This letter 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 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.

中文翻译：

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人类感知优化规划，实现舒适的基于 VR 的远程呈现


这封信通过考虑沉浸在远程机器人视角中的人类，介绍了一个新兴的运动规划问题。面临的挑战是使体验既有效（例如提供临场感）又舒适（例如避免恶心等不良症状）。我们将这个具有挑战性的新领域称为人类感知优化规划，并提出了一个可以在许多设想的场景中实例化的通用多目标优化框架。然后，我们将特定的 VR 远程呈现任务视为人类感知优化规划的案例，其中我们模拟一个机器人，将 360 度视频发送给远程用户，以便通过头戴式显示器进行查看。在这个特定的任务中，我们规划了最大限度地减少 VR 不适感（从而最大限度地提高舒适度）的轨迹。 A* 型方法用于创建分段线性轨迹的帕累托最优集合，同时考虑提高舒适度的标准。我们对参观虚拟博物馆的人类受试者进行了一项研究，其中将我们的算法计算的路径与基于 RRT 的参考轨迹进行比较。一般来说，用户较少遭受 VR 眩晕感，并且更喜欢由所提出的算法创建的路径。