ProACT：面向 VR 消防员训练的性能驱动难度自适应系统
Too Hard, Too Easy, Just Right: Performance-Driven Challenge Adaptation in Virtual Reality Firefighter Training
AI 导读
研究者提出 ProACT，一个针对 VR 消防员训练的性能驱动自适应系统，通过学员表现与模拟火势的闭环交互调节场景难度。该系统包含全局模块（实时根据表现调整火势蔓延动态）和局部模块（周期性更新环境中物体的可燃性）。评估显示，相比自适应与非自适应基线，ProACT 在客观训练表现和主观用户体验上均有提升。
正文 · 原文
Abstract:Effective firefighter training requires practice under dynamic and stressful emergency conditions, yet real-world live-fire exercises are costly, risky, and difficult to scale. Virtual Reality (VR) offers a safe and controllable alternative, but many existing VR firefighter training systems rely on fixed scenarios or provide limited difficulty adaptation, making it difficult to maintain an appropriate level of challenge for skill development as trainees' performance changes over time. To address this limitation, we present ProACT, a performance-driven adaptive training system that regulates scenario difficulty through closed-loop interactions between trainee performance and simulated fire dynamics. Specifically, ProACT combines a global module that continuously adjusts scene-level fire spread dynamics in real time based on trainee performance with a local module that periodically updates object-level flammability across the environment. Our evaluation shows that ProACT improves both objective training performance and subjective user experience compared with existing adaptive and non-adaptive baselines. We further discuss how adaptive immersive environments can extend beyond firefighting to broader training domains.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.38117 [cs.AI] |
| (or arXiv:2610.38117v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.38117 arXiv-issued DOI via DataCite |
Submission history
From: Xuan Wang [view email]
[v1]
Wed, 28 Oct 2026 00:52:28 UTC (7,060 KB)
来源：arXiv cs.AI · arxiv.org