A reservoir computing approach for learning forward dynamics of industrial manipulators

Many robot learning algorithms depend on a model of the robot's forward dynamics for simulating potential trajectories and ultimately learning a required task. In this paper, we present a data-driven reservoir computing approach and apply it for learning forward dynamics models. Our proposed machine learning algorithm exploits the concepts of dynamic reservoir, self-organized learning and Bayesian inference. We have evaluated our approach on datasets gathered from two industrial robotic manipulators and compared it on both step-by-step and multi-step trajectory prediction scenarios with state-of-the-art algorithms. The evaluation considers the algorithms' convergence and prediction performance on joint and operational space for varying prediction horizons, as well as computational time. Results show that the proposed algorithm performs better than the state-of-the-art, converges fast and can achieve accurate predictions over longer horizons, which makes it a reliable, data-efficient approach for learning forward models.