Learning Transferable Policies for Monocular Reactive MAV Control

Understand

The ability to transfer knowledge gained in previous tasks into new contexts is one of the most important mechanisms of human learning.

• Despite this, adapting autonomous behavior to be reused in partially similar settings is still an open problem in current robotics research.
• In this paper, we take a small step in this direction and propose a generic framework for learning transferable motion policies.
• Our goal is to solve a learning problem in a target domain by utilizing the training data in a different but related source domain.