17 research outputs found
Natural Language Interaction to Facilitate Mental Models of Remote Robots
Increasingly complex and autonomous robots are being deployed in real-world
environments with far-reaching consequences. High-stakes scenarios, such as
emergency response or offshore energy platform and nuclear inspections, require
robot operators to have clear mental models of what the robots can and can't
do. However, operators are often not the original designers of the robots and
thus, they do not necessarily have such clear mental models, especially if they
are novice users. This lack of mental model clarity can slow adoption and can
negatively impact human-machine teaming. We propose that interaction with a
conversational assistant, who acts as a mediator, can help the user with
understanding the functionality of remote robots and increase transparency
through natural language explanations, as well as facilitate the evaluation of
operators' mental models.Comment: In Workshop on Mental Models of Robots at HRI 202
CRWIZ: A Framework for Crowdsourcing Real-Time Wizard-of-Oz Dialogues
Large corpora of task-based and open-domain conversational dialogues are
hugely valuable in the field of data-driven dialogue systems. Crowdsourcing
platforms, such as Amazon Mechanical Turk, have been an effective method for
collecting such large amounts of data. However, difficulties arise when
task-based dialogues require expert domain knowledge or rapid access to
domain-relevant information, such as databases for tourism. This will become
even more prevalent as dialogue systems become increasingly ambitious,
expanding into tasks with high levels of complexity that require collaboration
and forward planning, such as in our domain of emergency response. In this
paper, we propose CRWIZ: a framework for collecting real-time Wizard of Oz
dialogues through crowdsourcing for collaborative, complex tasks. This
framework uses semi-guided dialogue to avoid interactions that breach
procedures and processes only known to experts, while enabling the capture of a
wide variety of interactions. The framework is available at
https://github.com/JChiyah/crwizComment: 10 pages, 5 figures. To Appear in LREC 202