让机器人在重复互动中动态适应人类信念，兼顾任务表现与信任提升
Belief-Aware Influence and Trust (BAIT): Shaping Human Belief During Repeated Human-Robot Interaction
- 用分层粒子滤波器追踪人类策略变化和信念更新
- 在仿真、实验和真实车辆场景中保持高任务性能与信任度
- 适合长期人机协作系统设计，如自动驾驶交互
重复的人机交互（HRI）需主动应对人类持续演化的机器人认知。现有框架常将互动视为孤立事件，导致因人类感知漂移引发任务性能累积下降；或依赖不可预测行为维持长期影响，损害用户信任且计算成本过高。为此，本文提出信念感知的影响与信任（BAIT）控制器。BAIT融合分层粒子滤波器，实时推断人类快速策略变化与缓慢信念更新，并结合信念感知的模型预测路径积分规划器，在保证即时任务性能约束的前提下，显式优化长时影响与用户信任之间的权衡。在仿真、真人实验及真实世界GEM车辆的多次变道场景部署中，BAIT在任务表现上媲美依赖不可预测性优化长期影响的基线方法，同时显著提升用户信任。视频演示见https://youtu.be/9o4GqKLWDCw。
原文摘要 · Abstract (English)
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.
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