Scaling data volume and diversity is critical for generalizing embodied intelligence. While synthetic data generation offers a scalable alternative to expensive physical data acquisition, existing pipelines remain fragmented and task-specific. This isolation leads to significant engineering inefficiency and system instability, failing to support the sustained, high-throughput data generation required for foundation model training. To address these challenges, we present Nimbus, a unified synthetic data generation framework designed to integrate heterogeneous navigation and manipulation pipelines. Nimbus introduces a modular four-layer architecture featuring a decoupled execution model that separates trajectory planning, rendering, and storage into asynchronous stages. By implementing dynamic pipeline scheduling, global load balancing, distributed fault tolerance, and backend-specific rendering optimizations, the system maximizes resource utilization across CPU, GPU, and I/O resources. Our evaluation demonstrates that Nimbus achieves a 2-3X improvement in end-to-end throughput compared to unoptimized baselines and ensuring robust, long-term operation in large-scale distributed environments. This framework serves as the production backbone for the InternData suite, enabling seamless cross-domain data synthesis.
@article{arxiv.2601.21449,
title = {Nimbus: A Unified Embodied Synthetic Data Generation Framework},
author = {Zeyu He and Yuchang Zhang and Yuanzhen Zhou and Miao Tao and Hengjie Li and Hui Wang and Yang Tian and Jia Zeng and Tai Wang and Wenzhe Cai and Yilun Chen and Ning Gao and Jiangmiao Pang},
journal= {arXiv preprint arXiv:2601.21449},
year = {2026}
}