用行为经济学模型提升机器人决策安全性
Risk-Aware Preference Learning for Stochastic Outcomes
- 用前景理论替代期望效用，更贴近人类风险偏好
- 在罕见事故场景下，新方法误差降低约40%
- 适合研究人机交互与安全型机器人系统
从人类偏好中学习奖励函数是实现人机交互对齐的重要方法。现有方法多假设人类按期望效用（EU）评估不确定结果，将结果效用线性加权概率。然而行为研究表明，人类存在系统性风险敏感：高估罕见负面事件，表现出损失厌恶。本文在社交机器人导航任务中研究此偏差影响，其中碰撞等安全关键事件虽罕见但后果严重。我们在Bradley-Terry偏好学习框架中对比了欧氏期望效用（EU）与非线性前景理论（CPT）模型。初步实验显示，当用户具有风险敏感特征时，基于CPT的学習器相比EU方法显著降低后悔值。结果表明，在学习随机机器人行为偏好时，建模人类风险敏感性至关重要。
原文摘要 · Abstract (English)
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.
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