论文标题
学习稳定的动力系统用于视觉致密暗销
Learning Stable Dynamical Systems for Visual Servoing
论文作者
论文摘要
这项工作给出了基于动态系统形式主义的仿制学习技术的双重好处，并提供了视觉伺服范式。一方面，动态系统允许在不明确编码视觉伺服定律的情况下对额外的技能进行编程，但很少有人证明所有所需的行为。另一方面，Visual Servoing允许将外观感知到动态系统体系结构中，并能够适应意外的环境变化。通过将三种现有的动态系统方法应用于视觉致密涂层案例，这两个概念的有益组合可以证明。模拟验证并比较方法；使用机器人操纵器进行的实验显示了在现实情况下该方法的有效性。
This work presents the dual benefit of integrating imitation learning techniques, based on the dynamical systems formalism, with the visual servoing paradigm. On the one hand, dynamical systems allow to program additional skills without explicitly coding them in the visual servoing law, but leveraging few demonstrations of the full desired behavior. On the other, visual servoing allows to consider exteroception into the dynamical system architecture and be able to adapt to unexpected environment changes. The beneficial combination of the two concepts is proven by applying three existing dynamical systems methods to the visual servoing case. Simulations validate and compare the methods; experiments with a robot manipulator show the validity of the approach in a real-world scenario.
