China advances standards for embodied AI data

China’s National Data Administration plans to accelerate standards for embodied artificial intelligence and direct local authorities on implementation, targeting one of the biggest bottlenecks in the fast-growing robotics sector: access to high-quality, diverse and large-scale training data.

The regulator said the work will focus on creating clearer rules for datasets used by embodied AI systems, which combine artificial intelligence with physical machines such as humanoid robots. The effort is intended to support more consistent data collection, processing, evaluation and use as companies race to improve machines that must perceive, reason and act in real-world environments.

China has already moved to build a broader standards framework around the sector. National standards published on August 27 cover the quality of real-world embodied-intelligence data and technical requirements for data-generation platforms, while several additional specifications remain under development.

Among projects overseen by the National Data Administration are proposed standards covering the sources and constituent elements of high-quality embodied-intelligence datasets, simulated synthetic-data generation and processing, and specifications for data collection and model training at training bases. The projects are being developed under the National Data Standardisation Technical Committee.

One draft programme, registered in April, sets a 12-month timetable for a standard on data collection and model training at embodied-intelligence training bases. Its drafting group includes the China Electronics Standardization Institute, Beijing Institute of Technology, the Institute of Software at the Chinese Academy of Sciences and robotics companies.

A separate draft addresses synthetic data, which is becoming increasingly important because collecting enough physical-world robot interactions can be expensive, slow and difficult to reproduce. China’s policy framework encourages the use of simulation and synthetic techniques to expand supply where genuine operating data are scarce or costly to obtain.

The National Data Administration has made high-quality datasets a central part of its wider artificial-intelligence strategy. An implementation plan issued in June called for faster construction of datasets in strategic and emerging fields including embodied intelligence, intelligent driving and the low-altitude economy. It also called for data covering physical interaction, environmental perception and motion control in key scenarios.

The plan directs authorities and industry participants to improve data cleaning, enhancement, labelling, alignment and quality inspection, while developing national standards covering formats, categories, annotation and quality assessment. The regulator said such work should produce datasets that are structurally complete, diverse, accurately labelled and suitable for model development.

China’s push reflects the distinct data demands of robots operating in the physical world. Unlike text-based systems trained largely on digital information, embodied AI requires large volumes of multimodal material that can include video, images, sensor readings, point clouds, movement trajectories and feedback from physical interaction.

Data supply has therefore emerged as a competitive constraint for developers seeking to train machines to perform long sequences of actions reliably across changing environments. The National Data Development Research Institute has said embodied intelligence and world models are driving sharply higher demand for three-dimensional, video and other multimodal data, while also raising requirements for collection and annotation quality.

The government has also been expanding the underlying pool of AI-ready datasets. At a national data industry event in Guiyang in August, the National Data Administration said more than 126,000 high-quality datasets had been built across China, with a combined volume exceeding 1,815 petabytes.

Officials described a system combining a national dataset management platform, an open-source community and a data-labelling network with sector-specific pilot programmes. The framework includes 106 leading organisations and 140 pilot tasks, with regional authorities encouraged to develop projects suited to local industrial strengths.

Embodied intelligence has featured in those pilots alongside other emerging fields. The administration has promoted cooperation among robotics companies, universities, research institutes and public bodies to increase the quantity and usefulness of machine-training data while testing standards before wider application.