Efficiently Finding (Nearly) Minimal Fst Of Repetitive Unsegmented Demonstration Data

This paper presents an algorithm that enables a robot to learn from demonstration by inferring a nearly minimal plan instead of the more common policy, The algorithm uses only the demon- strated actions to build the plan, without relying on observation of the world states during the demonstration. By making assumptions about the format of the data. it can generate this plan in O(n(5)).