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Improving Responsiveness to Robots for Tacit Human-Robot Interaction via Implicit and Naturalistic Team Status Projection

2023-01-24 03:54:00
Andrew Boateng, Wenlong Zhang, Yu Zhang

Abstract

Fluent human-human teaming is often characterized by tacit interaction without explicit communication. This is because explicit communication, such as language utterances and gestures, are inherently interruptive. On the other hand, tacit interaction requires team situation awareness (TSA) to facilitate, which often relies on explicit communication to maintain, creating a paradox. In this paper, we consider implicit and naturalistic team status projection for tacit human-robot interaction. Implicitness minimizes interruption while naturalness reduces cognitive demand, and they together improve responsiveness to robots. We introduce a novel process for such Team status Projection via virtual Shadows, or TPS. We compare our method with two baselines that use explicit projection for maintaining TSA. Results via human factors studies demonstrate that TPS provides a more fluent human-robot interaction experience by significantly improving human responsiveness to robots in tacit teaming scenarios, which suggests better TSA. Participants acknowledged robots implementing TPS as more acceptable as a teammate and favorable. Simultaneously, we demonstrate that TPS is comparable to, and sometimes better than, the best-performing baseline in maintaining accurate TSA

Abstract (translated)

流利的人类-人类团队合作通常以没有明确沟通的暗合互动为特征。这是因为明确沟通,如语言发表和手势,本身就是干扰性的。另一方面,暗合互动需要团队情况意识(TSA)来促进,而常常依赖于明确沟通来维持,创造了一个矛盾。在本文中,我们考虑了暗合和自然化的团队状态投射,以暗合人类-机器人互动。暗合最小化干扰,自然化减少认知需求,它们一起改进对机器人的反应能力。我们介绍了一种通过虚拟阴影或TPS的方法,用于暗合团队状态投射的新颖过程。我们比较了维持TSA的明确投射基准的两个基线。通过人因学研究表明,TPS通过显著改善暗合团队场景中人类对机器人的反应能力,提供了更流畅的人类-机器人互动体验,这暗示了更好的TSA。参与者承认,机器人实施TPS更可以接受作为队友和有利可图。同时,我们表明,TPS与,有时比,维持准确TSA的最佳表现基准相当。

URL

https://arxiv.org/abs/2301.09800

PDF

https://arxiv.org/pdf/2301.09800.pdf


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