Embodied AI Data Solution

The primary objective of Nexdata’s Embodied AI Data Solutions is to overcome a fundamental limitation in embodied intelligence development: the lack of scalable, real-world data that enables AI systems to learn perception, decision-making, and physical control in realistic environments. As embodied AI moves beyond simulation and laboratory settings, traditional data approaches—focused on static perception or synthetic scenarios—have proven insufficient for training reliable, deployable systems.

To address this gap, the work was designed with a clear goal: to industrialize embodied AI data production and make real-world learning repeatable, scalable, and model-ready. A central part of this objective was the creation of a dedicated Embodied AI Data Factory. Spanning over 4,000 square meters and equipped with 100+ different models of humanoid robots and over 50+ different models of robotic hands, encompassing mainstream robot brands and types, such as Unitree, Franka, Leju, Linker, etc. The facility enables controlled yet realistic data collection across diverse task environments such as retail spaces, factories, pharmacies, and automotive repair scenarios.

Beyond physical data capture, the solution was built to support the entire embodied AI data lifecycle. Data collected in the factory is processed through standardized cleaning and synchronization pipelines, then enriched with structured annotations covering environment context, behavior trajectories, decision logic, and control signals. These datasets are designed to directly support imitation learning, reinforcement learning, and Vision-Language-Action (VLA) models, reducing the gap between raw data and effective training.

Another intended goal was to transform complex embodied data generation into an enterprise-ready capability. By integrating human-in-the-loop workflows, intelligent pre-annotation engines, and multi-layer quality control, the solution ensures both scalability and semantic accuracy.

Ultimately, this work aims to move embodied AI from experimental development toward real-world deployment by providing the data foundation required for intelligent systems to perceive, decide, and act reliably in physical environments.