Transfer learning across heterogeneous robots with action sequence mapping

Abstract

Transfer learning refers to reusing the knowledge gained while solving a task, to solve a related task more efficiently. Much of the prior work on transfer learning, assumes that identical robots were involved in both the tasks. In this work we focus on transfer learning across heterogeneous robots while solving the same task. The action capabilities of the robots are different and are unknown to each other. The actions of one robot cannot be mimicked by another even if known. Such situations arise in multi-robot systems. The objective then is to speed-up the learning of one robot, i.e., reduce its initial exploration, using very minimal knowledge from a different robot. We propose a framework in which the knowledge transfer is effected through a pseudo reward function generated from the trajectories followed by a different robot while solving the same task. The framework can effectively be used even with a single trajectory.We extend the framework to enable the robot to learn an equivalence between certain sequences of its actions and certain sequences of actions of the other robot. These are then used to learn faster on subsequent tasks. We empirically validate the framework in a rooms world domain.