The development of hand-crafted models for action and dialog generation for a social robot is a time consuming process that yields solutions for only a relatively narrow range of interactions envisioned by the programmers. Further, it is important that such models be compatible with human behavior.

We are exploring a data-driven approach for interactive behavior generation that leverages on-line games as a means for collecting large-scale data corpora for human-robot interaction research. We are developing methods, building on the work of Orkin and Roy’s Restaurant Game, to learn context-relevant action and dialog models from this crowdsourced behavior to support human-robot collaboration.

We have developed a 2-player on-line game called MARS ESCAPE to support this research. In the game, you can take the role of an astronaut or a robot on Mars who must complete their mission before oxygen supplies run out.  Data collected from the game will be used to generate autonomous behaviors for the MDS robot Nexi and to study human-robot interaction by recreating the game environment in real life at the Boston Museum of Science in 2011.

These learned models of typical and atypical human behavior are being applied to enable a virtual robot to collaborate with a new human player to accomplish collaborative tasks in the context of the MARS ESCAPE game. We are also investigating how such models can be applied as a kind of “episodic memory” to enable a physical robot to perform a similar task with people in the real world.

This project is in collaboration with Prof. Sonia Chernova at WPI.

This project is supported by grants from Microsoft and the Office of Naval Research.