Transfer learning of shared latent spaces between robots with similar kinematic structure

Learning complex manipulation tasks often requires to collect a large training dataset to obtain a model of a specific skill. This process may become laborious when dealing with high-DoF robots, and even more tiresome if the skill needs to be learned by multiple robots. In this paper, we investigate how this learning process can be accelerated by using shared latent variable models for knowledge transfer among similar robots in an imitation setting. For this purpose, we take advantage of a shared Gaussian process latent variable model to learn a common latent representation of robot skills. Such representation is then reused as prior information to train new robots by reducing the learning process to a latent-to-output mapping. We show that our framework exhibits faster training convergence and similar performance when compared to single- and multi-robot models. All experiments were conducted in simulation on three different robotic platforms: WALK-MAN, COMAN and CENTAURO robots.