Machine learning is rapidly advancing nearly every field of science and engineering, and
control theory is no exception. In particular, it has shown incredible promise for handling …

The efficient control of complex dynamical systems has many applications in the natural and
applied sciences. In most real-world control problems, both control energy and cost …

Koopman operator theory has proven to be a promising approach to nonlinear system
identification and global linearization. For nearly a century, there had been no efficient …

The performance of robots in high-level tasks depends on the quality of their lower level
controller, which requires fine-tuning. However, the intrinsically nonlinear dynamics and …

Data-driven methods such as reinforcement and imitation learning have achieved
remarkable success in robot autonomy. However, their data-centric nature still hinders them …

This paper proposes a new method for differentiating through optimal trajectories arising
from non-convex, constrained discrete-time optimal control (COC) problems using the …

Abstract We propose a Safe Pontryagin Differentiable Programming (Safe PDP)
methodology, which establishes a theoretical and algorithmic framework to solve a broad …

We present a new solver for non-convex trajectory optimization problems that is specialized
for robotics applications. CALIPSO, or the Conic Augmented Lagrangian Interior-Point …

A Stackelberg congestion game (SCG) is a bilevel program in which a leader aims to
maximize their own gain by anticipating and manipulating the equilibrium state at which …

In contact-rich tasks, like dexterous manipulation, the hybrid nature of making and breaking
contact creates challenges for model representation and control. For example, choosing and …