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Guiding Exploratory Behaviors for Multi-Modal Grounding of Linguistic Descriptions.
Jesse
Thomason, Jivko Sinapov, Raymond
J. Mooney, and Peter Stone.
In Proceedings of the 32nd Conference
on Artificial Intelligence (AAAI), February 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.
@InProceedings{AAAI18-jesse,
author = {Jesse Thomason and Jivko Sinapov and Raymond J. Mooney and Peter Stone},
title = {Guiding Exploratory Behaviors for Multi-Modal Grounding of Linguistic Descriptions},
booktitle = {Proceedings of the 32nd Conference on Artificial Intelligence (AAAI)},
location = {New Orleans, LA},
month = {February},
year = {2018},
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.
},
}
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