Robust spoken instruction understanding for HRI

—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. Keywords-natural human-robot interaction; natural language processing; dialogue interactions; integrated architecture

• Type: Conference
• Year: 2010
• Where: HRI
• Authors: Rehj Cantrell, Matthias Scheutz, Paul W. Schermerhorn, Xuan Wu
• Keywords: HRI 2010, Human Robot Interaction, Human-robot Interaction, Non-embodied Nlu Systems, —Natural Human-robot Interaction