Learning a Stable Dynamic System with a Lyapunov Energy Function for Demonstratives Using Neural Networks
Abstract
Autonomous Dynamic System (DS)-based algorithms hold a pivotal and foundational role in the field of Learning from Demonstration (LfD). Nevertheless, they confront the formidable challenge of striking a delicate balance between achieving precision in learning and ensuring the overall stability of the system. In response to this substantial challenge, this paper introduces a novel DS algorithm rooted in neural network technology. This algorithm not only possesses the capability to extract critical insights from demonstration data but also demonstrates the capacity to learn a candidate Lyapunov energy function that is consistent with the provided data. The model presented in this paper employs a straightforward neural network architecture that excels in fulfilling a dual objective: optimizing accuracy while simultaneously preserving global stability. To comprehensively evaluate the effectiveness of the proposed algorithm, rigorous assessments are conducted using the LASA dataset, further reinforced by empirical validation through a robotic experiment.
Cite
@article{arxiv.2309.08849,
title = {Learning a Stable Dynamic System with a Lyapunov Energy Function for Demonstratives Using Neural Networks},
author = {Yu Zhang and Yongxiang Zou and Haoyu Zhang and Xiuze Xia and Long Cheng},
journal= {arXiv preprint arXiv:2309.08849},
year = {2024}
}