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.

翻译：基于自主动态系统的算法在示教学习领域具有基础和核心地位。然而，这些算法面临着在学习精度与系统整体稳定性之间达成微妙平衡的严峻挑战。针对这一重大挑战，本文提出一种基于神经网络技术的新型动态系统算法。该算法不仅能从示教数据中提取关键知识，还能学习与给定数据相容的候选李雅普诺夫能量函数。本文提出的模型采用简洁的神经网络架构，能够完美实现双重目标：在保持全局稳定性的同时优化学习精度。为全面评估所提算法的有效性，我们使用LASA数据集开展了严格评估，并通过机器人实验进行了实证验证。