Crowdsourcing human-robot interaction: Application from virtual to physical worlds

Abstract

The ability for robots to engage in interactive behavior with a broad range of people is critical for future development of social robotic applications. In this paper, we propose the use of online games as a means of generating large-scale data corpora for human-robot interaction research in order to create robust and diverse interaction models. We describe a data collection approach based on a multiplayer game that was used to collect movement, action and dialog data from hundreds of online users. We then study how these records of human-human interaction collected in a virtual world can be used to generate contextually correct social and task-oriented behaviors for a robot collaborating with a human in a similar real-world environment. We evaluate the resulting behavior model using a physical robot in the Boston Museum of Science, and show that the robot successfully performs the collaborative task and that its behavior is strongly influenced by patterns in the crowdsourced dataset.

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