Nimbus: A Unified Embodied Synthetic Data Generation Framework

Zeyu He; Yuchang Zhang; Yuanzhen Zhou; Miao Tao; Hengjie Li; Hui Wang; Yang Tian; Jia Zeng; Tai Wang; Wenzhe Cai; Yilun Chen; Ning Gao; Jiangmiao Pang

arXiv · 2601.21449

Nimbus: A Unified Embodied Synthetic Data Generation Framework

Zeyu He·Yuchang Zhang·Yuanzhen Zhou·Miao Tao·Hengjie Li·Hui Wang·Yang Tian·Jia Zeng·Tai Wang·Wenzhe Cai·Yilun Chen·Ning Gao·Jiangmiao Pang

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Abstract

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.

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Zeyu He, Yuchang Zhang, Yuanzhen Zhou, Miao Tao, Hengjie Li, Hui Wang, Yang Tian, Jia Zeng, Tai Wang, Wenzhe Cai, Yilun Chen, Ning Gao, Jiangmiao Pang. 2026-01-29. Nimbus: A Unified Embodied Synthetic Data Generation Framework. https://arxiv.org/abs/2601.21449

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