ニューラルネットワークを用いたリャプノフエネルギー関数による安定な動的システムの学習
Learning a Stable Dynamic System with a Lyapunov Energy Function for Demonstratives Using Neural Networks
デモンストレーション学習において、ニューラルネットワークでリャプノフエネルギー関数を同時に学習し、精度と大域的安定性を両立させる動的システムの手法を提案した。
著者: Yu Zhang, Yongxiang Zou, Haoyu Zhang, Xiuze Xia, Long Cheng
分類: cs.RO
原文アブストラクト
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.
