メタ学習によるワンショット視覚模倣学習
One-Shot Visual Imitation Learning via Meta-Learning
メタ学習を用いて、ロボットが1回の視覚的なデモンストレーションから新しいスキルを学習できる手法を提案した論文。
著者: Chelsea Finn, Tianhe Yu, Tianhao Zhang, Pieter Abbeel, Sergey Levine
分類: cs.LG, cs.AI, cs.CV, cs.RO
原文アブストラクト
In order for a robot to be a generalist that can perform a wide range of jobs, it must be able to acquire a wide variety of skills quickly and efficiently in complex unstructured environments. High-capacity models such as deep neural networks can enable a robot to represent complex skills, but learning each skill from scratch then becomes infeasible. In this work, we present a meta-imitation learning method that enables a robot to learn how to learn more efficiently, allowing it to acquire new skills from just a single demonstration. Unlike prior methods for one-shot imitation, our method can scale to raw pixel inputs and requires data from significantly fewer prior tasks for effective learning of new skills. Our experiments on both simulated and real robot platforms demonstrate the ability to learn new tasks, end-to-end, from a single visual demonstration.
