On July 27, Physical AI data engine company Axis Robotics announced the completion of a $12 million seed funding round, led by Hack VC, with participation from Nomad Capital, Pi Network Ventures, 10K Ventures, and several angel investors. Axis Robotics stated that the funds will be used to build a large-scale, human-robot collaborative global data engine to address the data bottlenecks faced in the Physical AI field, including the scarcity of training data, insufficient model generalization capabilities, and data fragmentation across different robotic hardware. Unlike large language models that rely on vast amounts of internet text data, Physical AI requires extensive real human physical interaction trajectory data for training. Founder Chris of Axis Robotics mentioned that the company is constructing a sustainable data production system to accelerate the development of general robotic intelligence by continuously generating, collecting, and optimizing robot training data. Axis's 'composite data engine' integrates task generation, data collection, model training, and optimization processes, including: randomly generating data tasks involving different objects, spatial layouts, visual environments, and robot forms through a task generation engine; providing a browser-based remote operation platform for robot simulation to enhance data collection efficiency; collecting real-world human motion data through a mobile application; and automatically completing trajectory cleaning, domain randomization, and language annotation to generate multimodal datasets suitable for model training. The company reported that it has established a global robot data network with over 100,000 active contributors, capable of generating more than 1,200 hours of simulated data and over 20,000 hours of real-world first-person perspective data each month. Axis Robotics claims that its dataset has demonstrated performance advantages in robotic benchmarking. In the LIBERO-Plus test, the π0.5 model trained on Axis's diverse dataset improved its success rate by 4.9 percentage points, achieving a 31.3 percentage point increase compared to the RoboCasa365 benchmark dataset. On the commercialization front, Axis Robotics has established partnerships with companies such as Booster Robotics, Manycore Tech, Feagine Robotics, Dexmal, Lotus Car, and Geely Automobile, providing customized training data services for robot manufacturers, Physical AI model companies, and industrial automation enterprises. The company stated that it will use this round of funding to expand its data generation capabilities, grow its global contributor network, and further develop the core data infrastructure that supports the next generation of general robotic intelligence.