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 …

Most policy search (PS) algorithms require thousands of training episodes to find an
effective policy, which is often infeasible with a physical robot. This survey article focuses on …

While deep reinforcement learning (RL) has fueled multiple high-profile successes in
machine learning, it is held back from more widespread adoption by its often poor data …

Real-time adaptation is imperative to the control of robots operating in complex, dynamic
environments. Adaptive control laws can endow even nonlinear systems with good trajectory …

Transporting suspended payloads is challenging for autonomous aerial vehicles because
the payload can cause significant and unpredictable changes to the robot's dynamics. These …

Model-based control requires an accurate model of the system dynamics for precisely and
safely controlling the robot in complex and dynamic environments. Moreover, in presence of …

Mobile manipulation in robotics is challenging due to the need to solve many diverse tasks,
such as opening a door or picking-and-placing an object. Typically, a basic first-principles …

We study representation learning for efficient imitation learning over linear systems. In
particular, we consider a setting where learning is split into two phases:(a) a pre-training …

Safe deployment of autonomous robots in diverse scenarios requires agents that are
capable of efficiently adapting to new environments while satisfying constraints. In this …

Real-time adaptation is imperative to the control of robots operating in complex, dynamic
environments. Adaptive control laws can endow even nonlinear systems with good trajectory …