Learning Visualization Policies of Augmented Reality for Human-Robot Collaboration

Kishan Chandan, Jack Albertson, Shiqi Zhang
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引用次数: 1

Abstract

In human-robot collaboration domains, augmented reality (AR) technologies have enabled people to visualize the state of robots. Current AR-based visualization policies are designed manually, which requires a lot of human efforts and domain knowledge. When too little information is visualized, human users find the AR interface not useful; when too much information is visualized, they find it difficult to process the visualized information. In this paper, we develop a framework, called VARIL, that enables AR agents to learn visualization policies (what to visualize, when, and how) from demonstrations. We created a Unity-based platform for simulating warehouse environments where human-robot teammates collaborate on delivery tasks. We have collected a dataset that includes demonstrations of visualizing robots' current and planned behaviors. Results from experiments with real human participants show that, compared with competitive baselines from the literature, our learned visualization strategies significantly increase the efficiency of human-robot teams, while reducing the distraction level of human users. VARIL has been demonstrated in a built-in-lab mock warehouse.
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面向人机协作的增强现实学习可视化策略
在人机协作领域,增强现实(AR)技术使人们能够可视化机器人的状态。目前基于ar的可视化策略都是手工设计的,这需要大量的人力和领域知识。当可视化的信息太少时,人类用户会觉得AR界面没有用;当太多的信息被可视化时,他们发现很难处理可视化的信息。在本文中,我们开发了一个名为VARIL的框架,使AR代理能够从演示中学习可视化策略(可视化什么,何时以及如何)。我们创建了一个基于unity的平台,用于模拟仓库环境,在仓库环境中,人机队友协作完成交付任务。我们收集了一个数据集,其中包括可视化机器人当前和计划行为的演示。真实人类参与者的实验结果表明,与文献中的竞争基线相比,我们的学习可视化策略显著提高了人机团队的效率,同时降低了人类用户的分心程度。VARIL已在一个内置实验室模拟仓库中进行了演示。
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