# Balancing Efficiency and Coverage in Human-Robot Dialogue Collection

arXiv: https://arxiv.org/abs/1810.02017 · HTML: https://mlsanity.com/paper/1810.02017/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Oct 2018.

## Built on by (most cited first, 1 of 1)

- [SCOUT: A Situated and Multi-Modal Human-Robot Dialogue Corpus](https://mlsanity.com/paper/2411.12844/) (2024)

## Abstract (arXiv)

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.