Toward RAPS: the Robot Autonomy Perception Scale

TL;DR

This work tackles the lack of multi-dimensional tools to measure perceived robot autonomy in HRI by introducing the Robot Autonomy Perception Scale (RAPS). Built on Beer et al.'s five autonomy components, RAPS comprises 15 items on a 7-point Likert scale to capture sense, plan, act, goal-directed intent, and self-determination without external control. In a preliminary validation using Performative Autonomy manipulations during a latency-affected collaboration task, RAPS showed high reliability ($\alpha=0.928$) and strong sensitivity to PA effects ($BF_{10}=5.34\times10^5$), particularly for planning, acting, and autonomous control, while sensing and goal-directedness remained unaffected. These findings support RAPS as a promising instrument for assessing perceived robot autonomy and guiding future scale refinement and broader validation in varied HRI contexts.

Abstract

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.

Figures (2)

• Figure 1: Reduced visualization of the player game interface. Resource stations show the type of resources needed to clear that spot. Cleared resource stations turn black.
• Figure 2: Effects of PA Strategy on perceived autonomy. Error bars represent 95% confidence interval.