RoboMemory: A Brain-inspired Multi-memory Agentic Framework for Lifelong
  Learning in Physical Embodied Systems
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.
689183e9f01a094725f83527
Mingcong Lei
689183e9f01a094725f83528
Honghao Cai
689183e9f01a094725f83529
Zezhou Cui
689183e9f01a094725f8352a
Liangchen Tan
689183e9f01a094725f8352b
Junkun Hong
689183e9f01a094725f8352c
Gehan Hu
689183e9f01a094725f8352d
Shuangyu Zhu
689183e9f01a094725f8352e
Yimou Wu
689183e9f01a094725f8352f
Shaohan Jiang
689183e9f01a094725f83530
Ge Wang
689183e9f01a094725f83531
Zhen Li
689183e9f01a094725f83532
Shuguang Cui
689183e9f01a094725f83533
Yiming Zhao
689183e9f01a094725f83534
Yatong Han
RoboMemory: A Brain-inspired Multi-memory Agentic Framework for Lifelong
  Learning in Physical Embodied Systems
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.
RoboMemory, a brain-inspired multi-memory framework, enhances lifelong learning in physical robots by integrating cognitive neuroscience principles and achieving state-of-the-art performance in real-world tasks.
Information Preprocessor Lifelong Embodied Memory System Closed-Loop Planning Module Low-Level Executer Spatial submodule Temporal submodule Episodic submodule Semantic submodule Knowledge Graph dynamic Knowledge Graph consistent architectural design memory consistency memory scalability lifelong learning ablation studies real-world deployment EmbodiedBench Qwen2.5-VL-72B-Ins Claude3.5-Sonnet state-of-the-art SOTA critic spatial memory long-term memory