NODE and Contraction Methods for Dynamics Learning from Human Expert Demonstrations

초록

In this paper, we propose model-free or learning-from-demonstration methodologies for accurately estimating the complex and nonlinear behaviors of dynamic systems such as mobile robots, robotic arm manipulators, and unmanned aerial vehicles (UAVs). Under learning from demonstration (LfD), this study investigates two different approaches: The first proposed methodology is the contraction theory, in which the assigned task demonstration is practically performed by the human expert, who tries to learn and imitate it. On the other hand, the same task learns and imitates by utilizing the neural ordinary differential equations (NODEs) for dynamic systems. Using the concepts of both approaches, we tried to make it possible for the system to pick up on and imitate the shown behavior or demonstration accurately. In dynamics learning, the proposed contraction method utilizes the conceptual framework of the contraction theory, which ensures the motions of dynamic systems that eventually converge to nominal or desired behavior. At the same time, NODE uses the neural network with different configurations of hidden layers, learning rate, nonlinear activation function, and ODE solver. A spiral trajectory is considered a human expert demonstration that is estimated by both methodologies (i) NODE and (ii) contraction theory. For validation purposes, we compared the results of both approaches. © 2024 by SCITEPRESS-Science and Technology Publications, Lda.

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