With the development of large language models (LLMs), striking a balance between the
performance and safety of AI systems has never been more critical. However, the inherent …

General world models represent a crucial pathway toward achieving Artificial General
Intelligence (AGI), serving as the cornerstone for various applications ranging from virtual …

In the rapidly evolving landscape of autonomous driving, the capability to accurately predict
future events and assess their implications is paramount for both safety and efficiency …

Federated semi-supervised learning (FSSL) has emerged as a powerful paradigm for
collaboratively training machine learning models using distributed data with label deficiency …

In the field of safe reinforcement learning (RL), finding a balance between satisfying safety
constraints and optimizing reward performance presents a significant challenge. A key …

Offline safe reinforcement learning (RL) aims to train a policy that satisfies constraints using
a pre-collected dataset. Most current methods struggle with the mismatch between imperfect …

Among approaches for provably safe reinforcement learning, Model Predictive Shielding
(MPS) has proven effective at complex tasks in continuous, high-dimensional state spaces …

Model-based Reinforcement Learning (RL) has shown its high training efficiency and
capability of handling high-dimensional tasks. Regarding safety issues, safe model-based …

Shielding is a popular technique for achieving safe reinforcement learning (RL). However,
classical shielding approaches come with quite restrictive assumptions making them difficult …

The use of autonomous robots is increasing in agriculture; however, their development is
still slow as each task usually needs a specific hard-coded behaviour. Hard coding of all …