A spatial relationship aware dataset for robotics

Wang, Peng, Pham, Minh Huy, Guo, Zhihao and Zhou, Wei 2025. A spatial relationship aware dataset for robotics. Presented at: MM '25: The 33rd ACM International Conference on Multimedia, Dublin, Ireland, 27-31 October 2025. MM '25: Proceedings of the 33rd ACM International Conference on Multimedia. ACM, pp. 13332-13338. 10.1145/3746027.3758293

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Abstract

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 at https://github.com/PengPaulWang/SpatialAwareRobotDataset, supporting further research in spatial reasoning for robotics.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Published Online
Status:	Published
Schools:	Schools > Computer Science & Informatics
Publisher:	ACM
ISBN:	9798400720352
Date of First Compliant Deposit:	18 November 2025
Last Modified:	18 Nov 2025 10:30
URI:	https://orca.cardiff.ac.uk/id/eprint/182482

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