Unsupervised Temporal Segmentation Using Models That Discriminate Between Demonstrations and Unintentional Actions

Abstract

Segmentation of a compound task with multiple subtasks is crucial for imitation learning. Conventional unsupervised segmentation methods focused on only reproducibility of demonstrations and did not use the property that goal-directed actions rarely occur without intention. In this paper, we propose a novel method to segment demonstrations into goal-directed actions by self-supervised learning. We use the discriminator between demonstrations and self-generated unintentional actions performed by the same body in behavioral cloning paradigm because goal-directed actions rarely occur without intention, and thus can be separated from unintentional actions. And we consider the states that cannot be reached by unintentional actions as subtask changepoints. We evaluated our method on manipulation tasks with multiple subtasks. The results indicate that our method can detect subtask changepoints more accurately than an existing unsupervised segmentation method.