Model-based reinforcement learning has been widely studied for controller synthesis in
cyber-physical systems (CPSs). In particular, for safety-critical CPSs, it is important to …

Recent advances in constrained reinforcement learning (RL) have endowed reinforcement
learning with certain safety guarantees. However, deploying existing constrained RL …

Neural networks (NNs) playing the role of controllers have demonstrated impressive
empirical performance on challenging control problems. However, the potential adoption of …

Ensuring safety in dynamic multi-agent systems is challenging due to limited information
about the other agents. Control Barrier Functions (CBFs) are showing promise for safety …

Hybrid dynamical systems are ubiquitous as practical robotic applications often involve both
continuous states and discrete switchings. Safety is a primary concern for hybrid robotic …

Integrating learning-based techniques, especially reinforcement learning, into robotics is
promising for solving complex problems in unstructured environments. However, most …

Reinforcement learning is challenging in delayed scenarios, a common real-world situation
where observations and interactions occur with delays. State-of-the-art (SOTA) state …

The recent development of cloud computing and edge computing shows great promise for
the Connected and Automated Vehicle (CAV), by enabling CAVs to offload their massive on …

Constrained reinforcement learning (RL) algorithms have attracted extensive attentions
nowadays to tackle sequential decision-making problems that contain constraints defined …

Recently, the autonomous driving technology is at a critical phase evolving from typical,
closed scenarios to largescale, open driving scenarios, which is challenged by the diversity …