Deep reinforcement learning has recently seen huge success across multiple areas in the
robotics domain. Owing to the limitations of gathering real-world data, ie, sample inefficiency …

Electrification turned over a new leaf in aviation by introducing new types of aerial vehicles
along with new means of transportation. Addressing a plethora of use cases, drones are …

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 …

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

You are holding in your hands… oh, come on, who holds books like this in their hands
anymore? Anyway, you are reading this, and it means that I have managed to release one of …

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

Develo** and testing algorithms for autonomous vehicles in real world is an expensive
and time consuming process. Also, in order to utilize recent advances in machine …

Neural networks predictions are unreliable when the input sample is out of the training
distribution or corrupted by noise. Being able to detect such failures automatically is …

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 …

Autonomous exploration of subterranean environments remains a major challenge for
robotic systems. In response, this paper contributes a novel graph‐based subterranean …