学习遗忘 -- 分层事件记忆用于终身机器人部署
Learning to Forget -- Hierarchical Episodic Memory for Lifelong Robot Deployment
AI总结：
本文提出H$^2$-EMV框架，通过用户交互帮助机器人学习选择性遗忘，以应对长期多模态感知存储限制，实验证明其在减少内存和查询时间的同时保持回答准确性，并随时间适应用户需求。
摘要：
机器人在被要求
英文摘要：
Robots must verbalize their past experiences when users ask "Where did you put my keys?" or "Why did the task fail?" Yet maintaining life-long episodic memory (EM) from continuous multimodal perception quickly exceeds storage limits and makes real-time query impractical, calling for selective forgetting that adapts to users' notions of relevance. We present H$^2$-EMV, a framework enabling humanoids to learn what to remember through user interaction. Our approach incrementally constructs hierarchical EM, selectively forgets using language-model-based relevance estimation conditioned on learned natural-language rules, and updates these rules given user feedback about forgotten details. Evaluations on simulated household tasks and 20.5-hour-long real-world recordings from ARMAR-7 demonstrate that H$^2$-EMV maintains question-answering accuracy while reducing memory size by 45% and query-time compute by 35%. Critically, performance improves over time - accuracy increases 70% in second-round queries by adapting to user-specific priorities - demonstrating that learned forgetting enables scalable, personalized EM for long-term human-robot collaboration.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
