具身智能体与个性化：通过记忆利用的视角探讨挑战与解决方案
Embodied Agents Meet Personalization: Investigating Challenges and Solutions Through the Lens of Memory Utilization
AI总结：
本文提出MEMENTO框架，通过分析记忆利用中的挑战，设计了基于层次知识图谱的用户档案记忆模块，提升智能体在个性化任务中的表现。
摘要：
基于大语言模型的具身智能体在传统物体排列任务中表现出色，但提供利用过去交互中用户特定知识的个性化帮助则带来了新的挑战。我们通过智能体的记忆利用视角，从两个关键维度探讨这些挑战：物体语义（基于个人意义识别物体）和用户模式（回忆行为惯例中的序列）。为了评估这些能力，我们构建了MEMENTO，一个端到端的两阶段评估框架，包含单一记忆和联合记忆任务。我们的实验表明，当前智能体可以回忆简单的物体语义，但难以将顺序用户模式应用于规划。通过深入分析，我们发现两个关键瓶颈：处理多个记忆时的信息过载和协调失败。基于这些发现，我们探索了记忆架构方法以解决这些挑战。鉴于我们观察到事件记忆既能提供个性化知识，又能带来上下文学习的好处，我们设计了一个基于层次知识图谱的用户档案记忆模块，分别管理个性化知识，在单一和联合记忆任务上均取得了显著提升。项目网站：https://connoriginal.github.io/MEMENTO
英文摘要：
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
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。
