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Multi-modal Predicate Identification using Dynamically Learned Robot Controllers.
Saeid Amiri, Suhua Wei, Shiqi
Zhang, Jivko Sinapov, Jesse Thomason,
and Peter Stone.
In Proceedings of the 27th International Joint Conference
on Artificial Intelligence (IJCAI-18), July 2018.
Intelligent robots frequently need to explore the objects in their working environments. Modern sensors have enabled robots to learn object properties via perception of multiple modalities. However, object exploration in the real world poses a challenging trade-off between information gains and exploration action costs. Mixed observability Markov decision process (MOMDP) is a framework for planning under uncertainty, while accounting for both fully and partially observable components of the state. Robot perception frequently has to face such mixed observability. This work enables a robot equipped with an arm to dynamically construct query-oriented MOMDPs for multi-modal predicate identification (MPI) of objects. The robot's behavioral policy is learned from two datasets collected using real robots. Our approach enables a robot to explore object properties in a way that is significantly faster while improving accuracies in comparison to existing methods that rely on hand-coded exploration strategies.
@InProceedings{IJCAI18-saeid,
author = {Saeid Amiri and Suhua Wei and Shiqi Zhang and Jivko Sinapov and Jesse Thomason and Peter Stone},
title = {Multi-modal Predicate Identification using Dynamically Learned Robot Controllers},
booktitle = {Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI-18)},
location = {Stockholm, Sweden},
month = {July},
year = {2018},
abstract = {
Intelligent robots frequently need to explore the objects in their working
environments. Modern sensors have enabled robots to learn object properties
via perception of multiple modalities. However, object exploration in the
real world poses a challenging trade-off between information gains and
exploration action costs. Mixed observability Markov decision process (MOMDP)
is a framework for planning under uncertainty, while accounting for both
fully and partially observable components of the state. Robot perception
frequently has to face such mixed observability. This work enables a robot
equipped with an arm to dynamically construct query-oriented MOMDPs for
multi-modal predicate identification (MPI) of objects. The robot's behavioral
policy is learned from two datasets collected using real robots. Our approach
enables a robot to explore object properties in a way that is significantly
faster while improving accuracies in comparison to existing methods that rely
on hand-coded exploration strategies.
},
}
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