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arXiv · 2002.10696

Human Perception-Optimized Planning for Comfortable VR-Based Telepresence

Israel Becerra·Markku Suomalainen·Eliezer Lozano·Katherine J. Mimnaugh·Rafael Murrieta-Cid·Steven M. LaValle
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Abstract

This paper introduces an emerging motion planning problem by considering a human that is immersed into the viewing perspective of a remote robot. The challenge is to make the experience both effective (such as delivering a sense of presence) and comfortable (such as avoiding adverse sickness symptoms, including nausea). We refer to this challenging new area as human perception-optimized planning and propose a general multiobjective optimization framework that can be instantiated in many envisioned scenarios. We then consider a specific VR telepresence task as a case of human perception-optimized planning, in which we simulate a robot that sends 360 video to a remote user to be viewed through a head-mounted display. In this particular task, we plan trajectories that minimize VR sickness (and thereby maximize comfort). An A* type method is used to create a Pareto-optimal collection of piecewise linear trajectories while taking into account criteria that improve comfort. We conducted a study with human subjects touring a virtual museum, in which paths computed by our algorithm are compared against a reference RRT-based trajectory. Generally, users suffered less from VR sickness and preferred the paths created by the presented algorithm.

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Israel Becerra, Markku Suomalainen, Eliezer Lozano, Katherine J. Mimnaugh, Rafael Murrieta-Cid, Steven M. LaValle. 2020-02-25. Human Perception-Optimized Planning for Comfortable VR-Based Telepresence. https://doi.org/10.1109/lra.2020.3015191

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