# Commonsense Scene Graph-based Target Localization for Object Search

arXiv: https://arxiv.org/abs/2404.00343 · HTML: https://mlsanity.com/paper/2404.00343/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Mar 2024.

## Built on by (most cited first, 4 of 4)

- [Automatic Cognitive Task Generation for In-Situ Evaluation of Embodied Agents](https://mlsanity.com/paper/2602.05249/) (2026)
- [Relational Semantic Reasoning on 3D Scene Graphs for Open World Interactive Object Search](https://mlsanity.com/paper/2603.05642/) (2026)
- [Object Search in Partially-Known Environments via LLM-informed Model-based Planning and Prompt Selection](https://mlsanity.com/paper/2603.23800/) (2026)
- [OpenSGA: Efficient 3D Scene Graph Alignment in the Open World](https://mlsanity.com/paper/2605.10484/) (2026)

## Abstract (arXiv)

Object search is a fundamental skill for household robots, yet the core problem lies in the robot's ability to locate the target object accurately. The dynamic nature of household environments, characterized by the arbitrary placement of daily objects by users, makes it challenging to perform target localization. To efficiently locate the target object, the robot needs to be equipped with knowledge at both the object and room level. However, existing approaches rely solely on one type of knowledge, leading to unsatisfactory object localization performance and, consequently, inefficient object search processes. To address this problem, we propose a commonsense scene graph-based target localization, CSG-TL, to enhance target object search in the household environment. Given the pre-built map with stationary items, the robot models the room-level knowledge with object-level commonsense knowledge generated by a large language model (LLM) to a commonsense scene graph (CSG), supporting both types of knowledge for CSG-TL. To demonstrate the superiority of CSG-TL on target localization, extensive experiments are performed on the real-world ScanNet dataset and the AI2THOR simulator. Moreover, we have extended CSG-TL to an object search framework, CSG-OS, validated in both simulated and real-world environments. Code and videos are available at https://sites.google.com/view/csg-os.