We study the multi-agent safe control problem where agents should avoid collisions to static
obstacles and collisions with each other while reaching their goals. Our core idea is to learn …

Developing reinforcement learning algorithms that satisfy safety constraints is becoming
increasingly important in real-world applications. In multi-agent reinforcement learning …

Safety in terms of collision avoidance for multi-robot systems is a difficult challenge under
uncertainty, non-determinism, and lack of complete information. This paper aims to propose …

We introduce an automated, formal, counterexample-based approach to synthesise Barrier
Certificates (BC) for the safety verification of continuous and hybrid dynamical models. The …

This paper presents two new control approaches for guaranteed safety (remaining in a safe
set) subject to actuator constraints (the control is in a convex polytope). The control signals …

Robot missions typically involve a number of desired objectives, such as avoiding collisions,
staying connected to other robots, gathering information using sensors and returning to the …

Robot swarms have been shown to improve the ability of individual robots by inter-robot
collaboration. In this paper, we present the PuzzleBots-a low-cost robotic swarm system …

Multi-Agent Reinforcement Learning (MARL) discovers policies that maximize reward but do
not have safety guarantees during the learning and deployment phases. Although shielding …

In this work, we study verification and synthesis problems for safety specifications over
unknown discrete-time stochastic systems. When a model of the system is available, barrier …

Formal synthesis of controllers for stochastic control systems with unknown models is a
challenging problem. In this paper, we focus on safety controller synthesis for nonlinear …