Learning Human Types from Demonstration

Research on POMDP formulations for collaborative tasks in game AI applications (Nguyen et al. 2011; Macindoe, The development of new industrial robotic systems that operate Kaelbling, and Lozano-Pérez 2012; Silver and Veness in the same physical space as people highlights the 2010) also assumed a known human model. Additionally, emerging need for robots that can integrate seamlessly into previous partially observable formalisms (Ong et al. 2010; human group dynamics by adapting to the personalized style Bandyopadhyay et al. 2013; Broz, Nourbakhsh, and Simmons of human teammates. This adaptation requires learning a statistical 2011; Fern and Tadepalli 2010; Nguyen et al. 2011; model of human behavior and integrating this model Macindoe, Kaelbling, and Lozano-Pérez 2012) in assistive into the decision-making algorithm of the robot in a principled or collaborative tasks represented the preference or intention way. We present a framework for automatically learning of the human for their own actions, rather than those of human user models from joint-action demonstrations the robot, as the partially observable variable.