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 that conversational data. Our experiments show that improving both parsing and perception components from conversations improves communication quality and human ratings of the agent.

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In Late-breaking Track at the SIGDIAL Special Session on Physically Situated Dialogue (RoboDIAL-18), Melbourne, Australia, July 2018.

@inproceedings{thomason:robodial18, title={Jointly Improving Parsing and Perception for Natural Language Commands through Human-Robot Dialog}, author={Jesse Thomason and Aishwarya Padmakumar and Jivko Sinapov and Nick Walker and Yuqian Jiang and Harel Yedidsion and Justin Hart and Peter Stone and Raymond J. Mooney}, booktitle={Late-breaking Track at the SIGDIAL Special Session on Physically Situated Dialogue (RoboDIAL-18)}, month={July}, address={Melbourne, Australia}, url="http://www.cs.utexas.edu/users/ai-labpub-view.php?PubID=127720", year={2018} }