AAAI Publications, Thirty-Second AAAI Conference on Artificial Intelligence

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Guiding Exploratory Behaviors for Multi-Modal Grounding of Linguistic Descriptions
Jesse Thomason, Jivko Sinapov, Raymond J. Mooney, Peter Stone

Last modified: 2018-04-27

Abstract


A major goal of grounded language learning research is to enable robots to connect language predicates to a robot's physical interactive perception of the world. Coupling object exploratory behaviors such as grasping, lifting, and looking with multiple sensory modalities (e.g., audio, haptics, and vision) enables a robot to ground non-visual words like ``heavy'' as well as visual words like ``red''. A major limitation of existing approaches to multi-modal language grounding is that a robot has to exhaustively explore training objects with a variety of actions when learning a new such language predicate. This paper proposes a method for guiding a robot's behavioral exploration policy when learning a novel predicate based on known grounded predicates and the novel predicate's linguistic relationship to them. We demonstrate our approach on two datasets in which a robot explored large sets of objects and was tasked with learning to recognize whether novel words applied to those objects.


Keywords


Multi-modal grounding; NLP; Human-robot interaction

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