Nimbus: A Unified Embodied Synthetic Data Generation Framework

TL;DR

Nimbus is a unified, modular framework that significantly improves the efficiency and stability of synthetic data generation for embodied intelligence by integrating heterogeneous pipelines and optimizing resource utilization.

Contribution

It introduces a novel four-layer architecture with dynamic scheduling and fault tolerance, enabling scalable, high-throughput synthetic data generation across diverse tasks.

Findings

Achieves 2-3X throughput improvement over baselines

Supports robust, long-term large-scale data synthesis

Serves as backbone for InternData suite

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.