论文标题
稳定动力学系统的逐步学习
Incremental Skill Learning of Stable Dynamical Systems
论文作者
论文摘要
有效的技能获取，代表和对不同方案的在线适应已成为辅助机器人应用的基本重要性。在过去的十年中，动态系统（DS）已成为一种灵活而健壮的工具，可以代表学习技能并产生运动轨迹。这项工作提出了一种新颖的方法，可以在提供任务的新演示时逐步修改通用自动ds的动态。从演示中学到了控制输入，以修改系统的轨迹，同时保留重形DS的稳定性。学习是通过高斯流程回归逐步执行的，每次提供新的演示时都会提高机器人对技能的了解。通过在公开可用的复杂动作数据集上进行实验证明了拟议方法的有效性。
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.
