Due to the need for personalised intelligent robot training, the personalised intelligent data industry has exploded in recent years. From robot “students” who work uninterrupted in artificial intelligence training grounds, to human collectors wearing sensors all over the body in a moving cage, to simulation training grounds that generate 100 pieces of data from a single piece of data in a virtual world, the embodied intelligent data industry is continuously attracting participants to join this wave of physical AI. Among them, real machine data, data collected without a body, and simulation data have become the three major types of data feed for realistic intelligent training, corresponding to different production methods and production limitations. While the industry is rapidly expanding the scale of data, practitioners believe that data quality and data evaluation standards are critical.

Due to the need for personalised intelligent robot training, the personalised intelligent data industry has exploded in recent years. From robot “students” who work uninterrupted in artificial intelligence training grounds, to human collectors wearing sensors all over the body in a moving cage, to simulation training grounds that generate 100 pieces of data from a single piece of data in a virtual world, the embodied intelligent data industry is continuously attracting participants to join this wave of physical AI. Among them, real machine data, data collected without a body, and simulation data have become the three major types of data feed for realistic intelligent training, corresponding to different production methods and production limitations. While the industry is rapidly expanding the scale of data, practitioners believe that data quality and data evaluation standards are critical.