Priming transformational planning with observations of human activities

People perform daily activities in many different ways. When setting a table, they might use a tray, stack plates, stack cups on plates, leave the doors of a cupboard open when taking several items out of it. Similarly flexible behavior is desired when mobile robots perform household tasks. Moreover, they should perform actions in a way that they are accepted by the people, for example by showing human-like behavior. In this paper we propose to extend a transformational planning system with models characterizing the behavior produced by the different plans in the plan library. These models are used by the robot to select a plan that resembles human behavior. In addition to acting more human-like, this helps the robot choose good plans for a task by imitating humans instead of performing exhaustive search. We show the feasibility of this approach using a household robot application as an example and present empirical results on the classification accuracy in this domain.