发表机构
Kyung Hee University; Kyungpook National University（庆熙大学; 庆北国立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
TACET提出一种情境自适应声学-社交导航方法，通过行为令牌耦合视觉-语言推理与反应控制，同时优化四足机器人的移动路径和音量，在保持100%个人空间合规的同时降低噪声达9.3 dBA。
AI 中文摘要
四足机器人进入医院、养老院和安静的办公室时，不仅需要在移动位置上保持情境适应性，还需要在移动音量上保持情境适应性：腿式机器人的运动噪声（主要由脚与地面的撞击产生）本身就是一个社交变量。以往的社交导航方法尊重人类空间，但将机器人的声音视为统一的；而安静运动方法则将噪声降低到操作者指定的、不考虑情境的水平。我们提出TACET，一种情境自适应的声学-社交导航方法，它从机器人的自我中心视角推断社交情境，并决定其行走位置和音量，通过一个紧凑的行为令牌<步态，速度，社交代价>将慢速微调的视觉-语言推理器与快速反应控制器耦合。该令牌同时调节社交代价地图（去哪里）和安静运动策略（移动多大声），而结构化的视野外记忆在最近看到的人离开相机视野后仍将其保留在推理器的上下文中。在真实四足机器人上，情境条件运动在匹配速度下将运动噪声降低高达9.3 dBA，在我们的场景中，完整方法保持个人空间合规性为100%，且声学侵入性低（≤2.9 dBA），在评估场景中共同改善了空间和声学性能。项目页面可在以下网址获取：此 https URL。
英文摘要
Quadruped robots entering hospitals, care homes, and quiet offices must be context-appropriate not only in where they move but in how loudly they move: a legged robot's locomotion noise, dominated by foot-ground impacts, is itself a social variable. Prior social navigation respects human space but treats the robot as acoustically uniform, while quiet-locomotion methods reduce noise to an operator-specified, context-blind level. We present TACET, a context-appropriate acoustic-social navigation method that infers social context from the robot's egocentric view and decides both where it walks and how loudly, coupling a slow fine-tuned vision-language reasoner to a fast reactive controller through a single compact behavior token, <gait, speed, social_cost>. The same token conditions both a social costmap (where to go) and a quiet locomotion policy (how loudly to move), while a structured out-of-view memory keeps recently seen people in the reasoner's context after they leave the camera view. On a real quadruped, context-conditioned locomotion lowers locomotion noise by up to 9.3 dBA at matched speed, and across our scenarios the full method keeps personal-space compliance at 100% with low acoustic intrusion (<=2.9 dBA), jointly improving spatial and acoustic performance in the evaluated scenarios. The project page is available at https://rcilab.khu.ac.kr/tacet/.
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