Safe learning in robotics: From learning-based control to safe reinforcement learning
The last half decade has seen a steep rise in the number of contributions on safe learning
methods for real-world robotic deployments from both the control and reinforcement learning …
methods for real-world robotic deployments from both the control and reinforcement learning …
The safety filter: A unified view of safety-critical control in autonomous systems
Recent years have seen significant progress in the realm of robot autonomy, accompanied
by the expanding reach of robotic technologies. However, the emergence of new …
by the expanding reach of robotic technologies. However, the emergence of new …
[HTML][HTML] Magnetic control of tokamak plasmas through deep reinforcement learning
Nuclear fusion using magnetic confinement, in particular in the tokamak configuration, is a
promising path towards sustainable energy. A core challenge is to shape and maintain a …
promising path towards sustainable energy. A core challenge is to shape and maintain a …
Learning-based model predictive control: Toward safe learning in control
Recent successes in the field of machine learning, as well as the availability of increased
sensing and computational capabilities in modern control systems, have led to a growing …
sensing and computational capabilities in modern control systems, have led to a growing …
Safe reinforcement learning using robust MPC
Reinforcement learning (RL) has recently impressed the world with stunning results in
various applications. While the potential of RL is now well established, many critical aspects …
various applications. While the potential of RL is now well established, many critical aspects …
Actor-critic model predictive control
A Romero, Y Song… - 2024 IEEE International …, 2024 - ieeexplore.ieee.org
An open research question in robotics is how to combine the benefits of model-free
reinforcement learning (RL)—known for its strong task performance and flexibility in …
reinforcement learning (RL)—known for its strong task performance and flexibility in …
Autonomous drone racing: A survey
Over the last decade, the use of autonomous drone systems for surveying, search and
rescue, or last-mile delivery has increased exponentially. With the rise of these applications …
rescue, or last-mile delivery has increased exponentially. With the rise of these applications …
Integrating machine learning and model predictive control for automotive applications: A review and future directions
In this review paper, the integration of Machine Learning (ML) and Model Predictive Control
(MPC) in Automotive Control System (ACS) applications are discussed. ACS can be divided …
(MPC) in Automotive Control System (ACS) applications are discussed. ACS can be divided …
[HTML][HTML] Artificial Intelligence in manufacturing: State of the art, perspectives, and future directions
Inspired by the natural intelligence of humans and bio-evolution, Artificial Intelligence (AI)
has seen accelerated growth since the beginning of the 21st century. Successful AI …
has seen accelerated growth since the beginning of the 21st century. Successful AI …
Data-driven safety filters: Hamilton-jacobi reachability, control barrier functions, and predictive methods for uncertain systems
Today's control engineering problems exhibit an unprecedented complexity, with examples
including the reliable integration of renewable energy sources into power grids, safe …
including the reliable integration of renewable energy sources into power grids, safe …