Learning spatial-semantic representations from natural language descriptions and scene classifications

@article{Hemachandra2014LearningSR,
  title={Learning spatial-semantic representations from natural language descriptions and scene classifications},
  author={Sachithra Hemachandra and Matthew R. Walter and Stefanie Tellex and Seth J. Teller},
  journal={2014 IEEE International Conference on Robotics and Automation (ICRA)},
  year={2014},
  pages={2623-2630}
}
We describe a semantic mapping algorithm that learns human-centric environment models by interpreting natural language utterances. Underlying the approach is a coupled metric, topological, and semantic representation of the environment that enables the method to fuse information from natural language descriptions with low-level metric and appearance data. We extend earlier work with a novel formulation that incorporates spatial layout into a topological representation of the environment. We… CONTINUE READING
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