Soft robots' flexibility and compliance give them the potential to outperform traditional rigid-
bodied robots while performing multiple tasks in unexpectedly changing environments and …

Skeleton-based human action recognition is becoming increasingly important in a variety of
fields. Most existing works train a CNN or GCN based backbone to extract spatial-temporal …

Abstract Purpose of Review We review recent advances in algorithmic development and
validation for modeling and control of soft robots leveraging the Koopman operator theory …

An accurate, physically-based, and differentiable model of soft robots can unlock
downstream applications in optimal control. The Finite Element Method (FEM) is an …

Disentangling complex data to its latent factors of variation is a fundamental task in
representation learning. Existing work on sequential disentanglement mostly provides two …

In this work, we consider the problem of Koopman modeling and data-driven predictive
control for a class of uncertain nonlinear systems subject to time delays. A robust deep …

Existing modeling and control methods for real-world systems typically deal with uncertainty
and nonlinearity on a case-by-case basis. We present a universal and robust control …

Generating realistic time series data is important for many engineering and scientific
applications. Existing work tackles this problem using generative adversarial networks …

The Koopman operator framework provides a perspective that non-linear dynamics can be
described through the lens of linear operators acting on function spaces. As the framework …

Developing reliable health indicators for industrial assets is essential for accurate condition
monitoring, fault detection, and predicting the remaining useful lifetime. However …