模倣学習による自律移動制御のためのドメイン適応

抄録

Imitation learning is one of the techniques for autonomous control in robotics. This technique enables robot to autonomous control by human demonstrations. However, in a different environment from demonstrations, test of autonomous control based on learning result is often fail because domain that distribution of dataset between demonstration and test is difference. In domain adaptation, this approach transfers knowledge of an environment with sufficient domain to an environment with insufficient domain and it enables autonomous control even in an environment with a small amount of demonstration. We verify the domain adaptation to autonomous moving control by imitation learning and clarify the problem.