Incrementally Grounding Expressions for Spatial Relations between Objects

Tiago Mota, Mohan Sridharan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

Recognizing, reasoning about, and providing understandable descriptions of spatial relations between objects is an important task for robots interactingwith humans. This paper describes an architecture for incrementally learning and revising the grounding of spatial relations between objects. Answer Set Prolog, a declarative language, is used to represent and reason with incomplete knowledge that includes prepositional spatial relations between scene objects. A generic grounding of prepositions for spatial relations, human input (when available), and non-monotonic logical inference, are used to infer spatial relations between 3D point clouds in given scenes, incrementally acquiring a specialized metric grounding of the prepositions and the relative confidence associated with each grounding. The architecture is evaluated on a benchmark dataset of tabletop images and on complex simulated scenes of furniture.
Original languageEnglish
Title of host publicationProceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (IJCAI-18)
Publication statusPublished - 13 Jul 2018
Event27th International Joint Conference on Artificial Intelligence: IJCAI 2018 - Stockholmsmässan, Stockholm, Sweden
Duration: 13 Jul 201819 Jul 2018


Conference27th International Joint Conference on Artificial Intelligence
Abbreviated titleIJCAI 2018
Internet address


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