让具身智能体拥有「空间感」！清华、北航联合提出类脑空间认知框架，导航、推理、做早餐样样精通

Core Viewpoint - The article discusses the innovative BSC-Nav framework developed by Tsinghua University and Beihang University, which enhances embodied intelligence in robots by integrating a structured spatial memory system inspired by biological cognition, enabling robots to perform complex navigation and interaction tasks autonomously [4][11][42]. Group 1: BSC-Nav Overview - BSC-Nav is the first unified framework inspired by the spatial cognition mechanisms of the biological brain, providing advanced navigation capabilities and enabling higher-level spatial perception and interaction tasks [7][8]. - The framework addresses the limitations of existing AI models in physical environments, particularly their short-term memory and poor generalization in dynamic settings [8][11]. Group 2: Memory Components - BSC-Nav incorporates three key memory components: Landmark Memory Module, Cognitive Map Module, and Working Memory Module, which collectively replicate human spatial cognition [12][17][18]. - The Landmark Memory Module identifies and records significant objects in the environment, while the Cognitive Map Module creates a global cognitive map based on observed features [16][17]. - The Working Memory Module allows the robot to retrieve and reconstruct relevant spatial memories for task execution, enhancing its reasoning and generalization capabilities [18][19]. Group 3: Performance Validation - Extensive experiments in the Habitat simulation environment demonstrated BSC-Nav's superior performance across four major navigation tasks, achieving new state-of-the-art results [20][24]. - In object navigation tasks, BSC-Nav achieved a success rate of 78.5%, surpassing the previous best method by 24% [24]. - The framework also excelled in complex instruction navigation and active embodied question answering, showcasing its ability to understand and execute intricate tasks [25][28][31]. Group 4: Real-World Application - BSC-Nav was tested in a real-world environment, achieving over 80% navigation success rate across various tasks, demonstrating its strong generalization capabilities [35][38]. - The robot successfully performed complex operations, including the multi-step task of preparing breakfast, highlighting its practical applicability [38][43]. Group 5: Future Directions - The research emphasizes that the evolution of embodied intelligence may not solely rely on computational power but can be significantly enhanced through effective memory systems [41][42]. - Future plans include expanding the memory framework to more dynamic environments and complex cognitive tasks, aiming for further advancements in embodied AI [42].