{"ID":2887656,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.01415","arxiv_id":"2508.01415","title":"RoboMemory: A Brain-inspired Multi-memory Agentic Framework for Interactive Environmental Learning in Physical Embodied Systems","abstract":"Embodied intelligence aims to enable robots to learn, reason, and generalize robustly across complex real-world environments. However, existing approaches often struggle with partial observability, fragmented spatial reasoning, and inefficient integration of heterogeneous memories, limiting their capacity for long-horizon adaptation. To address this, we introduce RoboMemory, a brain-inspired framework that unifies Spatial, Temporal, Episodic, and Semantic memory within a parallelized architecture for efficient long-horizon planning and interactive learning. Its core innovations are a dynamic spatial knowledge graph for scalable, consistent memory updates and a closed-loop planner with a critic module for adaptive decision-making. Extensive experiments on EmbodiedBench show that RoboMemory, instantiated with Qwen2.5-VL-72B-Ins, improves the average success rate by 26.5% over its strong baseline and even surpasses the closed-source SOTA, Claude-3.5-Sonnet. Real-world trials further confirm its capability for cumulative learning, with performance consistently improving over repeated tasks. Our results position RoboMemory as a scalable foundation for memory-augmented embodied agents, bridging insights from cognitive neuroscience with practical robotic autonomy.","short_abstract":"Embodied intelligence aims to enable robots to learn, reason, and generalize robustly across complex real-world environments. However, existing approaches often struggle with partial observability, fragmented spatial reasoning, and inefficient integration of heterogeneous memories, limiting their capacity for long-hori...","url_abs":"https://arxiv.org/abs/2508.01415","url_pdf":"https://arxiv.org/pdf/2508.01415v7","authors":"[\"Mingcong Lei\",\"Honghao Cai\",\"Yuyuan Yang\",\"Yimou Wu\",\"Jinke Ren\",\"Zezhou Cui\",\"Liangchen Tan\",\"Junkun Hong\",\"Gehan Hu\",\"Shuangyu Zhu\",\"Shaohan Jiang\",\"Ge Wang\",\"Junyuan Tan\",\"Zhenglin Wan\",\"Zheng Li\",\"Zhen Li\",\"Shuguang Cui\",\"Yiming Zhao\",\"Yatong Han\"]","published":"2025-08-02T15:39:42Z","proceeding":"cs.RO","tasks":"[\"cs.RO\",\"cs.AI\"]","methods":"[]","has_code":false}