Efficient skill acquisition, representation, and on-line adaptation to different scenarios has become of fundamental importance for assistive robotic applications. In the past decade, dynamical systems (DS) have arisen as a flexible and robust tool to represent learned skills and to generate motion trajectories. This work presents a novel approach to incrementally modify the dynamics of a generic autonomous DS when new demonstrations of a task are provided. A control input is learned from demonstrations to modify the trajectory of the system while preserving the stability properties of the reshaped DS. Learning is performed incrementally through Gaussian process regression, increasing the robot's knowledge of the skill every time a new demonstration is provided. The effectiveness of the proposed approach is demonstrated with experiments on a publicly available dataset of complex motions.

中文翻译：

---

稳定动力系统的增量技能学习

有效的技能获取、表示和对不同场景的在线适应已成为辅助机器人应用的基础。在过去的十年中，动态系统 (DS) 已成为一种灵活而强大的工具，用于表示所学技能和生成运动轨迹。这项工作提出了一种新方法，可以在提供新的任务演示时逐步修改通用自主 DS 的动态。从演示中学习控制输入以修改系统的轨迹，同时保持重塑的 DS 的稳定性属性。学习是通过高斯过程回归逐步进行的，每次提供新的演示时都会增加机器人对技能的了解。