The field of dynamical systems is being transformed by the mathematical tools and
algorithms emerging from modern computing and data science. First-principles derivations …

In this paper, we describe the advances in the design, actuation, modeling, and control field
of continuum robots. After decades of pioneering research, many innovative structural …

Soft robots promise improved safety and capability over rigid robots when deployed near
humans or in complex, delicate, and dynamic environments. However, infinite degrees of …

Controlling soft robots with precision is a challenge due to the difficulty of constructing
models that are amenable to model-based control design techniques. Koopman operator …

In this work, we demonstrate how physical principles—such as symmetries, invariances and
conservation laws—can be integrated into the dynamic mode decomposition (DMD). DMD is …

This article reviews the state-of-the-art modeling and control techniques for aerial robots
such as quadrotor systems and presents several future research directions in this area. The …

Active learning is a decision-making process. In both abstract and physical settings, active
learning demands both analysis and action. This is a review of active learning in robotics …

Nonlinear dynamical systems can be made easier to control by lifting them into the space of
observable functions, where their evolution is described by the linear Koopman operator …

In this article, we propose an integrated calibration and identification method that can
identify kinematic and dynamic parameters at the same time. Only a series of static …

We study the application of a data‐enabled predictive control (DeePC) algorithm for position
control of real‐world nano‐quadcopters. The DeePC algorithm is a finite‐horizon, optimal …