Abstract
The challenge of teaching robots to perform dexterous manipulation, dynamic locomotion, or whole--body manipulation from a small number of demonstrations is an important research field that has attracted interest from across the robotics community. In this work, we propose a novel approach by joining the theories of Koopman Operators and Dynamic Movement Primitives to Learning from Demonstration. Our approach, named \gls{admd}, projects nonlinear dynamical systems into linear latent spaces such that a solution reproduces the desired complex motion. Use of an autoencoder in our approach enables generalizability and scalability, while the constraint to a linear system attains interpretability. Our results are comparable to the Extended Dynamic Mode Decomposition on the LASA Handwriting dataset but with training on only a small fractions of the letters.
Abstract (translated)
教机器人进行灵巧操作、动态运动或全身操作,从少数演示中学习,是一个重要的机器人领域,吸引了来自机器人界的广泛关注。在这项工作中,我们提出了一个新方法,将Koopman操作理论和动态运动原型与从演示中学习相结合。我们的方法称为\gls{admd},将非线性动力学系统投影到线性潜在空间中,使得解决方案能够复制所需的复杂运动。使用自编码器在我们的方法中实现了泛化性和可扩展性,而将约束限制到线性系统使得可解释性得到实现。我们的结果与在LASA手写数据集上扩展动态模式分解类似,但只有对少数字母进行训练。
URL
https://arxiv.org/abs/2312.03328
