Human-robot Collaborative Navigation Search using Social Reward Sources

TL;DR

This paper introduces a Social Reward Sources framework for human-robot collaborative search, integrating task, interaction, and environment considerations, and evaluates its effectiveness through quantitative and qualitative methods.

Contribution

It presents a novel Social Reward Sources design for collaborative navigation, incorporating multiple communication levels and validating through comprehensive performance and perception assessments.

Findings

Effective handling of collaborative search tasks

Different communication levels impact performance and perception

Model outperforms individual agent baselines

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.