AI 中文总结
研究针对现有触觉引导系统不足，提出LfH框架，通过从稀疏演示中学习用户偏好的安全干预，基于可微CBF优化层自动调整参数，经实验验证可学习个性化安全干预并减少反馈与偏好的不匹配。
AI 中文摘要
触觉反馈在人机共享控制中为传达安全意图提供了一个隐含通道。现有的触觉引导系统通常采用预定义的干预策略，无法适应个体用户或应用场景的多样安全偏好。为解决此局限，我们提出了一种从触觉学习（LfH）框架，该框架从稀疏演示中学习用户偏好的安全干预，无需手动试错设计。我们的框架基于一个基于可微控制障碍函数（CBF）的优化层构建，能自动调整基础安全参数以匹配演示的触觉响应。用户通过教导系统在遥操作期间期望的干预方式，而非直接调整控制器参数。所得的触觉引导反映了演示的干预偏好，同时保留了触觉共享控制的直观交互。仿真和硬件实验表明，该框架能从稀疏用户输入中学习个性化安全干预，并减少生成的触觉反馈与演示偏好之间的不匹配。
英文摘要
Haptic feedback provides an implicit channel for communicating safety intentions during human-robot shared control. Existing haptic guidance systems typically employ predefined intervention strategies that cannot accommodate the diverse safety preferences of individual users or application scenarios. To address this limitation, we propose a Learning from Haptics (LfH) framework that learns user-preferred safety interventions from sparse demonstrations, eliminating the need for manual trial-and-error design. Our framework is built on a differentiable Control Barrier Function (CBF)-based optimization layer that automatically adjusts the underlying safety parameters to match the demonstrated haptic responses. Instead of tuning controller parameters directly, users teach the system how they expect it to intervene during teleoperation. The resulting haptic guidance reflects the demonstrated intervention preferences while preserving the intuitive interaction of haptic shared control. Simulation and hardware experiments demonstrate that the proposed framework can learn personalized safety interventions from sparse user input and reduce the mismatch between the generated haptic feedback and the demonstrated preferences.
