Learning Human Types from Demonstration

Stefanos Nikolaidis, Keren Gu, Ramya Ramakrishnan, Julie A. Shah

Abstract: The development of new industrial robotic systems that operate in the same physical space as people highlights the emerging need for robots that can integrate seamlessly into human group dynamics by adapting to the personalized style of human teammates. This adaptation requires learning a statistical model of human behavior and integrating this model into the decision-making algorithm of the robot in a principled way. We present a framework for automatically learning human user models from joint-action demonstrations that enables the robot to compute a robust policy for a collaborative task with a human, assuming access to demonstrations of human teams working on the task. The robustness of the action selection mechanism of the robot is compared to previous model-learning algorithms in the ability to function despite increasing deviations of human actions from previously demonstrated behavior.