统一合成数据生成框架，提升智能体训练的数据效率与稳定性。
Nimbus: A Unified Embodied Synthetic Data Generation Framework
- 模块化四层架构，异步处理路径规划、渲染与存储。
- 吞吐量提升2-3倍，支持大规模分布式长期运行。
- 适合需要跨领域合成数据的智能体研究与工业级训练。
扩展数据量和多样性对提升智能体泛化能力至关重要。尽管合成数据生成可作为昂贵物理数据采集的可扩展替代方案，现有流水线仍呈碎片化且任务专用，导致工程效率低下和系统不稳定，难以满足基础模型训练所需的持续高吞吐数据生成需求。为此，我们提出Nimbus——一个统一的合成数据生成框架，旨在集成异构的导航与操作流水线。Nimbus采用解耦执行模型，将轨迹规划、渲染与存储分为异步阶段，通过动态调度、全局负载均衡、分布式容错及后端渲染优化，最大化利用CPU、GPU与I/O资源。评估表明，Nimbus相比未优化基线实现2-3倍的端到端吞吐提升，并在大规模分布式环境中保持稳健运行。该框架已作为InternData系列的数据生产核心，支持跨领域数据无缝合成。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。