Predicting the Robot Learning Curve based on Properties of Human Interaction

Sekou Remy, Ayanna M. Howard

In this work we present research on three topics which have implications for future robotic applications. Couched in learning from human provided examples, we study how robots can demonstrate learning curves akin to those observed for human students. Specifically we show how the parameters of robot learning curves relate to those parameters from learning curves generated by human students. Next we show how these parameters and learning process they represent are affected by the quality of instruction provided. Finally, we present a method to generate an estimate of the robot learning curve. This method is of merit since it is based on properties of interaction that can be extracted as learning occurs.


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