提升智能体个性化记忆能力，让助手更懂用户习惯与物品意义。
Embodied Agents Meet Personalization: Investigating Challenges and Solutions Through the Lens of Memory Utilization
- 构建双阶段评估框架，测试智能体对用户记忆的利用能力。
- 现有模型难处理用户行为序列，记忆过载导致规划失败。
- 设计分层知识图谱记忆模块，显著提升个性化任务表现。
基于大语言模型的具身智能体在常规物体重排任务中表现良好，但要提供基于用户历史交互的个性化服务，仍面临挑战。本文从记忆利用角度，聚焦两个关键维度：基于个人意义识别物体，以及回忆行为习惯中的动作序列。为此，我们构建了MEMENTO——一个端到端的两阶段评估框架，包含单记忆与联合记忆任务。实验表明，当前智能体可记住简单物体语义，但难以将用户行为序列用于规划。深入分析发现两大瓶颈：多记忆下的信息过载与协调失败。基于此，我们探索记忆架构改进方案。观察到情景记忆兼具个性化知识与上下文学习优势后，提出一种基于分层知识图谱的用户画像记忆模块，独立管理个性化信息，在单记忆与联合记忆任务上均取得显著提升。
原文摘要 · Abstract (English)
LLM-powered embodied agents have shown success on conventional object-rearrangement tasks, but providing personalized assistance that leverages user-specific knowledge from past interactions presents new challenges. We investigate these challenges through the lens of agents' memory utilization along two critical dimensions: object semantics (identifying objects based on personal meaning) and user patterns (recalling sequences from behavioral routines). To assess these capabilities, we construct MEMENTO, an end-to-end two-stage evaluation framework comprising single-memory and joint-memory tasks. Our experiments reveal that current agents can recall simple object semantics but struggle to apply sequential user patterns to planning. Through in-depth analysis, we identify two critical bottlenecks: information overload and coordination failures when handling multiple memories. Based on these findings, we explore memory architectural approaches to address these challenges. Given our observation that episodic memory provides both personalized knowledge and in-context learning benefits, we design a hierarchical knowledge graph-based user-profile memory module that separately manages personalized knowledge, achieving substantial improvements on both single and joint-memory tasks. Project website: https://connoriginal.github.io/MEMENTO
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。