Core Insights - The article discusses the advancements in embodied intelligence technology led by the Shanghai AI Laboratory, particularly focusing on the open-source release of the Intern-Robotics engine, which aims to transition from fragmented development to full-stack production in the field of embodied intelligence [3]. Group 1: Technological Developments - The Shanghai AI Laboratory has launched the Intern-Robotics engine, which includes simulation, data, and training engines, achieving over 140,000 downloads of related models and datasets [3]. - A series of technical advancements have been introduced based on Intern-Robotics, covering navigation, operation, humanoid robot motion models, and datasets, with open-source releases starting from September 14, 2025 [3][4]. Group 2: Navigation Models - The InternVLA N1 navigation model integrates long-range spatial reasoning and agile execution, achieving international leading scores in six mainstream benchmark tests and demonstrating zero-shot generalization across scenes and entities at a continuous reasoning efficiency of 60Hz [6]. Group 3: Operation Models - The InternVLA M1 operation model creates a complete feedback loop covering "perception-planning-action," enhancing spatial reasoning and planning capabilities through a two-stage training strategy [11]. - The InternVLA A1 model focuses on high-dynamic scene multi-robot collaboration, showing superior performance in real-world evaluations compared to existing models [12]. Group 4: Reinforcement Learning - The VLAC model enhances reinforcement learning efficiency in real-world scenarios by providing continuous and reliable supervision signals, effectively distinguishing between normal and abnormal behaviors [13]. Group 5: Datasets and Evaluation - The InternScenes dataset contains approximately 40,000 indoor scenes and 1.96 million 3D object data, significantly surpassing existing datasets, providing a solid foundation for research in embodied and spatial intelligence [15]. - The OmniWorld dataset integrates large-scale synthetic data and multi-source heterogeneous data, facilitating advancements in world modeling and significantly improving performance in tasks like reconstruction and rendering [16]. Group 6: Community and Support - The article promotes the establishment of a community focused on embodied intelligence, offering resources for academic support, technical discussions, and collaboration opportunities among developers and researchers in the field [20][21].