Home/Enhancing robot perception using human teammates

Enhancing robot perception using human teammates

Jean Hyaejin Oh, Arne Suppe, Anthony (Tony) Stentz and Martial Hebert
Conference Paper, Carnegie Mellon University, Proceedings of the 12th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2013), May, 2013

Download Publication (PDF)

Copyright notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author’s copyright. These works may not be reposted without the explicit permission of the copyright holder.

Abstract

In robotics research, perception is one of the most challenging tasks. In contrast to existing approaches that rely only on computer vision, we propose an alternative method for improving perception by learning from human teammates. To evaluate, we apply this idea to a door detection problem. A set of preliminary experiments has been completed using software agents with real vision data. Our results demonstrate that information inferred from teammate observations significantly improves the perception precision.

BibTeX Reference
@conference{Oh-2013-7728,
title = {Enhancing robot perception using human teammates},
author = {Jean Hyaejin Oh and Arne Suppe and Anthony (Tony) Stentz and Martial Hebert},
booktitle = {Proceedings of the 12th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2013)},
keyword = {human-robot team, perception, inference},
notes = {Associated Project: Robotics CTA (RCTA) Associated Center: NREC},
school = {Robotics Institute , Carnegie Mellon University},
month = {May},
year = {2013},
address = {Pittsburgh, PA},
}
2017-09-13T10:39:21+00:00