スキルパラメータの転送によるシンプルな継続学習手法
A Simple Approach to Continual Learning by Transferring Skill Parameters
学習済みスキルポリシーのみを保存し、適切な事前学習とスキル転送のタイミング選択により、ロボットマニピュレーションの継続学習を実現する手法を提案した論文。
著者: K. R. Zentner, Ryan Julian, Ujjwal Puri, Yulun Zhang, Gaurav S. Sukhatme
分類: cs.LG, cs.RO
原文アブストラクト
In order to be effective general purpose machines in real world environments, robots not only will need to adapt their existing manipulation skills to new circumstances, they will need to acquire entirely new skills on-the-fly. A great promise of continual learning is to endow robots with this ability, by using their accumulated knowledge and experience from prior skills. We take a fresh look at this problem, by considering a setting in which the robot is limited to storing that knowledge and experience only in the form of learned skill policies. We show that storing skill policies, careful pre-training, and appropriately choosing when to transfer those skill policies is sufficient to build a continual learner in the context of robotic manipulation. We analyze which conditions are needed to transfer skills in the challenging Meta-World simulation benchmark. Using this analysis, we introduce a pair-wise metric relating skills that allows us to predict the effectiveness of skill transfer between tasks, and use it to reduce the problem of continual learning to curriculum selection. Given an appropriate curriculum, we show how to continually acquire robotic manipulation skills without forgetting, and using far fewer samples than needed to train them from scratch.
