发表机构
HDC LABS（HDC实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出床边机器人Nuni，将检测到的痛苦线索作为询问理由，通过姿态混合流水线在机器人视角下提升线索识别，并验证先问式控制器能限制不确定感知的后果。
AI 中文摘要
身体线索识别可以支持辅助机器人，但基准准确率并不能保证在机器人摄像头视角下的可靠行为。我们提出了Nuni，一个床边机器人原型，它将检测到的痛苦线索视为询问的理由而非警报的理由。我们比较了两个X3D-UGT RGB外观分类器（在NTU RGB+D上分别达到97.7%和94.8%的六类准确率）与一个以姿态为中心的混合流水线，后者在从机器人摄像头录制的28个单人脚本片段上进行了测试。混合路径实现了0.71的六类宏召回率，而微调和从零训练的RGB变体分别为0.25和0.29。更重要的是在交互方面，混合路径在16个痛苦片段中的12个中产生了触发询问的痛苦线索，并且在8个正常片段中的2个中会不必要地触发询问；RGB变体仅在16个痛苦片段中的2个和3个中产生了触发询问的线索。我们通过事件注入单独测试了先问式控制器。所有13个状态转换试验均通过：有效响应导致停止，两次未回答的提示产生一次警报，三个边界条件被正确处理。这些结果是初步的技术评估，而非用户研究或医学验证，但它们展示了交互策略如何限制不确定感知的后果。
英文摘要
Body-cue recognition can support assistive robots, but benchmark accuracy does not guarantee reliable behavior under a robot-camera viewpoint. We present Nuni, a bedside robot prototype that treats a detected distress cue as a reason to ask rather than a reason to alert. We compare two X3D-UGT RGB appearance classifiers, which reach 97.7% and 94.8% six-way accuracy on NTU RGB+D, with a pose-centric hybrid pipeline on 28 single-actor scripted clips recorded from the robot camera. The hybrid path achieved 0.71 six-way macro recall, versus 0.25 and 0.29 for the fine-tuned and from-scratch RGB variants. More importantly for interaction, it produced a question-triggering distress cue in 12/16 distress clips and would have prompted unnecessarily in 2/8 normal clips; the RGB variants yielded a question-triggering cue in only 2/16 and 3/16 distress clips. We separately tested the question-first controller through event injection. All 13 state-transition trials passed: valid responses caused stand-down, two unanswered prompts produced one alert, and three boundary conditions were handled correctly. These results are a preliminary technical evaluation, not a user study or medical validation, but they show how interaction policy can limit the consequences of uncertain perception.
CommentsTo appear in the 4th Workshop on Nonverbal Cues for Human-Robot Cooperative Intelligence (NOC), IROS 2026
