# NVIDIA Isaac Sim: Enabling Scalable, GPU-Accelerated Simulation for Robotics

arXiv: https://arxiv.org/abs/2606.03551 · HTML: https://mlsanity.com/paper/2606.03551/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jun 2026.

## Built on by (most cited first, 5 of 5)

- [A Definition and Roadmap for World Models](https://mlsanity.com/paper/2607.06401/) (2026)
- [Learning to Fold: prizewinning solution at LeHome Challenge 2026 (1st place online, 2nd offline)](https://mlsanity.com/paper/2606.27163/) (2026)
- [Ensuring Safe Physical AI in Urban Mobility via Hazard-Informed Synthesized Envelopes](https://mlsanity.com/paper/2608.14481/) (2026)
- [Learning Multi-Agent Task Assignment and Navigation in the Factory: from Simulation to Real Robots](https://mlsanity.com/paper/2609.14567/) (2026)
- [SonoWorld: Towards Interactive Pose-Controllable Ultrasound World Models](https://mlsanity.com/paper/2610.19327/) (2026)

## Abstract (arXiv)

Simulation has become a core infrastructure for robotics research. Unlike previous simulators, NVIDIA Isaac Sim leverages GPU acceleration to enable large-scale parallel training and physics-accurate modeling. Its synthetic data generation pipeline alleviates the scarcity of high-quality training data, supporting data-driven robot learning and large-scale simulation-centric experimentation. However, existing surveys often treat it as one simulator among many, without a systematic analysis of its architectural characteristics, usage patterns, and limitations. This survey reviews Isaac Sim from system and application perspectives, outlining its architecture and comparing it with widely used simulators. We analyze representative studies across five major domains and summarize common usage patterns, particularly in data generation and high-fidelity simulation. We also outline key future directions and challenges, including physics open-world learning, simulation-centric training and practical usability constraints.