  In the robotics literature, different knowledge transfer approaches have been
proposed to leverage the experience from a source task or robot -- real or
virtual -- to accelerate the learning process on a new task or robot. A
commonly made but infrequently examined assumption is that incorporating
experience from a source task or robot will be beneficial. In practice,
inappropriate knowledge transfer can result in negative transfer or unsafe
behaviour. In this work, inspired by a system gap metric from robust control
theory, the $\nu$-gap, we present a data-efficient algorithm for estimating the
similarity between pairs of robot systems. In a multi-source inter-robot
transfer learning setup, we show that this similarity metric allows us to
predict relative transfer performance and thus informatively select experiences
from a source robot before knowledge transfer. We demonstrate our approach with
quadrotor experiments, where we transfer an inverse dynamics model from a real
or virtual source quadrotor to enhance the tracking performance of a target
quadrotor on arbitrary hand-drawn trajectories. We show that selecting
experiences based on the proposed similarity metric effectively facilitates the
learning of the target quadrotor, improving performance by 62% compared to a
poorly selected experience.
