一句话：首个面向 Isaac Sim 的模糊测试方法，利用 LLM 语义阶段分割与多级变异提升覆盖率和缺陷检测能力。
机器人模拟器是具身智能的基础设施，NVIDIA Isaac Sim 因 GPU 加速物理引擎与逼真渲染而流行，但其复杂性引入软件缺陷，影响仿真可靠性。现有模糊测试难以应对上下文相关对象语义、层次化仿真控制和庞大状态空间。本文提出 IcFuzz，首个面向 Isaac Sim 的模糊测试方法：先利用 LLM 进行语义阶段分割，将仿真程序分解为捕获对象语义的结构化阶段；据此设计多级变异算子，在层次化粒度上系统测试模拟器；并用多臂老虎机自适应调度变异算子以高效探索状态空间。评估显示，IcFuzz 代码覆盖率达基线的约 190%–205%，三轮 12 小时测试平均检测 3.7 个唯一崩溃，基线未发现崩溃；四个月内发现 11 个缺陷，其中 9 个被确认或修复。
Robotics simulators serve as a foundational infrastructure for embodied AI, facilitating safe and scalable robotic system development. NVIDIA Isaac Sim has emerged as one of the most popular simulators, distinguished by its GPU-accelerated physics engine and photorealistic rendering, which enable high-fidelity modeling of complex environments. However, its inherent complexity inevitably introduces software bugs that can compromise simulation reliability. Existing fuzzing approaches struggle to test Isaac Sim effectively due to challenges of context-aware object semantics, hierarchical simulation control, and a vast simulation state space. In this paper, we propose IcFuzz, the first fuzzing approach for Isaac Sim. IcFuzz first performs an LLM-based semantic stage segmentation, decomposing simulation programs into structured stages that capture context-aware object semantics. Guided by this information, IcFuzz designs multi-level mutation operators to systematically exercise the simulator across hierarchical granularities. To efficiently navigate the vast simulation state space, IcFuzz employs a multi-armed bandit algorithm to adaptively schedule mutation operators. Experimental results show that IcFuzz outperforms the baselines in terms of both code coverage and bug detection. Specifically, IcFuzz achieves approximately 190%–205% of the code coverage of the baselines and detects an average of 3.7 unique crashes over three rounds of 12-hour tests, while no crashes are detected by the baselines. Moreover, IcFuzz has uncovered 11 bugs over approximately four months, 9 of which have been confirmed or fixed by the developers.
@inproceedings{paperbot12384,
title = {IcFuzz: Fuzzing Isaac Sim with Semantic Stage Guidance and Multi-level Mutation},
author = {Zhixiang Chen and Zhuangbin Chen and Ruoxi Jia and Zeqin Liao and Wei Li and Jinyang Liu and Zibin Zheng},
booktitle = {ASE},
year = {2026},
doi = {10.1145/3832783.3837550}
}