Active Third-Person Imitation Learning
Timo Klein Susanna Weinberger Adish Singla Sebastian Tschiatschek
We consider the problem of third-person imitation learning with the
additional challenge that the learner must select the perspective from which
they observe the expert. In our setting, each perspective provides only limited
information about the expert's behavior, and the learning agent must carefully
select and combine information from different perspectives to achieve
competitive performance. This setting is inspired by real-world imitation
learning applications, e.g., in robotics, a robot might observe a human
demonstrator via camera and receive information from different perspectives
depending on the camera's position. We formalize the aforementioned active
third-person imitation learning problem, theoretically analyze its
characteristics, and propose a generative adversarial network-based active
learning approach. Empirically, we demstrate that our proposed approach can
effectively learn from expert demonstrations and explore the importance of
different architectural choices for the learner's performance.