The inclusion of physical information in machine learning frameworks has revolutionized
many application areas. This involves enhancing the learning process by incorporating …

On-ramp merging is a challenging task for autonomous vehicles (AVs), especially in mixed
traffic where AVs coexist with human-driven vehicles (HDVs). In this paper, we formulate the …

An exploration task can be performed by a team of mobile robots more efficiently than
human counterparts. They can access and give live updates for hard-to-reach areas such as …

Applying reinforcement learning (RL) algorithms to control systems design remains a
challenging task due to the potential unsafe exploration and the low sample efficiency. In …

Deep reinforcement learning has gathered much attention recently. Impressive results were
achieved in activities as diverse as autonomous driving, game playing, molecular …

We consider the problem of designing distributed collision-avoidance multi-agent control in
large-scale environments with potentially moving obstacles, where a large number of agents …

Distributed, scalable, and safe control of large-scale multi-agent systems is a challenging
problem. In this paper, we design a distributed framework for safe multi-agent control in …

The role of natural language constraints in Safe Multi-agent Reinforcement Learning
(MARL) is crucial, yet often overlooked. While Safe MARL has vast potential, especially in …

In this paper, a data-driven conflict-aware safe reinforcement learning (CAS-RL) algorithm is
presented for control of autonomous systems. Existing safe RL results with predefined …

An increasing number of Autonomous Mobile Robots (AMRs) are used in warehouses and
factories in recent years. The risk of some of the AMRs being out of control is surging …