让机器人理解模糊指令并按用户习惯智能响应
PARAssist: A Framework for Personalized and Adaptive Robotic Assistance from Ambiguous User Requests
- 用视觉语言模型分析任务需求，被动学习用户偏好
- 结合历史行为与当前状态生成候选任务，匹配用户习惯
- 实验证明个性化提升指令消歧准确率，适合服务机器人场景
服务机器人常面临模糊用户指令，需结合上下文进行推理。用户在请求协助时也存在个性化偏好。本文提出PARAssist（个性化自适应机器人辅助框架），利用视觉语言模型识别用户任务的物理与认知需求，并通过对比用户自主完成与请求机器人协助的任务需求，被动学习其偏好。当接收到模糊请求时，系统基于用户的历史动作、活动、位置、对话及请求记录，结合当前用户与环境状态生成任务候选，并依据已学习的用户偏好模型筛选合适的协助选项。实验表明，个性化策略能有效对齐任务消歧与用户过往请求的实际需求。消融实验证实了框架各组件在个性化消歧中的关键作用。
原文摘要 · Abstract (English)
Service robots may encounter ambiguous user requests that require context-aware inference. Users may also have unique preferences with certain tasks when requesting robotic assistance. We introduce PARAssist (Personalized and Adaptive Robotic Assistance), a unique architecture for disambiguating requests in a personalized manner for service robots. PARAssist utilizes vision-language models to determine the physical and cognitive demands of a user's tasks, and passively learns user preferences for assistance by contrasting the demands of tasks the user performs independently with those they request from the robot. When an ambiguous request is received, task candidates are generated from the history of the user's actions, activities, locations, conversations, and requests, as well as the current user and environment state. Task candidates are then evaluated against the learned user preference model to suggest suitable assistance options. Experiments conducted with PARAssist show that personalization can align disambiguation with the task demands of a user's prior assistance requests. An ablation study confirms the contributions of PARAssist's main components in personalizing disambiguation.
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