Learning to Guide Human Attention on Mobile Telepresence Robots with 360 Vision

TL;DR

This paper introduces GHAL360, a reinforcement learning framework that guides human attention on mobile telepresence robots with 360-degree vision, improving target search efficiency in remote environments.

Contribution

GHAL360 is a novel framework that enables MTRs to learn goal-oriented policies for guiding human attention using visual indicators, bridging the observability gap in shared autonomy.

Findings

GHAL360 outperforms existing baselines in target search tasks.

The framework effectively guides human attention using visual indicators.

Experimental results demonstrate improved team efficiency in remote navigation.

Abstract

Mobile telepresence robots (MTRs) allow people to navigate and interact with a remote environment that is in a place other than the person's true location. Thanks to the recent advances in 360 degree vision, many MTRs are now equipped with an all-degree visual perception capability. However, people's visual field horizontally spans only about 120 degree of the visual field captured by the robot. To bridge this observability gap toward human-MTR shared autonomy, we have developed a framework, called GHAL360, to enable the MTR to learn a goal-oriented policy from reinforcements for guiding human attention using visual indicators. Three telepresence environments were constructed using datasets that are extracted from Matterport3D and collected from a real robot respectively. Experimental results show that GHAL360 outperformed the baselines from the literature in the efficiency of a human-MTR team completing target search tasks.