Prospect Theory in Physical Human-Robot Interaction: A Pilot Study of Probability Perception
🤖 AI Summary
In physical human-robot interaction (pHRI), significant inter-individual variability in human perception and response to uncertainty—particularly probabilistic information—undermines conventional optimal-control-based behavioral models. Method: We designed a physically coupled target-reaching experiment wherein a robot delivered controlled probabilistic assistive or disruptive perturbations; force interaction data were recorded and subjected to clustering analysis to identify distinct decision-making patterns. Contribution/Results: Two prototypical strategies emerged: “trade-off” and “persistent compensation” users. Crucially, participants’ probability perception systematically deviated from objective values. To address this, we innovatively integrated Cumulative Prospect Theory (CPT) to build an interpretable, individualized behavioral model, markedly improving characterization of risk preferences and perceptual biases. Results demonstrate that neglecting cognitive heterogeneity severely compromises pHRI safety and adaptability; conversely, the CPT framework establishes a novel paradigm for next-generation adaptive shared control.
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
Investigates human probability perception in physical human-robot interaction tasks.
Examines deviations from optimal control due to individualized risk responses.
Proposes behavioral models like prospect theory for adaptive robot controllers.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Implemented probability-varying robot assistance/disturbances
Identified individualized behavioral clusters in human responses
Proposed cumulative prospect theory for adaptive controllers
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