Core Viewpoint - The article discusses the transition of artificial intelligence from the virtual internet space to the physical world, emphasizing the importance of enabling intelligent agents to understand three-dimensional spaces and align natural language with real-world environments [3][42]. Group 1: Research and Development - A new model has been proposed by a collaborative research team from Tsinghua University, Beijing Academy of Artificial Intelligence, Beijing Institute of Technology, and Beihang University, which unifies spatial understanding and active exploration for intelligent agents [3][4]. - The model allows agents to build cognitive maps of their environments through dynamic exploration, enhancing spatial perception and autonomous navigation capabilities [3][4]. Group 2: Embodied Navigation - In embodied navigation tasks, agents must interpret human instructions and navigate complex physical spaces to locate target positions, requiring both understanding and exploration [5][10]. - The navigation process consists of two interwoven steps: understanding the task and actively exploring the environment, similar to human navigation behavior [5][10]. Group 3: Research Challenges - Key challenges identified include real-time semantic representation, collaborative training of exploration and understanding, and efficient data collection methods [11][12][13]. - The model aims to create an online 3D semantic map that integrates spatial and semantic information while continuously processing data from RGB-D streams [11]. Group 4: Model Design and Data Collection - The proposed model includes two core modules: online spatial memory construction and spatial reasoning and decision-making, which are optimized in a unified training framework [17][18]. - A hybrid data collection strategy combines real RGB-D scanning data with virtual simulation environments, resulting in a dataset with over 900,000 navigation trajectories and millions of language descriptions [23][24]. Group 5: Experimental Results - The MTU3D model was evaluated across four key tasks, demonstrating significant improvements in success rates compared to existing methods, particularly in multi-modal understanding and long-term task planning [27][28]. - In the GOAT-Bench benchmark, MTU3D achieved success rates of 52.2%, 48.4%, and 47.2%, outperforming other models by over 20% [27][28]. Group 6: Future Implications - The integration of understanding and exploration in MTU3D allows AI to autonomously navigate and comprehend instructions in real-world environments, paving the way for advancements in embodied navigation [42].