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

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 …

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 …

This paper presents an active learning strategy for robotic systems that takes into account
task information, enables fast learning, and allows control to be readily synthesized by …

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

This article presents a generalizable methodology for data-driven identification of nonlinear
dynamics that bounds the model error in terms of the prediction horizon and the magnitude …

To operate with limited sensor horizons in unpredictable environments, autonomous robots
use a receding-horizon strategy to plan trajectories, wherein they execute a short plan while …

We address the problem of optimizing the performance of a dynamic system while satisfying
hard safety constraints at all times. Implementing an optimal control solution is limited by the …

This paper explores the application of Koopman operator theory to the control of robotic
systems. The operator is introduced as a method to generate data-driven models that have …

This paper presents a data-driven methodology for linear embedding of nonlinear systems.
Utilizing structural knowledge of general nonlinear dynamics, the authors exploit the …