Human-robot Collaborative Navigation Search using Social Reward Sources
Abstract
This paper proposes a Social Reward Sources (SRS) design for a Human-Robot Collaborative Navigation (HRCN) task: human-robot collaborative search. It is a flexible approach capable of handling the collaborative task, human-robot interaction and environment restrictions, all integrated on a common environment. We modelled task rewards based on unexplored area observability and isolation and evaluated the model through different levels of human-robot communication. The models are validated through quantitative evaluation against both agents' individual performance and qualitative surveying of participants' perception. After that, the three proposed communication levels are compared against each other using the previous metrics.
Cite
@article{arxiv.1909.04768,
title = {Human-robot Collaborative Navigation Search using Social Reward Sources},
author = {Marc Dalmasso and Anaís Garrell and Pablo Jiménez and Alberto Sanfeliu},
journal= {arXiv preprint arXiv:1909.04768},
year = {2019}
}
Comments
Preprint, accepted to the fourth Iberian Robotics Conference (Robot 2019)