Semiparametrical Gaussian Processes Learning of Forward Dynamical Models for Navigating in a Circular Maze
It addresses model learning for robot navigation in complex environments, presenting it as a benchmark for reinforcement and robot learning, but is incremental as it combines existing methods.
The paper tackled learning a forward dynamical model for navigating a ball in a circular maze with non-linear effects like friction and contacts, proposing a semiparametric Gaussian Process model that showed accuracy in estimation and prediction compared to standard algorithms, and used it for trajectory optimization.
This paper presents a problem of model learning for the purpose of learning how to navigate a ball to a goal state in a circular maze environment with two degrees of freedom. The motion of the ball in the maze environment is influenced by several non-linear effects such as dry friction and contacts, which are difficult to model physically. We propose a semiparametric model to estimate the motion dynamics of the ball based on Gaussian Process Regression equipped with basis functions obtained from physics first principles. The accuracy of this semiparametric model is shown not only in estimation but also in prediction at n-steps ahead and its compared with standard algorithms for model learning. The learned model is then used in a trajectory optimization algorithm to compute ball trajectories. We propose the system presented in the paper as a benchmark problem for reinforcement and robot learning, for its interesting and challenging dynamics and its relative ease of reproducibility.