通过实时检测阻力与用户反馈，让助穿机器人更安全自适应。
Hybrid Control Strategies for Safe and Adaptive Robot-Assisted Dressing
- 结合力觉监测与用户反馈，动态调整动作轨迹。
- 实测中显著提升任务连续性与安全性。
- 适合需高安全性的康复辅助场景，如老人助穿。
安全、可靠性和用户信任在人-机器人交互（HRI）中至关重要，尤其在机器人需实时应对风险的场景中。本研究针对机器人助穿（RAD）中的潜在危险，如衣物卡滞和用户不适，提出两种低层控制策略：(1) 衣物卡滞控制策略，通过检测异常受力，可触发聊天机器人请求用户干预或自主调整路径；(2) 用户不适/疼痛缓解策略，根据用户反馈动态降低速度，必要时终止任务。通过物理穿衣实验评估，结果表明融合力监测与用户反馈能有效提升安全性和任务连续性。研究强调混合控制的重要性——在自主干预、用户参与和可控终止之间取得平衡，并依赖双向交互与实时用户驱动的自适应能力，为更响应迅速且个性化的HRI系统铺平道路。
原文摘要 · Abstract (English)
Safety, reliability, and user trust are crucial in human-robot interaction (HRI) where the robots must address hazards in real-time. This study presents hazard driven low-level control strategies implemented in robot-assisted dressing (RAD) scenarios where hazards like garment snags and user discomfort in real-time can affect task performance and user safety. The proposed control mechanisms include: (1) Garment Snagging Control Strategy, which detects excessive forces and either seeks user intervention via a chatbot or autonomously adjusts its trajectory, and (2) User Discomfort/Pain Mitigation Strategy, which dynamically reduces velocity based on user feedback and aborts the task if necessary. We used physical dressing trials in order to evaluate these control strategies. Results confirm that integrating force monitoring with user feedback improves safety and task continuity. The findings emphasise the need for hybrid approaches that balance autonomous intervention, user involvement, and controlled task termination, supported by bi-directional interaction and real-time user-driven adaptability, paving the way for more responsive and personalised HRI systems.
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