Learning Temporal Plans from Observation of Human Collaborative Behavior

The objective of our research effort is to enable robots to engage in complex collaborative tasks with humanrobot interaction. To function as a reliable assistant or teammate, the robot must be able to adapt to the actions of its human partner and respond to temporal variations in its own and its partner’s actions. Dynamic plan execution algorithms provide a fast and robust method of executing collaborative multi-robot tasks in the presence of temporal uncertainty. However, current state of the art algorithms, rely on hand-crafted plans, providing no means of generating plans for new tasks. In this paper, we outline our approach for learning a model of collaborative robot behavior by observing humanhuman interaction of the target task. Through statistical analysis of the recorded human behavior we extract patterns of common behavior, and use the resulting model to learn a temporal plan. The result is a learning framework that automatically produces temporal plans for use with dynamic planning that model human collaborative behavior and produce human-like behavior in the robot. In this paper, we present our current progress in the development of this learning framework.