新たな環境へ少ない学習データで適応させる自律移動制御のためのメタ学習の検証

抄録

Imitation learning is one of the techniques for visual navigation in robotics. This technique enables robot to visual navigation by human demonstrations. However, in a different environment from demonstrations, evaluation of visual navigation based on a learned model often fail because domain that is distribution of dataset between demonstration data and test data is difference. In meta-learning, the goal of the trained model is to learn a new task from a few shot data. We apply a new task as a new domain in meta-learning to imitation learning of visual navigation. We evaluate not only meta-learning but also domain adaptation to visual navigation by imitation learning and clarify the problem.