Robotic task planning in real-world environments requires not only object recognition but also a nuanced understanding of spatial relationships between objects. We present a spatial-relationship-aware dataset of nearly 1,000 robot-acquired indoor images, annotated with object attributes, positions, and detailed spatial relationships. Captured using a Boston Dynamics Spot robot and labelled with a custom annotation tool, the dataset reflects complex scenarios with similar or identical objects and intricate spatial arrangements. We benchmark six state-of-the-art scene-graph generation models on this dataset, analysing their inference speed and relational accuracy. Our results highlight significant differences in model performance and demonstrate that integrating explicit spatial relationships into foundation models, such as ChatGPT 4o, substantially improves their ability to generate executable, spatially-aware plans for robotics. The dataset and annotation tool are publicly available atthis https URL, supporting further research in spatial reasoning for robotics.
View on arXiv@article{wang2025_2506.12525,
title={ A Spatial Relationship Aware Dataset for Robotics },
author={ Peng Wang and Minh Huy Pham and Zhihao Guo and Wei Zhou },
journal={arXiv preprint arXiv:2506.12525},
year={ 2025 }
}