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.

翻译：具身智能旨在使机器人能够在复杂的现实环境中稳健地学习、推理和泛化。然而，现有方法通常难以应对部分可观测性、碎片化的空间推理以及异构记忆体的低效整合，限制了其进行长时域适应的能力。为解决这些问题，我们提出了RoboMemory，这是一种受大脑启发的框架，它将空间记忆、时间记忆、情景记忆和语义记忆统一在一个并行化架构中，以实现高效的长时域规划和交互式学习。其核心创新在于一个用于可扩展、一致性记忆更新的动态空间知识图谱，以及一个包含评判器模块的闭环规划器，用于自适应决策。在EmbodiedBench上进行的大量实验表明，以Qwen2.5-VL-72B-Ins实例化的RoboMemory，其平均成功率比其强大的基线模型提高了26.5%，甚至超越了闭源的SOTA模型Claude-3.5-Sonnet。真实世界试验进一步证实了其进行累积学习的能力，在重复任务中性能持续提升。我们的研究结果确立了RoboMemory作为记忆增强型具身智能体的可扩展基础，将认知神经科学的洞见与实用的机器人自主性连接起来。