A Demonstration of Interactive Task Learning

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1 Demonstration Details

We will demonstrate our tabletop robot, which consists of a small robot arm and a Kinect sensor (see Figure 1). We will give live demonstrations of Rosie learning and solving simple puzzles, such as Tower of Hanoi and the Frog and Toad puzzle. We will also show Rosie learning simple games such as Tic-Tac-Toe, that can then be played with spectators.

It would not be feasible for us to demonstrate task learning for a real mobile robot, but we can teach the same kinds of tasks with a simulated robot and environment. We will show live demonstrations of teaching the agent tasks involving navigation, manipulation, and communication, such as delivering an object to a person, fetching an object, telling a person a message, and guiding a person to a desired location.

2 RelatedWork

Although there has been a variety of research on task learning, they often depend on learning from many examples or on off-line processing. For example, Kaiser [Kaiser, 2012] and Barbu et al. [Barbu et al., 2010] describe systems that extract the relevant goal-state predicates for games, by viewing many iterations of game play. Most learning from demonstration systems [Argall et al., 2009][Chao et al., 2011][Nicolescu and Mataric, 2003] for robotic tasks teach action sequences and not descriptions of goals or conditions. Closely related to our work is the work of Hinrich et al. [Hinrichs and Forbus, 2014], where a computer agent learns to play Tic-Tac-Toe from interactions and gestures with a human instructor.

Acknowledgments

The work described here was supported by the National Science Foundation under Grant Number 1419590 and the Office of Naval Research under Grant Number N00014-08-1-0099. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressly or implied, of the NSF, ONR, or the U.S. Government.

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