以动能为基础，确保学习动态系统的稳定性（CS RO)

非线性动力系统是一种紧凑、灵活、有力的反应运动生成工具。 动态系统的有效性依赖于它们精确表示稳定运动的能力。 为了从演示中学习稳定和准确的运动，已经提出了几种方法。 有些方法将精度和稳定性分离为两个学习问题，增加了开放参数的数量和总的训练时间。 替代解决方案利用了单向性学习，但限制了对一种回归技术的适用性。 本文提出了一个单步方法来学习稳定和准确的运动，工作与任何回归技术。 该方法使考虑学习动态稳定系统在运行时，同时引入小偏差的演示运动。 由于注入系统的能量的初始值影响再生产的准确性，它是使用一个有效的程序从训练数据开始估计。 在实际机器人上的实验和公共基准上的比较表明了该方法的有效性。

原文题目：An Energy-based Approach to Ensure the Stability of Learned Dynamical Systems

原文：Non-linear dynamical systems represent a compact, flexible, and robust tool for reactive motion generation. The effectiveness of dynamical systems relies on their ability to accurately represent stable motions. Several approaches have been proposed to learn stable and accurate motions from demonstration. Some approaches work by separating accuracy and stability into two learning problems, which increases the number of open parameters and the overall training time. Alternative solutions exploit single-step learning but restrict the applicability to one regression technique. This paper presents a single-step approach to learn stable and accurate motions that work with any regression technique. The approach makes energy considerations on the learned dynamics to stabilize the system at run-time while introducing small deviations from the demonstrated motion. Since the initial value of the energy injected into the system affects the reproduction accuracy, it is estimated from training data using an efficient procedure. Experiments on a real robot and a comparison on a public benchmark shows the effectiveness of the proposed approach.

原文作者： Matteo Saveriano

原文地址：https://arxiv.org/abs/2003.11290