  Transfer learning is a conceptually-enticing paradigm in pursuit of truly
intelligent embodied agents. The core concept -- reusing prior knowledge to
learn in and from novel situations -- is successfully leveraged by humans to
handle novel situations. In recent years, transfer learning has received
renewed interest from the community from different perspectives, including
imitation learning, domain adaptation, and transfer of experience from
simulation to the real world, among others. In this paper, we unify the concept
of transfer learning in robotics and provide the first taxonomy of its kind
considering the key concepts of robot, task, and environment. Through a review
of the promises and challenges in the field, we identify the need of
transferring at different abstraction levels, the need of quantifying the
transfer gap and the quality of transfer, as well as the dangers of negative
transfer. Via this position paper, we hope to channel the effort of the
community towards the most significant roadblocks to realize the full potential
of transfer learning in robotics.
