Crowd-Sourcing Real-World Human-Robot Dialogue and Teamwork through Online Multiplayer Games
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Breazeal_Crowdsourcing real-world.pdf
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245.94 KB
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Checksum (MD5)
3904a1a5393072765c75b8016c85ac0c
Author(s)
Chernova, Sonia
DePalma, Nicholas Brian
Breazeal, Cynthia Lynn
Date Issued
2011
Journal
AI Magazine
Publisher
Association for the Advancement of Artificial Intelligence
Citation
Chernova, Sonia, Nick DePalma and Cynthia Breazeal. "Crowd-Sourcing Real-World Human-Robot Dialogue and Teamwork through Online Multiplayer Games." AI Magazine 32.4 (2011): 100-111. © 2011 Association for the Advancement of Artificial Intelligence
Version
Final published version
Abstract
We present an innovative approach for large-scale data collection in human-robot interaction research through the use of online multi-player games. By casting a robotic task as a collaborative game, we gather thousands of examples of human-human interactions online, and then leverage this corpus of action and dialog data to create contextually relevant, social and task-oriented behaviors for human-robot interaction in the real world. We demonstrate our work in a collaborative search and retrieval task requiring dialog, action synchronization and action sequencing between the human and robot partners. A user study performed at the Boston Museum of Science shows that the autonomous robot exhibits many of the same patterns of behavior that were observed in the online dataset and survey results rate the robot similarly to human partners in several critical measures.
MIT Department
Program in Media Arts and Sciences (Massachusetts Institute of Technology)
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
http://www.aaai.org/ojs/index.php/aimagazine/article/view/2380/2245
