主动投映（ASD）：通过隐式虚拟通信操控对机器人行为的感知
Active Shadowing (ASD): Manipulating Perception of Robotic Behaviors via Implicit Virtual Communication
AI总结：
针对现有隐式视觉通信需改变机器人物理行为导致次优的问题，提出基于增强现实阴影投影的主动投映（ASD）方法，可在保持机器人行为最优的同时提升可理解性，且信息性优于部分显式通信方法。
摘要：
显式通信常因信息传递的直接性受到重视，但在交互过程中需要注意力投入，会造成认知中断。另一方面，隐式通信有助于实现默契顺畅的交互，更适用于团队协作，但需要通过推理来解读。本文研究了一种借助增强现实视觉投影、利用阴影实现的新型隐式视觉通信（IVC），称为主动投映（active shadowing, ASD）。此前的IVC方法（例如可理解运动legible motion）常用于影响人们对机器人行为的感知，使其更易理解，但这类方法通常需要改变机器人的物理行为，导致行为次优。在本研究中，我们探究了如何利用ASD在不损失最优性的前提下实现类似效果。我们通过用户研究开展的评估表明，ASD能有效制造“错觉”，在保持物理行为最优的同时不牺牲其可理解性。我们还证明ASD可比其他显式通信方法提供更多信息，并考察了ASD效果减弱的适用条件。
英文摘要：
Explicit communication is often valued for its directness in presenting information but requires attention during exchange, resulting in cognitive interruptions. On the other hand, implicit communication contributes to tacit and smooth interaction, making it more suitable for teaming, but requires inference for interpretation. This paper studies a novel type of implicit visual communication (IVC) using shadows via visual projection with augmented reality, referred to as active shadowing (ASD). Prior IVC methods, such as legible motion, are often used to influence the perception of robot behavior to make it more understandable. They often require changing the physical robot behavior, resulting in suboptimality. In our work, we investigate how ASD can be used to achieve similar effects without losing optimality. Our evaluations with user studies demonstrates that ASD can effectively creates ''illusions'' that maintain optimal physical behavior without compromising its understandability. We also show that ASD can be more informative than other explicit communication methods, and examine the conditions under which ASD becomes less effective.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
