生成的敵対ネットワークを用いた非構造デモンストレーションからのマルチモーダル模倣学習
Multi-Modal Imitation Learning from Unstructured Demonstrations using Generative Adversarial Nets
ラベルなし・非構造のデモンストレーションからスキル分割と模倣を同時に学習し、単一のマルチモーダル方策で複数タスクを模倣できるフレームワークを提案した。
著者: Karol Hausman, Yevgen Chebotar, Stefan Schaal, Gaurav Sukhatme, Joseph Lim
分類: cs.RO, cs.LG
原文アブストラクト
Imitation learning has traditionally been applied to learn a single task from demonstrations thereof. The requirement of structured and isolated demonstrations limits the scalability of imitation learning approaches as they are difficult to apply to real-world scenarios, where robots have to be able to execute a multitude of tasks. In this paper, we propose a multi-modal imitation learning framework that is able to segment and imitate skills from unlabelled and unstructured demonstrations by learning skill segmentation and imitation learning jointly. The extensive simulation results indicate that our method can efficiently separate the demonstrations into individual skills and learn to imitate them using a single multi-modal policy. The video of our experiments is available at http://sites.google.com/view/nips17intentiongan
