发表机构
Kiel University（基尔大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出COOL框架，通过好奇心驱动的数据收集从日常观察中学习物体所有权，维护长时空间记忆，实现个性化机器人辅助的导航与任务执行。
AI 中文摘要
机器人越来越多地被期望在日常环境中提供个性化服务。为此，它们必须理解诸如“我的背包在哪里？”或“找到我的瓶子”这样的自然语言指令，并通过推理物体实例、人物、位置和所有权来执行这些指令。这具有挑战性，因为所有权很少被明确标注，必须从人类与物体互动的长期行为证据中推断出来。为了解决这个问题，我们提出了COOL，一个新颖的机器人框架，能够从日常观察中自主学习物体所有权，并维护其环境的长时空间记忆。为了保持记忆的时效性，COOL采用了一种基于智能体的好奇心驱动数据收集策略，引导机器人前往最有价值的位置以获取信息并刷新过时的观察。离线实验、消融研究和真实世界评估表明，COOL能够从真实世界互动中推断所有权关系，并利用这些知识进行所有权条件下的导航和任务执行。
英文摘要
Robots are increasingly expected to provide personalized services in everyday environments. To do so, they must ground natural-language commands such as "Where is my backpack?" or "Find my bottle" and execute them by reasoning about object instances, people, locations, and ownership. This is challenging because ownership is rarely labeled explicitly and must be inferred from long-term, behavioral evidence of human-object interactions. To address this, we present COOL, a novel robotic framework for autonomously learning object ownership from everyday observations and maintaining a long-term spatial memory of its environment. To keep its memory current, COOL uses an agent-based curiosity-driven data collection strategy that guides the robot toward the most promising locations to gain information and refresh stale observations. Offline experiments, ablation studies, and real-world evaluations show that COOL can infer ownership relations from real-world interactions and use this knowledge for ownership-conditioned navigation and task execution.
CommentsAccepted to CoRL 2026
