Learning to Interpret Natural Language Commands through Human-Robot Dialog (2015)
Intelligent robots frequently need to understand requests from naive users through natural language. Previous approaches either cannot account for language variation, e.g., keyword search, or require gathering large annotated corpora, which can be expensive and cannot adapt to new variation. We introduce a dialog agent for mobile robots that understands human instructions through semantic parsing, actively resolves ambiguities using a dialog manager, and incrementally learns from human-robot conversations by inducing training data from user paraphrases. Our dialog agent is implemented and tested both on a web interface with hundreds of users via Mechanical Turk and on a mobile robot over several days, tasked with understanding navigation and delivery requests through natural language in an office environment. In both contexts, We observe significant improvements in user satisfaction after learning from conversations.
In Proceedings of the 2015 International Joint Conference on Artificial Intelligence (IJCAI), pp. 1923--1929, Buenos Aires, Argentina, July 2015.

Slides (PDF)
Raymond J. Mooney Faculty mooney [at] cs utexas edu
Peter Stone Faculty pstone [at] cs utexas edu
Jesse Thomason Ph.D. Student jesse [at] cs utexas edu
Shiqi Zhang Postdoctoral Alumni szhang [at] cs utexas edu