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تسجيل مستخدم جديدBalancing Efficiency and Coverage in Human-Robot Dialogue Collection
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We describe a multi-phased Wizard-of-Oz approach to collecting human-robot dialogue in a collaborative search and navigation task. The data is being used to train an initial automated robot dialogue system to support collaborative exploration tasks. In the first phase, a wizard freely typed robot utterances to human participants. For the second phase, this data was used to design a GUI that includes buttons for the most common communications, and templates for communications with varying parameters. Comparison of the data gathered in these phases show that the GUI enabled a faster pace of dialogue while still maintaining high coverage of suitable responses, enabling more efficient targeted data collection, and improvements in natural language understanding using GUI-collected data. As a promising first step towards interactive learning, this work shows that our approach enables the collection of useful training data for navigation-based HRI tasks.
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This record contains the proceedings of the 2020 Workshop on Assessing, Explaining, and Conveying Robot Proficiency for Human-Robot Teaming, which was held in conjunction with the 2020 ACM/IEEE International Conference on Human-Robot Interaction (HRI
). This workshop was originally scheduled to occur in Cambridge, UK on March 23, but was moved to a set of online talks due to the COVID-19 pandemic.
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