人間の動画から一発学習：ドメイン適応メタ学習による模倣
One-Shot Imitation from Observing Humans via Domain-Adaptive Meta-Learning
メタ学習で事前知識を獲得し、人間の1本の動画からロボットが新しい物体の配置・押し・ピック＆プレースを学習できる手法を提案。
著者: Tianhe Yu, Chelsea Finn, Annie Xie, Sudeep Dasari, 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
Humans and animals are capable of learning a new behavior by observing others perform the skill just once. We consider the problem of allowing a robot to do the same -- learning from a raw video pixels of a human, even when there is substantial domain shift in the perspective, environment, and embodiment between the robot and the observed human. Prior approaches to this problem have hand-specified how human and robot actions correspond and often relied on explicit human pose detection systems. In this work, we present an approach for one-shot learning from a video of a human by using human and robot demonstration data from a variety of previous tasks to build up prior knowledge through meta-learning. Then, combining this prior knowledge and only a single video demonstration from a human, the robot can perform the task that the human demonstrated. We show experiments on both a PR2 arm and a Sawyer arm, demonstrating that after meta-learning, the robot can learn to place, push, and pick-and-place new objects using just one video of a human performing the manipulation.
