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Mobile telepresence robots (MTRs) allow people to navigate and interact witha 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 equippedwith an all-degree visual perception capability. However, people's visual fieldhorizontally spans only about 120 degree of the visual field captured by therobot. To bridge this observability gap toward human-MTR shared autonomy, wehave developed a framework, called GHAL360, to enable the MTR to learn agoal-oriented policy from reinforcements for guiding human attention usingvisual indicators. Three telepresence environments were constructed usingdatasets that are extracted from Matterport3D and collected from a real robotrespectively. Experimental results show that GHAL360 outperformed the baselinesfrom the literature in the efficiency of a human-MTR team completing targetsearch tasks.

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