IndustryEQA: Pushing the Frontiers of Embodied Question Answering in Industrial Scenarios

Abstract

Existing Embodied Question Answering (EQA) benchmarks primarily focus on household environments, often overlooking safety-critical aspects and reasoning processes pertinent to industrial settings. This drawback limits the evaluation of agent readiness for real-world industrial applications. To bridge this, we introduce IndustryEQA, the first benchmark dedicated to evaluating embodied agent capabilities within safety-critical industrial warehouse scenarios. Built upon the NVIDIA Isaac Sim platform, IndustryEQA provides high-fidelity episodic memory videos featuring diverse industrial assets, dynamic human agents, and carefully designed hazardous situations inspired by real-world safety guidelines. The benchmark includes rich annotations covering six categories: equipment safety, human safety, object recognition, attribute recognition, temporal understanding, and spatial understanding. Besides, it also provides extra reasoning evaluation based on these categories. Specifically, it comprises 971 question-answer pairs generated from small warehouse scenarios and 373 pairs from large ones, incorporating scenarios with and without human. We further propose a comprehensive evaluation framework, including various baseline models, to assess their general perception and reasoning abilities in industrial environments. IndustryEQA aims to steer EQA research towards developing more robust, safety-aware, and practically applicable embodied agents for complex industrial environments.

Cite

Text

Li et al. "IndustryEQA: Pushing the Frontiers of Embodied Question Answering in Industrial Scenarios." Advances in Neural Information Processing Systems, 2025.

Markdown

[Li et al. "IndustryEQA: Pushing the Frontiers of Embodied Question Answering in Industrial Scenarios." Advances in Neural Information Processing Systems, 2025.](https://mlanthology.org/neurips/2025/li2025neurips-industryeqa/)

BibTeX

@inproceedings{li2025neurips-industryeqa,
  title     = {{IndustryEQA: Pushing the Frontiers of Embodied Question Answering in Industrial Scenarios}},
  author    = {Li, Yifan and Chen, Yuhang and Dao, Anh and Li, Lichi and Cai, Zhongyi and Tan, Zhen and Chen, Tianlong and Kong, Yu},
  booktitle = {Advances in Neural Information Processing Systems},
  year      = {2025},
  url       = {https://mlanthology.org/neurips/2025/li2025neurips-industryeqa/}
}