Recognition Models to Learn Dynamics from Partial Observations with Neural ODEs

TL;DR

This paper introduces recognition models inspired by nonlinear observer theory to improve system identification of dynamical systems from partial observations using Neural ODEs, demonstrated on simulations and robotic data.

Contribution

It proposes a novel recognition model framework for partial observations in Neural ODEs, integrating physical insights for better system identification.

Findings

Effective in numerical simulations

Successful application to robotic exoskeleton data

Enhanced interpretability of latent space

Abstract

Identifying dynamical systems from experimental data is a notably difficult task. Prior knowledge generally helps, but the extent of this knowledge varies with the application, and customized models are often needed. Neural ordinary differential equations can be written as a flexible framework for system identification and can incorporate a broad spectrum of physical insight, giving physical interpretability to the resulting latent space. In the case of partial observations, however, the data points cannot directly be mapped to the latent state of the ODE. Hence, we propose to design recognition models, in particular inspired by nonlinear observer theory, to link the partial observations to the latent state. We demonstrate the performance of the proposed approach on numerical simulations and on an experimental dataset from a robotic exoskeleton.