[Submitted on 16 Jul 2026]
Title:Risk-Aware Preference Learning for Stochastic Outcomes
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Abstract:Learning reward functions from human preferences is a widely used approach for aligning robot behavior with user expectations in human-robot interaction. Most existing approaches assume that humans evaluate uncertain outcomes using expected utility (EU), aggregating outcome utilities linearly with their probabilities. However, behavioral evidence shows that humans are systematically risk-sensitive, overweighting rare negative events and exhibiting loss aversion. We study the consequences of this mismatch in social robot navigation, where safety-critical outcomes (e.g., collisions) are rare but highly consequential. We compare EU with Cumulative Prospect Theory (CPT), a nonlinear model of human decision-making, within a Bradley-Terry preference learning framework. Our preliminary experiments show that when preferences are generated by risk-sensitive users, CPT-based learners recover reward functions with substantially lower regret compared to EU-based learners. Our results highlight the importance of modeling human risk sensitivity when learning rewards from preferences over stochastic robot outcomes.
Comments: ICRA 2026 Workshop on Bridging the Gap between Robot Learning and Human-Robot Interaction
Subjects:
Robotics (cs.RO)
Cite as: arXiv:2607.15483 [cs.RO]
(or arXiv:2607.15483v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2607.15483
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Yi-Shiuan Tung [view email] [v1] Thu, 16 Jul 2026 21:59:38 UTC (24,794 KB)
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