RoboMemory: A Brain-inspired Multi-memory Agentic Framework for Lifelong
  Learning in Physical Embodied Systems
Authors: name: Mingcong Lei; name: Honghao Cai; name: Zezhou Cui; name: Liangchen Tan; name: Junkun Hong; name: Gehan Hu; name: Shuangyu Zhu; name: Yimou Wu; name: Shaohan Jiang; name: Ge Wang; name: Zhen Li; name: Shuguang Cui; name: Yiming Zhao; name: Yatong Han
Published: 2025-08-02 15:39:42

Abstract: 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.