@InProceedings{alomari-EtAl:2017:RoboNLP,
  author    = {Alomari, Muhannad  and  Duckworth, Paul  and  Hawasly, Majd  and  Hogg, David C.  and  Cohn, Anthony G.},
  title     = {Natural Language Grounding and Grammar Induction for Robotic Manipulation Commands},
  booktitle = {Proceedings of the First Workshop on Language Grounding for Robotics},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {35--43},
  abstract  = {We present a cognitively plausible system capable of acquiring knowledge in
	language and vision from pairs of short video clips and linguistic
	descriptions. The aim of this work is to teach a robot manipulator how to
	execute natural language commands by demonstration. This is achieved by first
	learning a set of visual `concepts' that abstract the visual feature spaces
	into concepts that have human-level meaning. Second, learning the
	mapping/grounding between words and the extracted visual concepts. Third,
	inducing grammar rules via a semantic representation known as Robot Control
	Language (RCL).
	We evaluate our approach against state-of-the-art supervised and unsupervised
	grounding and grammar induction systems, and show that a robot can learn to
	execute never seen-before commands from pairs of unlabelled linguistic and
	visual inputs.},
  url       = {http://www.aclweb.org/anthology/W17-2805}
}