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 …

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 …

Prompt-based learning has emerged as a successful paradigm in natural language
processing, where a single general-purpose language model can be instructed to perform …

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 …

Prompt-based learning has emerged as a successful paradigm in natural language
processing, where a single general-purpose language model can be instructed to perform …

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 …

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 …

Ensuring safety and achieving human-level driving performance remain challenges for
autonomous vehicles, especially in safety-critical situations. As a key component of artificial …

Despite the tremendous success of Reinforcement Learning (RL) algorithms in simulation
environments, applying RL to real-world applications still faces many challenges. A major …

Robotic-assisted surgery is now well established in clinical practice and has become the
gold-standard clinical treatment option for several clinical indications. The field of robotic …