  We observe a large variety of robots in terms of their bodies, sensors, and
actuators. Given the commonalities in the skill sets, teaching each skill to
each different robot independently is inefficient and not scalable when the
large variety in the robotic landscape is considered. If we can learn the
correspondences between the sensorimotor spaces of different robots, we can
expect a skill that is learned in one robot can be more directly and easily
transferred to other robots. In this paper, we propose a method to learn
correspondences among two or more robots that may have different morphologies.
To be specific, besides robots with similar morphologies with different degrees
of freedom, we show that a fixed-based manipulator robot with joint control and
a differential drive mobile robot can be addressed within the proposed
framework. To set up the correspondence among the robots considered, an initial
base task is demonstrated to the robots to achieve the same goal. Then, a
common latent representation is learned along with the individual robot
policies for achieving the goal. After the initial learning stage, the
observation of a new task execution by one robot becomes sufficient to generate
a latent space representation pertaining to the other robots to achieve the
same task. We verified our system in a set of experiments where the
correspondence between robots is learned (1) when the robots need to follow the
same paths to achieve the same task, (2) when the robots need to follow
different trajectories to achieve the same task, and (3) when complexities of
the required sensorimotor trajectories are different for the robots. We also
provide a proof-of-the-concept realization of correspondence learning between a
real manipulator robot and a simulated mobile robot.
