This paper provides a comprehensive review of recent challenges and results in the field of
generative AI with application to mobile telecommunications networks. The objective is to …

Learning from visual data opens the potential to accrue a large range of manipulation
behaviors by leveraging human demonstrations without specifying each of them mathe …

Generalizing policies across different domains with dynamics mismatch poses a significant
challenge in reinforcement learning. For example, a robot learns the policy in a simulator …

The burgeoning fields of robot learning and embodied AI have triggered an increasing
demand for large quantities of data. However, collecting sufficient unbiased data from the …

Human motion driven control (HMDC) is an effective approach for generating natural and
compelling robot motions while preserving high-level semantics. However, establishing the …

Learning good feature representations is important for deep reinforcement learning (RL).
However, with limited experience, RL often suffers from data inefficiency for training. For un …

In small batch and customized production, human-robot collaborative assembly (HRCA) is
an important method to deal with the production demand of new-energy vehicles, which …

Video shadow detection aims to generate consistent shadow predictions among video
frames. However, the current approaches suffer from inconsistent shadow predictions across …

Comparing unpaired samples of a distribution or population taken at different points in time
is a fundamental task in many application domains where measuring populations is …

The implementation of robotic reinforcement learning is hampered by problems such as an
unspecified reward function and high training costs. Many previous works have used cross …