Human-robot interactions can change significantly depending on how autonomous humans perceive a robot to be. Yet, while previous work in the HRI community measured perceptions of human autonomy, there is little work on measuring perceptions of robot autonomy. In this paper, we present our progress toward the creation of the Robot Autonomy Perception Scale (RAPS): a theoretically motivated scale for measuring human perceptions of robot autonomy. We formulated a set of fifteen Likert scale items that are based on the definition of autonomy from Beer et al.'s work, which identifies five key autonomy components: ability to sense, ability to plan, ability to act, ability to act with an intent towards some goal, and an ability to do so without external control. We applied RAPS to an experimental context in which a robot communicated with a human teammate through different levels of Performative Autonomy (PA): an autonomy-driven strategy in which robots may "perform" a lower level of autonomy than they are truly capable of to increase human situational awareness. Our results present preliminary validation for RAPS by demonstrating its sensitivity to PA and motivate the further validation of RAPS.

翻译：人机交互会因人类对机器人自主性的感知程度不同而发生显著变化。然而，尽管人机交互领域先前的研究测量了人类对自身自主性的感知，却鲜有工作关注对人类感知机器人自主性的测量。本文介绍了我们在创建机器人自主性感知量表（RAPS）方面的进展：这是一个基于理论动机的、用于测量人类对机器人自主性感知的量表。我们基于Beer等人工作中对自主性的定义，制定了一套包含十五个李克特量表项目的问卷，该定义识别了五个关键的自主性构成要素：感知能力、规划能力、行动能力、为实现特定目标而有意行动的能力，以及在没有外部控制的情况下执行上述行动的能力。我们将RAPS应用于一个实验情境，在该情境中，机器人通过不同水平的表演性自主性（PA）与人类队友进行交互。PA是一种由自主性驱动的策略，即机器人可能“表演”低于其实际能力的自主性水平，以提高人类的情境意识。我们的结果通过展示RAPS对PA的敏感性，为其提供了初步验证，并推动了RAPS的进一步验证工作。