InternDataEngine is a synthetic data generation engine for embodied AI that powers large-scale model training and iteration. Built on NVIDIA Isaac Sim, it unifies high-fidelity physical interaction from InternData-A1, semantic task and scene generation from InternData-M1, and high-throughput scheduling from the Nimbus framework to deliver realistic, task-aligned, and massively scalable robotic manipulation data.
- More realistic physical interaction: Unified simulation of rigid, articulated, deformable, and fluid objects across single-arm, dual-arm, and humanoid robots, enabling long-horizon, skill-composed manipulation that better supports sim-to-real transfer.
- More diverse data generation: By leveraging the internal state of the simulation engine to extract high-quality ground truth, coupled with multi-dimensional domain randomization (e.g., layout, texture, structure, and lighting), the data distribution is significantly expanded. This approach produces precise and diverse operational data, while simultaneously exporting rich multimodal annotations such as bounding boxes, segmentation masks, and keypoints.
- More efficient large-scale production: Nimbus-powered asynchronous pipelines that decouple planning, rendering, and storage, achieving 2–3× end-to-end throughput, cluster-level load balancing and fault tolerance for billion-scale data generation.
- [2026/03] We release the InternDataEngine codebase v1.0, which includes the core modules: InternData-A1 and Nimbus.
Please refer to the Installation and Usage to start the installation and run your first synthetic data generation task.
For more details, please check Documentation.
All the code within this repo are under CC BY-NC-SA 4.0. Please consider citing our papers if it helps your research.
@article{tian2025interndata,
title={Interndata-a1: Pioneering high-fidelity synthetic data for pre-training generalist policy},
author={Tian, Yang and Yang, Yuyin and Xie, Yiman and Cai, Zetao and Shi, Xu and Gao, Ning and Liu, Hangxu and Jiang, Xuekun and Qiu, Zherui and Yuan, Feng and others},
journal={arXiv preprint arXiv:2511.16651},
year={2025}
}
@article{he2026nimbus,
title={Nimbus: A Unified Embodied Synthetic Data Generation Framework},
author={He, Zeyu and Zhang, Yuchang and Zhou, Yuanzhen and Tao, Miao and Li, Hengjie and Tian, Yang and Zeng, Jia and Wang, Tai and Cai, Wenzhe and Chen, Yilun and others},
journal={arXiv preprint arXiv:2601.21449},
year={2026}
}
@article{chen2025internvla,
title={Internvla-m1: A spatially guided vision-language-action framework for generalist robot policy},
author={Chen, Xinyi and Chen, Yilun and Fu, Yanwei and Gao, Ning and Jia, Jiaya and Jin, Weiyang and Li, Hao and Mu, Yao and Pang, Jiangmiao and Qiao, Yu and others},
journal={arXiv preprint arXiv:2510.13778},
year={2025}
}