Enhancing robot perception using human teammates

Authors:
Jean Oh;Arne Suppe;Anthony Stentz;Martial Hebert
Affiliations:
Carnegie Mellon University, Pittsburgh, PA, USA;Carnegie Mellon University, Pittsburgh, PA, USA;Carnegie Mellon University, Pittsburgh, PA, USA;Carnegie Mellon University, Pittsburgh, PA, USA
Venue:
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
Year:
2013

Citing 2
Cited 0

Image-based procedural modeling of facades

ACM SIGGRAPH 2007 papers
Stacked hierarchical labeling

ECCV'10 Proceedings of the 11th European conference on Computer vision: Part VI

Quantified Score

Hi-index	0.00

Visualization

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.