Building general-purpose robots that operate seamlessly in any environment, with any
object, and utilizing various skills to complete diverse tasks has been a long-standing goal in …

As autonomous driving technology matures, the safety and robustness of its key
components, including trajectory prediction is vital. Although real-world datasets such as …

While the exploration for embodied AI has spanned multiple decades, it remains a persistent
challenge to endow agents with human-level intelligence, including perception, learning …

Reinforcement Learning is a promising tool for learning complex policies even in fast-
moving and object-interactive domains where human teleoperation or hard-coded policies …

Deep Reinforcement Learning (RL) has shown remarkable success in robotics with complex
and heterogeneous dynamics. However, its vulnerability to unknown disturbances and …

Continual reinforcement learning aims to sequentially learn a variety of tasks, retaining the
ability to perform previously encountered tasks while simultaneously developing new …

Achieving stable and reliable autonomous driving in complex traffic environments while
ensuring safety under unpredictable conditions is a critical challenge in autonomous driving …

A world model provides an agent with a representation of its environment, enabling it to
predict the causal consequences of its actions. Current world models typically cannot …

Offline model-based reinforcement learning (MBRL) enhances data efficiency by utilizing
pre-collected datasets to learn models and policies, especially in scenarios where …

Sim-to-real transfer remains a significant challenge in robotics due to the discrepancies
between simulated and real-world dynamics. Traditional methods like Domain …