分析性概念中心记忆用于具身操作代理
Analytic Concept-Centric Memory for Agentic Embodied Manipulation
AI总结：
提出分析性概念中心记忆框架，通过结构化概念（语义部件、参数模板等）组织经验，支持粗到细检索，提升长时程具身操作中的物体重识别、状态推理和技能复用。
摘要：
长时程具身操作要求代理记住持久物体、跟踪变化场景状态并复用先前交互知识。然而，现有代理记忆通常存储为非结构化历史或基于嵌入的记录，难以检索操作相关的物体部件、物理状态、动作效果和可执行技能。我们提出一种分析性概念中心记忆框架用于具身操作代理。我们的记忆围绕结构化分析概念组织经验，其中物体由语义部件、参数模板、接地姿态、可供性和操作状态表示。它进一步将物体和场景记忆与动作诱导状态变化的转换记忆以及用于模板接地和策略接地的技能记忆连接起来。在运行时，代理执行结构化粗到细检索以识别相关物体、状态、转换和技能，支持状态一致推理和技能复用。在依赖记忆的操作、铰接物体泛化、真实世界记忆评估和消融实验上的实验表明，我们的方法在任务完成度、检索准确率、物体重识别和跨物体技能泛化方面优于非结构化和基于嵌入的记忆基线。
英文摘要：
Long-horizon embodied manipulation requires agents to remember persistent objects, track changing scene states, and reuse prior interaction knowledge. However, existing agent memories are often stored as unstructured histories or embedding-based records, making it difficult to retrieve manipulation-relevant object parts, physical states, action effects, and executable skills. We propose an analytic concept-centric memory framework for agentic embodied manipulation. Our memory organizes experience around structured analytic concepts, where objects are represented by semantic parts, parametric templates, grounded poses, affordances, and manipulation states. It further connects object and scene memories with transition memory for action-induced state changes and skill memory for template-grounded and policy-grounded execution. At runtime, the agent performs structured coarse-to-fine retrieval to identify relevant objects, states, transitions, and skills, supporting state-consistent reasoning and skill reuse. Experiments on memory-dependent manipulation, articulated-object generalization, real-world memory evaluation, and ablations show that our approach improves task completion, retrieval accuracy, object re-identification, and cross-object skill generalization over unstructured and embedding-based memory baselines.
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。
