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arXiv · 2109.10385

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

Kishan Chandan·Jack Albertson·Xiaohan Zhang·Xiaoyang Zhang·Yao Liu·Shiqi Zhang
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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.

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Kishan Chandan, Jack Albertson, Xiaohan Zhang, Xiaoyang Zhang, Yao Liu, Shiqi Zhang. 2021-09-21. Learning to Guide Human Attention on Mobile Telepresence Robots with 360 Vision. https://arxiv.org/abs/2109.10385

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