Peter Stone's Selected Publications

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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. Multi-modal Predicate Identification using Dynamically Learned Robot Controllers. In Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI-18), July 2018.

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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.

BibTeX Entry

@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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