Planning with activity schemata: Closing the loop in experience-based planning

Learning task knowledge from robot activity experiences has been recognized as an effective approach to improve robot task planning performance. Cognitive capabilities are required to enable a robot to learn new activities from its human partners as well as to refine and improve already learned skills. This paper presents an approach for a robot to conceptualize plan-based robot activity experiences as activity schemata - enriched abstract task knowledge - as well as to exploit them to make plans in similar situations. The experiences are episodic descriptions of plan-based robot activities including environment perceptions, sequences of applied actions and achieved tasks. In this work, the robot activity experiences are obtained through human-robot interaction. The adopted conceptualization approach constructs an activity schema through deductive generalization, abstraction and feature extraction. A high-level task planner was developed to find a solution for a similar task by following an activity schema. The paper proposes a formalization for experience-based planning domains. The proposed learning and planning approach is illustrated in a restaurant environment where a service robot learns how to carry out complex tasks.