Active learning for generating motion and utterances in object manipulation dialogue tasks

Komei Sugiura, Naoto Iwahashi, Hisashi Kawai, Satoshi Nakamura

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In an object manipulation dialogue, a robot may misunderstand an ambiguous command from a user, such as "Place the cup down (on the table)," potentially resulting in an accident. Although making confirmation questions before all motion execution will decrease the risk of this failure, the user will find it more convenient if confirmation questions are not made under trivial situations. This paper proposes a method for estimating ambiguity in commands by introducing an active learning framework with Bayesian logistic regression to human-robot spoken dialogue. We conducted physical experiments in which a user and a manipulator-based robot communicated using spoken language to manipulate objects.

Original languageEnglish
Title of host publicationDialog with Robots - Papers from the AAAI Fall Symposium, Technical Report
Pages115-120
Number of pages6
Publication statusPublished - 2010 Dec 1
Externally publishedYes
Event2010 AAAI Fall Symposium - Arlington, VA, United States
Duration: 2010 Nov 112010 Nov 13

Publication series

NameAAAI Fall Symposium - Technical Report
VolumeFS-10-05

Conference

Conference2010 AAAI Fall Symposium
CountryUnited States
CityArlington, VA
Period10/11/1110/11/13

ASJC Scopus subject areas

  • Engineering(all)

Fingerprint Dive into the research topics of 'Active learning for generating motion and utterances in object manipulation dialogue tasks'. Together they form a unique fingerprint.

  • Cite this

    Sugiura, K., Iwahashi, N., Kawai, H., & Nakamura, S. (2010). Active learning for generating motion and utterances in object manipulation dialogue tasks. In Dialog with Robots - Papers from the AAAI Fall Symposium, Technical Report (pp. 115-120). (AAAI Fall Symposium - Technical Report; Vol. FS-10-05).