Balancing Efficiency and Coverage in Human-Robot Dialogue Collection
This incremental work addresses data collection efficiency for human-robot interaction in collaborative search and navigation tasks.
The researchers tackled the problem of collecting human-robot dialogue data efficiently by developing a multi-phased Wizard-of-Oz approach with a GUI interface, resulting in faster dialogue pace while maintaining high response coverage and enabling more efficient data collection.
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.