北大&智源研究院最新！RoboOS-NeXT：“记忆 + 分层架构” 实现通用多机器人协作

Core Insights - The article discusses the RoboOS-NeXT framework, which addresses the challenges in multi-robot collaboration by integrating a unified memory system and a hierarchical architecture for effective task execution and fault tolerance [1][4][23]. Group 1: Challenges in Multi-Robot Collaboration - Current multi-robot collaboration faces a "triple dilemma": reliance on single-robot memory, difficulty in adapting to heterogeneous robots, and lack of fault recovery capabilities [2][3]. - Existing solutions either fail to accumulate long-term experience or struggle with dynamic task allocation and fault tolerance [2][3]. Group 2: RoboOS-NeXT Framework - RoboOS-NeXT employs a "spatio-temporal entity unified memory (STEM)" and a "brain-cerebellum architecture" to facilitate global memory sharing and dynamic task execution [3][4]. - The framework consists of two core components: STEM for information integration and the brain-cerebellum model for planning and execution [4][9]. Group 3: Core Components of RoboOS-NeXT - STEM integrates spatial, temporal, and entity memories, providing a unified interface for all robots and eliminating information silos [6][7][8]. - Brain-Cerebellum Architecture separates global planning from local execution, ensuring efficient task decomposition and precise action control [9][10]. Group 4: Execution Workflow - The execution process involves four steps: task decomposition, dynamic scheduling, distributed execution, and dynamic memory updating [10][12]. - This workflow ensures that tasks are efficiently completed, even in the face of robot failures or tool malfunctions [10][12]. Group 5: Experimental Results - RoboOS-NeXT demonstrated superior performance in various scenarios, showing strong lifelong adaptability, collaboration scalability, and fault recovery capabilities [13][14][15]. - In adaptability tests, RoboOS-NeXT maintained a success rate of over 75% in long-sequence tasks, while the baseline without memory failed completely [13][14]. - The framework also showed significant improvements in execution efficiency, with average execution steps per task reduced by 20%-70% compared to the baseline [17][18]. Group 6: Key Conclusions and Future Directions - The unified memory is essential for collaboration, enabling lifelong adaptability and robust scheduling [23][25]. - Future enhancements may include multi-modal memory integration, end-to-end task optimization, and real-time performance improvements [25][26].