Intelligent vehicles (IVs) have gained worldwide attention due to their increased
convenience, safety advantages, and potential commercial value. Despite predictions of …

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 …

Lagrangian methods are widely used algorithms for constrained optimization problems, but
their learning dynamics exhibit oscillations and overshoot which, when applied to safe …

Safety remains a central obstacle preventing widespread use of RL in the real world:
learning new tasks in uncertain environments requires extensive exploration, but safety …

Many potential applications of reinforcement learning (RL) require guarantees that the agent
will perform well in the face of disturbances to the dynamics or reward function. In this paper …

Reinforcement Learning (RL) algorithms have had tremendous success in simulated
domains. These algorithms, however, often cannot be directly applied to physical systems …

Safe reinforcement learning (RL) trains a constraint satisfaction policy by interacting with the
environment. We aim to tackle a more challenging problem: learning a safe policy from an …

Learning-based methods have been successful in solving complex control tasks without
significant prior knowledge about the system. However, these methods typically do not …