Reinforcement learning (RL) has emerged as a dynamic and transformative paradigm in
artificial intelligence, offering the promise of intelligent decision-making in complex and …

Fine manipulation tasks, such as threading cable ties or slotting a battery, are notoriously
difficult for robots because they require precision, careful coordination of contact forces, and …

Large Language Models (LLMs) have excelled as high-level semantic planners for
sequential decision-making tasks. However, harnessing them to learn complex low-level …

Large sequence models (SM) such as GPT series and BERT have displayed outstanding
performance and generalization capabilities in natural language process, vision and …

Artificial intelligence (AI) systems possess significant potential to drive societal progress.
However, their deployment often faces obstacles due to substantial safety concerns. Safe …

Humans intuitively understand that inanimate objects do not move by themselves, but that
state changes are typically caused by human manipulation (eg, the opening of a book). This …

Imitation learning provides an efficient way to teach robots dexterous skills; however,
learning complex skills robustly and generalizablely usually consumes large amounts of …

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 …

The necessity for cooperation among intelligent machines has popularised cooperative multi-
agent reinforcement learning (MARL) in AI research. However, many research endeavours …

Recent work has shown promising results for learning end-to-end robot policies using
imitation learning. In this work we address the question of how far can we push imitation …