Abstract
Recognizing, reasoning about, and providing understandable descriptions of spatial relations between objects is an important task for robots interacting
with 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.
with 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 language | English |
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| Title of host publication | Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (IJCAI-18) |
| Publication status | Published - 13 Jul 2018 |