Prospect Theory in Physical Human-Robot Interaction: A Pilot Study of Probability Perception

TL;DR

This pilot study investigates how humans perceive and respond to probability-based uncertainties in physical human-robot interaction, revealing individualized behaviors and the need for models like prospect theory to improve robot control strategies.

Contribution

It provides empirical evidence of diverse human responses to probabilistic disturbances in pHRI and advocates for incorporating prospect theory into behavioral models.

Findings

Two behavioral clusters identified: trade-off and always-compensate.

Human probability perception often deviates from actual probabilities.

Highlights the importance of adaptive, interpretable models for robot control.

Abstract

Understanding how humans respond to uncertainty is critical for designing safe and effective physical human-robot interaction (pHRI), as physically working with robots introduces multiple sources of uncertainty, including trust, comfort, and perceived safety. Conventional pHRI control frameworks typically build on optimal control theory, which assumes that human actions minimize a cost function; however, human behavior under uncertainty often departs from such optimal patterns. To address this gap, additional understanding of human behavior under uncertainty is needed. This pilot study implemented a physically coupled target-reaching task in which the robot delivered assistance or disturbances with systematically varied probabilities (10\% to 90\%). Analysis of participants' force inputs and decision-making strategies revealed two distinct behavioral clusters: a "trade-off" group that modulated their physical responses according to disturbance likelihood, and an "always-compensate" group characterized by strong risk aversion irrespective of probability. These findings provide empirical evidence that human decision-making in pHRI is highly individualized and that the perception of probability can differ to its true value. Accordingly, the study highlights the need for more interpretable behavioral models, such as cumulative prospect theory (CPT), to more accurately capture these behaviors and inform the design of future adaptive robot controllers.