论文标题
基于学习的分布稳健运动控制使用高斯流程
Learning-based distributionally robust motion control with Gaussian processes
论文作者
论文摘要
安全是基于学习的机器人和自主系统的关键问题，因为有关其环境的学习信息通常不可靠且不准确。在本文中，我们提出了一种风险感知的运动控制工具，该工具可抵抗有关未知动力学移动障碍的学术分配信息中错误的强大。我们的模型预测控制（MPC）方法的显着特征是它的能力是限制不安全风险的能力，即使在歧义集中，真实分布偏离了由高斯过程（GP）回归估计的分布。不幸的是，GP的分布强大的MPC问题是棘手的，因为最差的案例风险限制涉及歧义集中的无限二维优化问题。为了消除无限维度问题，我们开发了一种系统的重新重新制定方法，利用现代分配强大的优化技术。我们的方法的性能和实用性是通过使用非线性汽车样的车辆模型进行自动驾驶的模拟来证明的。
Safety is a critical issue in learning-based robotic and autonomous systems as learned information about their environments is often unreliable and inaccurate. In this paper, we propose a risk-aware motion control tool that is robust against errors in learned distributional information about obstacles moving with unknown dynamics. The salient feature of our model predictive control (MPC) method is its capability of limiting the risk of unsafety even when the true distribution deviates from the distribution estimated by Gaussian process (GP) regression, within an ambiguity set. Unfortunately, the distributionally robust MPC problem with GP is intractable because the worst-case risk constraint involves an infinite-dimensional optimization problem over the ambiguity set. To remove the infinite-dimensionality issue, we develop a systematic reformulation approach exploiting modern distributionally robust optimization techniques. The performance and utility of our method are demonstrated through simulations using a nonlinear car-like vehicle model for autonomous driving.
