Augmented and mixed-reality techniques harbor a great potential for improving human-robot collaboration. Visual signals and cues may be projected to a human partner in order to explicitly communicate robot intentions and goals. However, it is unclear what type of signals support such a process and whether signals can be combined without adding additional cognitive stress to the partner. This paper focuses on identifying the effective types of visual signals and quantify their impact through empirical evaluations. In particular, the study compares static and dynamic visual signals within a collaborative object sorting task and assesses their ability to shape human behavior. Furthermore, an information-theoretic analysis is performed to numerically quantify the degree of information transfer between visual signals and human behavior. The results of a human subject experiment show that there are significant advantages to combining multiple visual signals within a single task, i.e., increased task efficiency and reduced cognitive load.

翻译：增强现实与混合现实技术为改善人机协作蕴含巨大潜力。视觉信号与线索可投射至人类伙伴，以明确传达机器人的意图与目标。然而，何种信号类型能支撑此类过程，以及信号能否在不增加伙伴额外认知负担的前提下进行组合，仍不明确。本文聚焦于识别有效的视觉信号类型，并通过实证评估量化其影响。具体而言，研究在协作物体分类任务中比较了静态与动态视觉信号，并评估其塑造人类行为的能力。此外，通过信息论分析对视觉信号与人类行为之间的信息传递程度进行了数值量化。人体实验结果表明，在单一任务中组合多种视觉信号具有显著优势，即提升了任务效率并降低了认知负荷。