用软体物理模拟优化机器人喂食咬合安全，提升舒适度。
Simulating Safe Bite Transfer in Robot-Assisted Feeding with a Soft Head and Articulated Jaw
- 构建带柔性头的软体动力学模型，实现骨骼驱动柔性结构。
- 仿真表明直线插入直线拔出策略能最小化受力，提升舒适度。
- 适合研究人机交互安全的机器人系统设计者参考。
由于机器人辅助喂食需密切的人机物理交互，确保咬合转移的安全与舒适极具挑战。本文提出一种基于物理引擎（MuJoCo）的新方法，通过软体动力学建模人机交互。将柔性头部模型与刚性骨架结合，并考虑内部动力学，使柔性结构可由骨架驱动。仿真中引入真实皮肤接触动力学，可系统评估咬合参数（如插入深度、进入角度）对用户安全与舒适的影响。结果表明，在头颅静止假设下，直线插入-直线拔出策略能最小化受力，显著提升舒适度。该仿真方法为实际实验提供更安全可控的替代方案。
原文摘要 · Abstract (English)
Ensuring safe and comfortable bite transfer during robot-assisted feeding is challenging due to the close physical human-robot interaction required. This paper presents a novel approach to modeling physical human-robot interaction in a physics-based simulator (MuJoCo) using soft-body dynamics. We integrate a flexible head model with a rigid skeleton while accounting for internal dynamics, enabling the flexible model to be actuated by the skeleton. Incorporating realistic soft-skin contact dynamics in simulation allows for systematically evaluating bite transfer parameters, such as insertion depth and entry angle, and their impact on user safety and comfort. Our findings suggest that a straight-in-straight-out strategy minimizes forces and enhances user comfort in robot-assisted feeding, assuming a static head. This simulation-based approach offers a safer and more controlled alternative to real-world experimentation. Supplementary videos can be found at: https://tinyurl.com/224yh2kx.
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