Toward RAPS: the Robot Autonomy Perception Scale

TL;DR

This paper introduces the Robot Autonomy Perception Scale (RAPS), a new tool designed to measure how humans perceive robot autonomy, which is crucial for understanding and improving human-robot interactions.

Contribution

The paper presents the development of RAPS, a theoretically grounded scale with fifteen items, and demonstrates its preliminary validation in an experimental context involving different levels of Performative Autonomy.

Findings

RAPS is sensitive to variations in Performative Autonomy levels.

Preliminary validation shows RAPS can effectively measure perceived robot autonomy.

The scale is based on five key components of autonomy from prior theoretical work.

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.