An Empirical Evaluation of Meta-Learning for Visual Navigation Adapt to New Environments with Few Training Data

Abstract

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.