Jointly Improving Parsing and Perception for Natural Language Commands through Human-Robot Dialog

Abstract

Natural language understanding in robots needs to be robust to a wide-range of both human speakers and human environments. Rather than force humans to use language that robots can understand, robots in human environments should dynamically adapt—continuously learning new language constructions and perceptual concepts as they are used in context. In this work, we present methods for parsing natural language to underlying meanings, and using robotic sensors to create multi-modal models of perceptual concepts. We combine these steps towards language understanding into a holistic agent for jointly improving parsing and perception on a robotic platform through human-robot dialog. We train and evaluate this agent on Amazon Mechanical Turk, then demonstrate it on a robotic platform initialized from conversational data gathered from Mechanical Turk. Our experiments show that improving both parsing and perception components from conversations improves communication quality and human ratings of the agent.

BibTeX Entry

@inproceedings{thomason2018mrhrc,
  title = {Jointly Improving Parsing and Perception for Natural Language Commands through Human-Robot Dialog},
  author = {Thomason, Jesse and Padmakumar, Aishwarya and Sinapov, Jivko and Walker, Nick and Jiang, Yuqian and Yedidsion, Harel and Hart, Justin and Stone, Peter and Mooney, Raymond J.},
  booktitle = {Proceedings of the RSS Workshop on Models and Representations for Natural Human-Robot Communication (MRHRC-18)},
  publisher = {Robotics: Science and Systems (RSS)},
  month = jun,
  year = {2018},
  wwwtype = {workshop},
  wwwhidden = {true},
  wwwpdf = {http://www.cs.utexas.edu/%7Epstone/Papers/bib2html-links/MRHRC18-thomason.pdf},
  wwwposter = {https://jessethomason.com/publication_supplements/RSS18_MRHRC_poster.pdf}
}