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 …

To facilitate the personalized smart manufacturing paradigm with cognitive automation
capabilities, Deep Reinforcement Learning (DRL) has attracted ever-increasing attention by …

Reinforcement learning from human feedback (RLHF) is a technique for training AI systems
to align with human goals. RLHF has emerged as the central method used to finetune state …

Deep reinforcement learning (RL) has emerged as a promising approach for autonomously
acquiring complex behaviors from low-level sensor observations. Although a large portion of …

Recent years have seen significant progress in the realm of robot autonomy, accompanied
by the expanding reach of robotic technologies. However, the emergence of new …

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 …

Safe exploration presents a major challenge in reinforcement learning (RL): when active
data collection requires deploying partially trained policies, we must ensure that these …

Electrified vehicles provide an effective solution to address the unfavorable impacts of fossil
fuel use in the transportation sector. Energy management strategy (EMS) is the core …

Safe reinforcement learning is a promising path toward applying reinforcement learning
algorithms to real-world problems, where suboptimal behaviors may lead to actual negative …

A trustworthy reinforcement learning algorithm should be competent in solving challenging
real-world problems, including {robustly} handling uncertainties, satisfying {safety} …