人間の遊びを模倣してロボットが遊びを学習する
Learning to Play by Imitating Humans
少量の人間のテレオペ遊びデータから行動クローニングで大量の遊びデータを自律生成し、それを追加学習に使うことで汎用ゴール条件付き方策の性能を向上させた。
著者: Rostam Dinyari, Pierre Sermanet, Corey Lynch
分類: cs.RO, cs.AI, cs.LG, cs.SY, eess.SY
原文アブストラクト
Acquiring multiple skills has commonly involved collecting a large number of expert demonstrations per task or engineering custom reward functions. Recently it has been shown that it is possible to acquire a diverse set of skills by self-supervising control on top of human teleoperated play data. Play is rich in state space coverage and a policy trained on this data can generalize to specific tasks at test time outperforming policies trained on individual expert task demonstrations. In this work, we explore the question of whether robots can learn to play to autonomously generate play data that can ultimately enhance performance. By training a behavioral cloning policy on a relatively small quantity of human play, we autonomously generate a large quantity of cloned play data that can be used as additional training. We demonstrate that a general purpose goal-conditioned policy trained on this augmented dataset substantially outperforms one trained only with the original human data on 18 difficult user-specified manipulation tasks in a simulated robotic tabletop environment. A video example of a robot imitating human play can be seen here: https://learning-to-play.github.io/videos/undirected_play1.mp4
