  This paper presents a novel method for transferring motion planning and
control policies between a teacher and a learner robot. With this work, we
propose to reduce the sim-to-real gap, transfer knowledge designed for a
specific system into a different robot, and compensate for system aging and
failures. To solve this problem we introduce a Schwarz-Christoffel
mapping-based method to geometrically stretch and fit the control inputs from
the teacher into the learner command space. We also propose a method based on
primitive motion generation to create motion plans and control inputs
compatible with the learner's capabilities. Our approach is validated with
simulations and experiments with different robotic systems navigating occluding
environments.
