Reinforcement Learning (RL) has achieved tremendous success in many complex decision-
making tasks. However, safety concerns are raised during deploying RL in real-world …

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 …

Learning-enabled control systems have demonstrated impressive empirical performance on
challenging control problems in robotics, but this performance comes at the cost of reduced …

Many safety-critical applications of neural networks, such as robotic control, require safety
guarantees. This article introduces a method for ensuring the safety of learned models for …

Safety and stability are common requirements for robotic control systems; however,
designing safe, stable controllers remains difficult for nonlinear and uncertain models. We …

The problem of dynamic locomotion over rough terrain requires both accurate foot
placement together with an emphasis on dynamic stability. Existing approaches to this …

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 …

Reinforcement Learning (RL) has achieved tremendous success in many complex decision-
making tasks. However, safety concerns are raised during deploying RL in real-world …

It is quite challenging to ensure the safety of reinforcement learning (RL) agents in an
unknown and stochastic environment under hard constraints that require the system state …

Learning-enabled control systems have demonstrated impressive empirical performance on
challenging control problems in robotics, but this performance comes at the cost of reduced …