发表机构
University of Illinois at Urbana-Champaign; University of Toronto; Virginia Tech（伊利诺伊大学厄巴纳-香槟分校; 多伦多大学; 弗吉尼亚理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对重复人机交互问题，提出BAIT控制器将分层粒子滤波器与信念感知规划器集成，能推断人类信念变化，优化长期影响与人类信任的权衡，在多场景实验中任务性能与基线相当且用户信任更高。
AI 中文摘要
重复的人机交互（HRI）需要主动考虑人类如何不断适应对机器人不断演变的信念。先前的框架通常将交互视为孤立事件，随着人类感知漂移，任务性能会累积下降，或者通过不稳定、不可预测的行为维持长期影响，这会削弱人类信任且计算复杂。为解决这些问题，我们引入信念感知影响与信任（BAIT）控制器。BAIT将分层粒子滤波器与信念感知模型预测路径积分规划器集成，能推断人类战略快速转变和感知信念缓慢更新。BAIT明确优化长期影响与人类信任间的权衡，同时将即时任务性能作为严格约束。通过模拟、人体研究和实际车道合并场景中的GEM车辆部署实验，BAIT实现了与通过不可预测性优化长期影响的基线相当的任务性能，同时产生了显著更高的用户信任。
英文摘要
Repeated human-robot interaction (HRI) requires proactively accounting for humans who continually adapt to evolving beliefs about the robot. Prior frameworks often treat encounters as isolated events, suffering cumulative task performance decay as human perception drifts, or maintain long-term influence through erratic, unpredictable behavior that erodes perceived human trust and relies on computationally unscalable formulations. To address these gaps, we introduce the Belief- Aware Influence and Trust (BAIT) controller. BAIT integrates a hierarchical particle filter, which infers both fast human strategic shifts and slow perceptual belief updates, with a belief-aware Model Predictive Path Integral planner. BAIT explicitly optimizes the trade-off between long-horizon influence and human trust, while enforcing immediate task performance as a strict constraint. Across simulations, a human-subject study, and a real-world GEM vehicle deployments in repeated lane-merging scenarios, BAIT achieves task performance comparable to baselines that optimize long-term influence through unpredictability while yielding significantly higher user trust. The video demonstrating our experiments is available at https://youtu.be/9o4GqKLWDCw.
Comments9 pages, 4 figures, submitted to IEEE Robotics and Automation Letter (RA-L)
