While recent work in scene reconstruction and understanding has made strides in grounding natural language to physical 3D environments, it is still challenging to ground abstract, high-level instructions to a 3D scene. High-level instructions might not explicitly invoke semantic elements in the scene, and even the process of breaking a high-level task into a set of more concrete subtasks, a process called hierarchical task analysis, is environment-dependent. In this work, we propose ASHiTA, the first framework that generates a task hierarchy grounded to a 3D scene graph by breaking down high-level tasks into grounded subtasks. ASHiTA alternates LLM-assisted hierarchical task analysis, to generate the task breakdown, with task-driven 3D scene graph construction to generate a suitable representation of the environment. Our experiments show that ASHiTA performs significantly better than LLM baselines in breaking down high-level tasks into environment-dependent subtasks and is additionally able to achieve grounding performance comparable to state-of-the-art methods.
View on arXiv@article{chang2025_2504.06553,
title={ ASHiTA: Automatic Scene-grounded HIerarchical Task Analysis },
author={ Yun Chang and Leonor Fermoselle and Duy Ta and Bernadette Bucher and Luca Carlone and Jiuguang Wang },
journal={arXiv preprint arXiv:2504.06553},
year={ 2025 }
}