论文标题
一种基于能量的方法来确保学习动力学系统的稳定性
An Energy-based Approach to Ensure the Stability of Learned Dynamical Systems
论文作者
论文摘要
非线性动力学系统代表了一种紧凑，灵活且健壮的工具，用于产生反应性运动。动态系统的有效性取决于它们准确表示稳定运动的能力。已经提出了几种方法来从演示中学习稳定而准确的运动。某些方法通过将准确性和稳定性分为两个学习问题来起作用，这增加了开放参数的数量和整体训练时间。替代解决方案利用单步学习，但将适用性限制为一个回归技术。本文提出了一种单步方法，以学习与任何回归技术一起使用的稳定而准确的动作。该方法对学习的动力学进行了能量考虑，以在运行时稳定系统，同时引入与所示运动的小偏差。由于注入系统的能量的初始值会影响繁殖精度，因此可以使用有效的程序从训练数据中估算出来。对真正的机器人的实验以及对公共基准的比较显示了拟议方法的有效性。
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.
