Core Viewpoint - Shanghai AI Laboratory has launched the "Intern-Robotics" embodied full-stack engine, addressing key challenges in the embodied intelligence sector and promoting a shift from fragmented development to full-stack mass production [3][4][9]. Group 1: Technological Innovations - Intern-Robotics integrates virtual simulation modeling, real-virtual data connectivity, and training-testing integration, creating a comprehensive solution for the entire chain of embodied intelligence from data collection to application [4][10]. - The engine allows for the development of a single model that can adapt to over 10 types of robotic forms, significantly enhancing the efficiency of model training and deployment across different robot types [6][9]. - Data collection costs have been reduced to 0.06% compared to previous solutions, thanks to the integration of real machine data and virtual synthesized data [6][10]. Group 2: Addressing Industry Challenges - The embodied intelligence field faces three main bottlenecks: lack of unified standards, high data costs, and long R&D cycles. Intern-Robotics provides systematic solutions to these issues [9][10]. - The engine supports six major tasks and over 20 datasets, enabling efficient training and evaluation, thus significantly shortening the development cycle [10][11]. Group 3: Collaborative Initiatives - The "Embodied Intelligence Photosynthesis Plan" has been initiated to empower training centers, robotic companies, and developer communities, fostering innovation and technology breakthroughs [5][20]. - The plan has already attracted 15 organizations, including leading robotics companies, to collaborate on the development and training using Intern-Robotics [5][20]. Group 4: Engine Components - Intern-Robotics consists of three core engines: simulation, data, and training-testing, which together meet the full-stack production needs of embodied intelligence [11][14]. - The simulation engine allows for easy switching of scenarios, robots, and evaluation metrics, significantly lowering the learning curve for developers [13][14]. - The data engine combines physical simulation and generative AI to create high-quality, low-cost data, enhancing the diversity and quality of training datasets [14][15].