圆形迷宫中导航前向动力学模型的半参数高斯过程学习
Semiparametrical Gaussian Processes Learning of Forward Dynamical Models for Navigating in a Circular Maze
摘要：
本文提出了一个模型学习问题，目的是学习如何在具有两个自由度的圆形迷宫环境中将球导航到目标状态。球在迷宫环境中的运动受到几种非线性效应的影响，例如干摩擦和接触，这些非线性效应很难在物理上建模。我们提出了一个半参数模型来估计球的运动动力学的基础上高斯过程回归配备从物理第一原理获得的基函数。这种半参数模型的准确性，不仅在估计，而且在预测的n步前进，并与标准算法的模型学习。然后，在轨迹优化算法中使用所学习的模型来计算球轨迹。我们提出的系统中提出的文件作为一个基准问题的强化和机器人学习，其有趣的和具有挑战性的动态和相对容易的再现性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
