Robust spoken instruction understanding for HRI

Authors:
Rehj Cantrell;Matthias Scheutz;Paul Schermerhorn;Xuan Wu
Affiliations:
Indiana University, Bloomington, IN, USA;Indiana University, Bloomington, IN, USA;Indiana University, Bloomington, IN, USA;Indiana University, Bloomington, IN, USA
Venue:
Proceedings of the 5th ACM/IEEE international conference on Human-robot interaction
Year:
2010

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Abstract

Natural human-robot interaction requires different and more robust models of language understanding (NLU) than non-embodied NLU systems. In particular, architectures are required that (1) process language incrementally in order to be able to provide early backchannel feedback to human speakers; (2) use pragmatic contexts throughout the understanding process to infer missing information; and (3) handle the underspecified, fragmentary, or otherwise ungrammatical utterances that are common in spontaneous speech. In this paper, we describe our attempts at developing an integrated natural language understanding architecture for HRI, and demonstrate its novel capabilities using challenging data collected in human-human interaction experiments.