There are spatio-temporal rules that dictate how robots should operate in complex
environments, eg, road rules govern how (self-driving) vehicles should behave on the road …

Recent advancement in combining trajectory optimization with function approximation
(especially neural networks) shows promise in learning complex control policies for diverse …

While mixed-integer convex programs (MICPs) arise frequently in mixed-integer optimal
control problems (MIOCPs), current state-of-the-art MICP solvers are often too slow for real …

The recent successes in deep reinforcement learning largely rely on the capabilities of
generating masses of data, which in turn implies the use of a simulator. In particular, current …

Model predictive control (MPC) is a powerful tool to control systems with non-linear
dynamics and constraints, but its computational demands impose limitations on the …

Closed-loop optimal control design for high-dimensional nonlinear systems has been a long-
standing challenge. Traditional methods, such as solving the associated Hamilton-Jacobi …

Solving complex optimal control problems have confronted computational challenges for a
long time. Recent advances in machine learning have provided us with new opportunities to …

Reinforcement learning and optimal control are two approaches to solving the decision-
making problem for dynamical systems, with a data-driven and model-driven perspective …

Advances in the fields of artificial intelligence and machine learning have unlocked a new
generation of robotic systems---" learning-enabled" robots that are designed to operate in …