发表机构
University of Toronto（多伦多大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出PARAssist框架，利用视觉-语言模型结合用户历史与环境信息，个性化消除服务机器人模糊请求的歧义，经实验验证其个性化能力及核心组件的有效性。
AI 中文摘要
服务机器人可能遇到需要上下文感知推理的模糊用户请求，用户在请求机器人辅助特定任务时也可能有独特偏好。我们提出PARAssist（Personalized and Adaptive Robotic Assistance，个性化自适应机器人辅助），这是一种用于服务机器人以个性化方式消除请求歧义的独特架构。PARAssist利用视觉-语言模型确定用户任务的物理和认知需求，并通过对比用户独立执行的任务与向机器人请求的任务的需求，被动学习用户的辅助偏好。当收到模糊请求时，会从用户的动作、活动、位置、对话、请求历史，以及当前用户和环境状态中生成候选任务，随后根据学习到的用户偏好模型对候选任务进行评估，以推荐合适的辅助选项。对PARAssist开展的实验表明，个性化可使歧义消除与用户先前辅助请求的任务需求相匹配；消融研究证实了PARAssist主要组件在个性化歧义消除方面的贡献。
英文摘要
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.
CommentsThis work has been submitted to the IEEE for possible publication
