感受咬食：基于鲁棒口腔感知与物理交互感知控制的机器人辅助口内咬食递送
Feel the Bite: Robot-Assisted Inside-Mouth Bite Transfer using Robust Mouth Perception and Physical Interaction-Aware Control
AI总结：
针对重度行动障碍者需口内直接喂食的需求，本研究提出融合抗遮挡多视角口腔感知与多模态交互识别控制的口内咬食递送系统，经13名受护者测试验证了舒适性、安全性与高接受度。
摘要：
机器人辅助喂食可极大改善行动受限人群的生活。当前的喂食系统能够夹取食物并将其放置在受护者嘴前以供咬食。然而，许多重度行动障碍者无法前倾身体，需要将食物直接送入口内。这要求极高的精准度，尤其是对于张口受限的人群，同时还需要对各类物理交互做出恰当反应——包括餐具在口内移动时的偶然接触、肌肉突然痉挛引发的冲击性接触、被喂食者为引导餐具做出的刻意舌部动作，以及主动咬食动作。本文提出了一套口内咬食递送系统，通过两个核心组件应对上述挑战：一是对工具遮挡具有鲁棒性的多视角口腔感知流水线，二是采用多模态时间序列分类来识别并响应不同物理交互的控制机制。我们通过两项消融实验验证了各组件的有效性。在完整系统评估中，我们的系统成功为13名存在不同行动障碍的受护者完成喂食。参与者一致强调该口内咬食递送系统的舒适性与安全性，并给出了很高的技术接受度评分——凸显了其在现实场景中的变革性潜力。补充材料与视频可访问http://emprise.cs.cornell.edu/bitetransfer/ 查看。
英文摘要：
Robot-assisted feeding can greatly enhance the lives of those with mobility limitations. Modern feeding systems can pick up and position food in front of a care recipient's mouth for a bite. However, many with severe mobility constraints cannot lean forward and need direct inside-mouth food placement. This demands precision, especially for those with restricted mouth openings, and appropriately reacting to various physical interactions - incidental contacts as the utensil moves inside, impulsive contacts due to sudden muscle spasms, deliberate tongue maneuvers by the person being fed to guide the utensil, and intentional bites. In this paper, we propose an inside-mouth bite transfer system that addresses these challenges with two key components: a multi-view mouth perception pipeline robust to tool occlusion, and a control mechanism that employs multimodal time-series classification to discern and react to different physical interactions. We demonstrate the efficacy of these individual components through two ablation studies. In a full system evaluation, our system successfully fed 13 care recipients with diverse mobility challenges. Participants consistently emphasized the comfort and safety of our inside-mouth bite transfer system, and gave it high technology acceptance ratings - underscoring its transformative potential in real-world scenarios. Supplementary materials and videos can be found at http://emprise.cs.cornell.edu/bitetransfer/ .
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
