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 …

This article is about deep learning (DL) and deep reinforcement learning (DRL) works
applied to robotics. Both tools have been shown to be successful in delivering data-driven …

Semantic navigation is necessary to deploy mobile robots in uncontrolled environments
such as homes or hospitals. Many learning-based approaches have been proposed in …

Quadrotors are agile. Unlike most other machines, they can traverse extremely complex
environments at high speeds. To date, only expert human pilots have been able to fully …

Building general-purpose robots that operate seamlessly in any environment, with any
object, and utilizing various skills to complete diverse tasks has been a long-standing goal in …

Autonomous, agile quadrotor flight raises fundamental challenges for robotics research in
terms of perception, planning, learning, and control. A versatile and standardized platform is …

Aerodynamic forces render accurate high-speed trajectory tracking with quadrotors
extremely challenging. These complex aerodynamic effects become a significant …

Legged robots have the potential to expand the reach of autonomy beyond paved roads. In
this work, we consider the difficult problem of locomotion on challenging terrains using a …

In many robotic tasks, such as autonomous drone racing, the goal is to travel through a set of
waypoints as fast as possible. A key challenge for this task is planning the timeoptimal …

State-of-the-art quadrotor simulators have a rigid and highly-specialized structure: either are
they really fast, physically accurate, or photo-realistic. In this work, we propose a paradigm …