RoboMemory：一种受大脑启发的多记忆代理框架，用于物理具身系统中的终身学习

RoboMemory: A Brain-inspired Multi-memory Agentic Framework for Lifelong Learning in Physical Embodied Systems

August 2, 2025

作者: Mingcong Lei, Honghao Cai, Zezhou Cui, Liangchen Tan, Junkun Hong, Gehan Hu, Shuangyu Zhu, Yimou Wu, Shaohan Jiang, Ge Wang, Zhen Li, Shuguang Cui, Yiming Zhao, Yatong Han

cs.AI

摘要

我们提出了RoboMemory，一种受大脑启发的多记忆框架，旨在解决物理具身系统中的终身学习问题，应对现实环境中的关键挑战：持续学习、多模块记忆延迟、任务关联捕捉以及闭环规划中的无限循环缓解。该框架基于认知神经科学，整合了四个核心模块：信息预处理器（类似丘脑）、终身具身记忆系统（类似海马体）、闭环规划模块（类似前额叶）和低级执行器（类似小脑），以实现长期规划和累积学习。作为框架的核心，终身具身记忆系统通过空间、时间、情景和语义子模块的并行更新/检索，缓解了复杂记忆框架中的推理速度问题。它结合了动态知识图谱（KG）和一致的架构设计，以增强记忆的一致性和可扩展性。在EmbodiedBench上的评估显示，RoboMemory在平均成功率上比开源基线（Qwen2.5-VL-72B-Ins）高出25%，并超越闭源的最先进技术（SOTA）（Claude3.5-Sonnet）5%，确立了新的SOTA。消融研究验证了关键组件（批评者、空间记忆、长期记忆），而实际部署则证实了其终身学习能力，在重复任务中显著提高了成功率。RoboMemory通过可扩展性缓解了高延迟挑战，为在物理机器人中集成多模态记忆系统提供了基础参考。

English

We present RoboMemory, a brain-inspired multi-memory framework for lifelong learning in physical embodied systems, addressing critical challenges in real-world environments: continuous learning, multi-module memory latency, task correlation capture, and infinite-loop mitigation in closed-loop planning. Grounded in cognitive neuroscience, it integrates four core modules: the Information Preprocessor (thalamus-like), the Lifelong Embodied Memory System (hippocampus-like), the Closed-Loop Planning Module (prefrontal lobe-like), and the Low-Level Executer (cerebellum-like) to enable long-term planning and cumulative learning. The Lifelong Embodied Memory System, central to the framework, alleviates inference speed issues in complex memory frameworks via parallelized updates/retrieval across Spatial, Temporal, Episodic, and Semantic submodules. It incorporates a dynamic Knowledge Graph (KG) and consistent architectural design to enhance memory consistency and scalability. Evaluations on EmbodiedBench show RoboMemory outperforms the open-source baseline (Qwen2.5-VL-72B-Ins) by 25% in average success rate and surpasses the closed-source State-of-the-Art (SOTA) (Claude3.5-Sonnet) by 5%, establishing new SOTA. Ablation studies validate key components (critic, spatial memory, long-term memory), while real-world deployment confirms its lifelong learning capability with significantly improved success rates across repeated tasks. RoboMemory alleviates high latency challenges with scalability, serving as a foundational reference for integrating multi-modal memory systems in physical robots.