In recent years, the development of robotics and artificial intelligence (AI) systems has been
nothing short of remarkable. As these systems continue to evolve, they are being utilized in …

End-to-End driving is a promising paradigm as it circumvents the drawbacks associated with
modular systems, such as their overwhelming complexity and propensity for error …

Driving safely requires multiple capabilities from human and intelligent agents, such as the
generalizability to unseen environments, the safety awareness of the surrounding traffic, and …

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

Imitation learning from human demonstrations can teach robots complex manipulation skills,
but is time-consuming and labor intensive. In contrast, Task and Motion Planning (TAMP) …

Reinforcement learning (RL) is a promising approach in unmanned ground vehicles (UGVs)
applications, but limited computing resource makes it challenging to deploy a well-behaved …

Learning from active human involvement enables the human subject to actively intervene
and demonstrate to the AI agent during training. The interaction and corrective feedback …

With the rapid growth of computing powers and recent advances in deep learning, we have
witnessed impressive demonstrations of novel robot capabilities in research settings …

The learning from intervention (LfI) approach has been proven effective in improving the
performance of RL algorithms; nevertheless, existing methodologies in this domain tend to …

Despite the substantial advancements in reinforcement learning (RL) in recent years,
ensuring trustworthiness remains a formidable challenge when applying this technology to …