Unsupervised Learning of Lagrangian Dynamics from Images for Prediction and Control
Unsupervised Learning of Lagrangian Dynamics from Images for Prediction and Control
复制标题
用于预测和控制的图像拉格朗日动力学无监督学习
DOI：
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
Naomi Ehrich Leonard
中科院分区：
文献类型：
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作者：
Yaofeng Desmond Zhong;Naomi Ehrich Leonard
最近用神经网络对物理系统的动力学进行建模的方法加强了拉格朗日或哈密顿结构，以提高预测和泛化能力。然而，这些方法无法处理当坐标被嵌入在诸如图像的高维数据中时的情况。我们引入了一种新的无监督神经网络模型，它可以从图像中学习拉格朗日动力学，具有有利于预测和控制的可解释性。该模型在广义坐标上推断拉格朗日动力学，同时使用坐标感知变分自动编码器（VAE）学习。VAE的目的是考虑到几何的物理系统组成的多个刚体在平面上。通过推断可解释的拉格朗日动力学，该模型学习物理系统属性，例如动能和势能，这使得能够长期预测图像空间中的动力学和基于能量的控制器的合成。
Recent approaches for modelling dynamics of physical systems with neural networks enforce Lagrangian or Hamiltonian structure to improve prediction and generalization. However, these approaches fail to handle the case when coordinates are embedded in high-dimensional data such as images. We introduce a new unsupervised neural network model that learns Lagrangian dynamics from images, with interpretability that benefits prediction and control. The model infers Lagrangian dynamics on generalized coordinates that are simultaneously learned with a coordinate-aware variational autoencoder (VAE). The VAE is designed to account for the geometry of physical systems composed of multiple rigid bodies in the plane. By inferring interpretable Lagrangian dynamics, the model learns physical system properties, such as kinetic and potential energy, which enables long-term prediction of dynamics in the image space and synthesis of energy-based controllers.
可解释的多项式神经常微分方程。
DOI：
10.1063/5.0130803
发表时间：
2023
期刊：
Chaos (Woodbury, N.Y.)
影响因子：
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作者：
Fronk,Colby;Petzold,Linda
通讯作者：
Petzold,Linda