Core Viewpoint - The article discusses RoboMemory, a brain-inspired multi-memory framework designed for lifelong learning in physical embodied systems, addressing key challenges in dynamic real-world environments [2][4]. Group 1: Framework Overview - RoboMemory is designed to tackle four core challenges: continuous learning capability, multi-module memory latency, task relevance capture, and avoidance of deadlock in closed-loop planning [2]. - The framework integrates four core modules: information preprocessing system (thalamus-like function), lifelong embodied memory system (hippocampus-like function), closed-loop planning module (prefrontal cortex-like function), and low-level executors (cerebellum-like function) [2]. Group 2: Memory System Features - The lifelong embodied memory system features parallel updating and retrieval mechanisms across four sub-modules: spatial memory, temporal memory, episodic memory, and semantic memory, effectively resolving reasoning speed bottlenecks in complex memory architectures [2]. - The system employs dynamic knowledge graphs and a consistent architecture design, significantly enhancing memory coherence and scalability [2]. Group 3: Application and Impact - The article emphasizes the importance of memory systems for embodied agents in real-world environments, highlighting the need for continuous learning capabilities [4][6]. - The discussion includes the pain points faced by embodied agents in real environments and how a robust memory system can address these challenges [6].