In this survey, we review the recent advances in control design methods for robotic multi-
agent systems (MAS), focusing on learning-based methods with safety considerations. We …

As autonomous driving technology matures, end-to-end methodologies have emerged as a
leading strategy, promising seamless integration from perception to control via deep …

This book presents the concept of Control Barrier Function (CBF), which captures the
evolution of safety requirements during the execution of a system and can be used to …

Robots and animals both experience the world through their bodies and senses. Their
embodiment constrains their experiences, ensuring that they unfold continuously in space …

Diffusion model-based approaches have shown promise in data-driven planning, but there
are no safety guarantees, thus making it hard to be applied for safety-critical applications. To …

The surge in autonomous robotic applications across various sectors highlights the crucial
need for effective robot battery management to ensure robots perform their tasks …

This paper proposes a new method for differentiating through optimal trajectories arising
from non-convex, constrained discrete-time optimal control (COC) problems using the …

This paper presents RAYEN, a framework to impose hard convex constraints on the output
or latent variable of a neural network. RAYEN guarantees that, for any input or any weights …

This paper presents a safe learning control strategy aimed at ensuring the accurate tracking
of multiple unmanned aerial vehicles (UAVs) along their predetermined trajectories while …

We consider perception-based control using state estimates that are obtained from high-
dimensional sensor measurements via learning-enabled perception maps. However, these …