ArXiv

IcFuzz: Fuzzing Isaac Sim with Semantic Stage Guidance and Multi-level Mutation

2026-08-06 · 14:29 UTC ·Zhixiang Chen, Zhuangbin Chen, Ruoxi Jia... ·1 min read
Authors
Zhixiang Chen, Zhuangbin Chen, Ruoxi Jia...
Categories
cs.RO, cs.SE
arXiv
https://arxiv.org/abs/2608.06088v1
PDF
https://arxiv.org/pdf/2608.06088v1

Brief

IcFuzz targets reliability gaps in NVIDIA Isaac Sim by decomposing simulation programs with LLM-based semantic stage segmentation, applying multi-level mutation operators informed by those stages, and using a multi-armed bandit to steer mutations. The tool substantially improves testing effectiveness—190%–205% of baseline coverage, an average of 3.7 unique crashes in 3×12h runs—and revealed 11 real bugs (9 confirmed/fixed).

Why it matters

IcFuzz is the first fuzzing approach for NVIDIA Isaac Sim that uses LLM-based semantic stage segmentation to extract context-aware object semantics, multi-level mutation operators across hierarchical granularities, and a multi-armed bandit to adaptively schedule mutations.

Key details

  • In experiments IcFuzz achieved approximately 190%–205% of the code coverage of baselines and detected an average of 3.7 unique crashes over three rounds of 12-hour tests (baselines found zero crashes).
  • Over roughly four months IcFuzz uncovered 11 bugs in Isaac Sim, 9 of which developers have confirmed or fixed; the work is accepted to ASE 2026 (Chen et al., ArXiv:2608.06088v1).
Source evidence

Abstract

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.

Comment: Accepted at the 41st IEEE/ACM International Conference on Automated Software Engineering (ASE 2026)