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

Abstract Deep Reinforcement Learning (DRL) is increasingly applied in cyber–physical
systems for automation tasks. It is important to record the developing trends in DRL's …

Offline Reinforcement learning (RL) has shown potent in many safe-critical tasks in robotics
where exploration is risky and expensive. However, it still struggles to acquire skills in …

Motion control algorithms in the presence of pedestrians are critical for the development of
safe and reliable Autonomous Vehicles (AVs). Traditional motion control algorithms rely on …

Learning contact-rich manipulation skills is essential. Such skills require the robots to
interact with the environment with feasible manipulation trajectories and suitable compliance …

Reinforcement learning (RL) has shown excellent performance in the sequential decision-
making problem, where safety in the form of state constraints is of great significance in the …

Neural networks have shown great promises in planning, control, and general decision
making for learning-enabled cyber-physical systems (LE-CPSs), especially in improving …

Efficient intersection planning is one of the most challenging tasks for an autonomous
vehicle at present. Politeness to other traffic participants and reaction to surrounding …

Safe Reinforcement Learning (RL) aims to find a policy that achieves high rewards while
satisfying cost constraints. When learning from scratch, safe RL agents tend to be overly …

Intelligent vehicles (IVs) have attracted wide attention thanks to the augmented
convenience, safety advantages, and potential commercial value. Although a few of …