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Miscommunication Detection and Recovery in Situated Human–Robot Dialogue

Published:17 February 2019Publication History
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Abstract

Even without speech recognition errors, robots may face difficulties interpreting natural-language instructions. We present a method for robustly handling miscommunication between people and robots in task-oriented spoken dialogue. This capability is implemented in TeamTalk, a conversational interface to robots that supports detection and recovery from the situated grounding problems of referential ambiguity and impossible actions. We introduce a representation that detects these problems and a nearest-neighbor learning algorithm that selects recovery strategies for a virtual robot. When the robot encounters a grounding problem, it looks back on its interaction history to consider how it resolved similar situations. The learning method is trained initially on crowdsourced data but is then supplemented by interactions from a longitudinal user study in which six participants performed navigation tasks with the robot. We compare results collected using a general model to user-specific models and find that user-specific models perform best on measures of dialogue efficiency, while the general model yields the highest agreement with human judges. Our overall contribution is a novel approach to detecting and recovering from miscommunication in dialogue by including situated context, namely, information from a robot’s path planner and surroundings.

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  1. Miscommunication Detection and Recovery in Situated Human–Robot Dialogue

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        cover image ACM Transactions on Interactive Intelligent Systems
        ACM Transactions on Interactive Intelligent Systems  Volume 9, Issue 1
        March 2019
        168 pages
        ISSN:2160-6455
        EISSN:2160-6463
        DOI:10.1145/3312745
        Issue’s Table of Contents

        Copyright © 2019 ACM

        Publication rights licensed to ACM. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of the United States government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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        New York, NY, United States

        Publication History

        • Published: 17 February 2019
        • Accepted: 1 October 2018
        • Revised: 1 August 2018
        • Received: 1 April 2018
        Published in tiis Volume 9, Issue 1

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