Based on the generative simulation world model, the cross-dimensional intelligent open-source embodied intelligence toolchain EmbodiChain

On January 20, 2026, Trans-Dimensional Intelligence announced the official open-sourcing of EmbodiChain, an embodied intelligence toolchain based on the generative simulated world model.

EmbodiChain is an embodied intelligence toolchain based on the generative simulated world model that can automatically train VLA models and successfully deploy them on real machines. It does not rely on real data. Instead, it trains VLA models with 100% synthetic data and can be directly deployed on real robots, achieving zero-shot transfer between virtual and real worlds.

Based on an end-to-end automated process, EmbodiChain integrates generative scenario construction and agent skill exploration to create an efficient closed-loop of "simulation - training - deployment". Through the automated generation technology of task scenarios and training data, it shortens the construction of high-quality training processes from months to days. It also builds a full - link evaluation system covering automatic scenario generation, skill discovery, and real - machine verification, providing a complete open - source benchmark for the practical application of embodied intelligence models.

In the field of large language models, the massive Internet text data has led to the emergence of intelligence. However, this successful paradigm is difficult to replicate in the field of robotics. The core contradiction lies in the essential difference in data: LLMs rely on the cleaning of existing data, while embodied intelligence requires incremental data that complies with physical laws. The passage of physical time and the limit of labor costs have always restricted the breakthrough of data scale.

The core concept of EmbodiChain is to "replace collection with generation". Through generative simulation technology, it creates an uninterrupted "online data stream", completely abandoning the inefficient traditional model of "generation - storage - reading". Its technical framework consists of three innovative modules:

World Generation: Through the Real2Sim and Gen2Sim modules, the engine can automatically generate physically consistent 3D scenarios and task environments based on a small number of real samples or language instructions, achieving full automation of data production.

Data Augmentation and Self - Repair: The system not only randomizes physical parameters and enhances visual diversity but also automatically generates correction trajectories when the robot fails in a task, forming a closed - loop mechanism of "error - learning" and significantly improving the model's robustness.

Privileged Information Driven: EmbodiChain provides "God's - eye view" information (such as object masks and spatial relationships) that is invisible in the real world, forcing the model to understand the physical essence rather than just surface pixels, which is highly consistent with the world model concept advocated by Yann LeCun.

Different from the "video - generative world model", EmbodiChain adheres to the 3D interactive and physically accurate generative simulation route. By providing privileged information (precise object masks, spatial relationships, and affordance labels), it forces the model to understand the geometric and physical essence of the scene rather than just fitting surface pixels, ensuring that the trained strategies are stable and reliable in the real world.

To verify the effectiveness of generative data, Trans - Dimensional Intelligence conducted an extreme test: training the Sim2Real - VLA model only with 100% simulated data and completely abandoning real data. The test showed that the model had a relatively high operation success rate in the real environment and exhibited strong robustness under interferences such as changing tablecloths and moving objects. This result proves that generative simulation data is not only feasible but may even be superior to traditional methods by avoiding over - fitting noise.

In the future, Trans - Dimensional Intelligence will gradually release the VLA base model automatically trained by EmbodiChain and examples of multiple specific tasks, providing a set of standardized infrastructure for the community.

The open - sourcing of EmbodiChain is a crucial step for Trans - Dimensional Intelligence to promote the collaborative development of the industry. Its goal is to make EmbodiChain the "water, electricity, and gas" in the field of embodied intelligence, enabling researchers to get rid of the physical labor of data collection and storage pressure and accelerating the implementation of embodied intelligence research and applications.