Automation (ICRA), 30 May - 05 Jun 2021, Xi’an, China. Institute of Electrical and Electronics Engineers , pp. 6293-6299. ISBN 978-1-7281-9077-8

Abstract

Metadata

Item Type: Proceedings Paper
Authors/Creators:
Copyright, Publisher and Additional Information:

© 2021 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: Uncertainty; Monte Carlo methods; Conferences; Neural networks; Tactile sensors; End effectors; Trajectory
Dates:
  • Published: 18 October 2021
  • Accepted: 5 May 2021
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield)
Funding Information:
Funder
Grant number
Engineering and Physical Science Research Council
EP/R026092/1
The Royal Society
RGS\R2\202432
Depositing User: Symplectic Sheffield
Date Deposited: 20 May 2021 07:44
Last Modified: 21 Jun 2023 15:24
Status: Published
Publisher: Institute of Electrical and Electronics Engineers
Refereed: Yes
Identification Number: 10.1109/ICRA48506.2021.9561749
Related URLs:
Open Archives Initiative ID (OAI ID):

Export

Statistics