Guiding Exploratory Behaviors for Multi-Modal Grounding of Linguistic Descriptions (2018)
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.
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To Appear In Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18) , February 2018.
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Raymond J. Mooney Faculty mooney [at] cs utexas edu
Jivko Sinapov Postdoctoral Alumni jsinapov [at] cs utexas edu
Peter Stone Faculty pstone [at] cs utexas edu
Jesse Thomason Ph.D. Student jesse [at] cs utexas edu