Learning to Guide Human Attention on Mobile Telepresence Robots with 360 Vision
This addresses a specific challenge in human-robot interaction for telepresence, but it is incremental as it builds on existing methods for attention guidance.
The paper tackles the problem of guiding human attention on mobile telepresence robots with 360-degree vision to bridge the observability gap, showing that their GHAL360 framework outperforms baselines in efficiency for target search tasks.
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.