Physical AI Data Resources in 2026: Companies and Dataset Scale

Physical AI Data Resources in 2026: Companies and Dataset Scale

With the emergence of VLA models, World Models and robotics technologies, the need for Physical AI datasets grows constantly. Robots need to learn not only about their environment, but also about the way humans and robots perform certain actions, interact with objects, and carry out continuous tasks.

Different kinds of data are used to train Physical AI models. From Robot Data and Teleoperation Data to Ego-Centric Data, Human Demonstration Data, Simulation Data, and Tactile Data, various types of data are utilized in training and teaching robots how to perceive the environment, manipulate and perform tasks.

At the same time, an increasing number of companies build Physical AI data resources and offer datasets for robotics and AI model development. According to publicly available information and known dataset scale, the following companies are examples of Physical AI data resources in the market.

Physical AI Datasets Are Becoming Larger and More Diverse

Considering the currently available data resources, Physical AI datasets are no longer limited to robot data. Robot Teleoperation Data provides operation data closely associated with robot actions. Ego-Centric and Human Demonstration Data capture how people perform different tasks in various environments. Simulation and Tactile Data provide more diversity in scenarios and types of information for models to learn from.

Physical AI datasets are also growing in size, with the amount of data in robot manipulation, human demonstration, Ego-Centric interaction, and simulation increasing. Nexdata continues to develop its Physical AI data resources, adding 10,000+ hours of Dexterous Hand Teleoperation Data, 135,000+ hours of Ego-Centric Data, and 288 million 3D Models & Scenes. Nexdata is also planning to expand its Ego-Centric data resources toward million-hour scale, with a wider variety of scenarios and tasks.

As Physical AI models are moving towards more complex and continuous tasks, data scale alone would not be sufficient anymore. Task diversity, scenario coverage, data modalities, and connection with robot operations will become more significant.