  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. We present this in the context of an autonomous MAV flight using
monocular reactive control, and demonstrate the efficacy of our proposed
approach through extensive real-world flight experiments in outdoor cluttered
environments.
