让机器人主动保持用户参与感，避免过度依赖故障触发交互。
Beyond Failure Recovery: An Engagement-Aware Human-in-the-loop Framework for Robotic Systems
- 基于用户互动动态模型，预判参与度变化并规划交互时机。
- 实测在模拟与真实场景中均提升用户体验且任务成功率不变。
- 适合行动受限者使用，平衡参与感与认知负荷，特别适用于陪护机器人。
传统人机协同方法仅在机器人遭遇失败或不确定性时才引入人类干预，将人视为提升性能的工具。但在以人为中心的机器人场景中，尤其是物理照护领域，用户因行动受限可能无法及时响应，导致长期被动观察，降低参与感。频繁交互又会增加负担。为此，本文提出面向参与度的模型预测控制（E-MPC），通过建模用户参与度随交互频率和类型的变化，主动规划交互策略，在保证任务成功的同时，维持用户适度参与。在多种用户角色的仿真评估中验证了有效性；并通过真实用户研究（模拟行动障碍者）在机器人喂食系统上测试，结果表明该方法显著提升用户体验，同时保持任务完成率。
原文摘要 · Abstract (English)
Conventional human-in-the-loop approaches typically involve users only when a robot encounters failure or uncertainty, treating humans primarily as tools for improving robot performance. However, in many human-centered robotics settings, interaction should support engagement by keeping users involved in decision-making rather than limiting them to failure-driven interventions. This is particularly compelling in physical caregiving, where mobility limitations can reduce users' ability to intervene or modulate the robot's behavior in the moment. As a result, failure-driven interaction policies may relegate users to passive observers for long stretches of the task. For example, a user with mobility limitations may feel less engaged when being continuously and passively fed by a robot. At the same time, overly frequent interaction can be tiring and increase the user's workload. To address this trade-off, we propose Engagement-aware MPC (E-MPC), a user-engagement-aware method that plans interaction to maintain engagement while respecting a workload constraint. E-MPC leverages a user interaction dynamics model that captures how user engagement evolves as a function of both the frequency and type of interaction. Rather than requesting input only when difficulties arise during task execution, the robot proactively considers the user's preferred level of engagement throughout the task, balancing autonomy and interaction while ensuring task success. We evaluate E-MPC in simulation with several ablations and baseline comparisons. Results demonstrate the effectiveness of our approach across diverse user personas. In addition, we conduct a real-world user study with participants with emulated mobility limitations on a robot-assisted bite acquisition system, showing that E-MPC improves user experience while maintaining task success.
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