TiLD: Third-person Imitation Learning by Estimating Domain Cognitive Differences of Visual Demonstrations

To enable agents to effectively imitate from the third-person visual demonstrations in complex imitation learning (IL) tasks, in this paper, we propose a new IL method, which is named third-person imitation learning by estimating domain cognitive differences (TiLD). The proposed TiLD is able to eliminate the domain cognitive difference between the samples from different perspectives, so as to achieve the purpose of allowing agent to directly learn from the third-person demonstrations. Experimental results indicate that TiLD can achieve significant performance improvements over the existing state-of-the-art IL methods, when dealing with imitation learning tasks with third-person expert demonstrations.