Science

Researchers have developed a hierarchical system that allows robots to better understand and predict the outcomes of complex actions, significantly improving long-term task planning.

Researchers have developed a novel framework that combines neural reasoning with deterministic validation to create more accurate and reliable autonomous simulations of complex fluid flows.

Researchers have unveiled ABot-M0, a framework that unifies diverse robotic datasets and employs a novel learning technique to enable more general and adaptable robotic manipulation skills.

A new generation of clinical decision support systems, powered by artificial intelligence, is showing promise in improving the accuracy and efficiency of diabetes care.

A new framework tackles the challenges of transferring robot skills from simulation to the real world, boosting performance on complex tasks like garment manipulation.

A new method efficiently reconstructs complex chemical reaction networks directly from experimental data, offering a powerful tool for systems biology.

New research demonstrates a framework for optimizing proactive agents to not only achieve goals but also minimize disruption and maximize user engagement.

New research explores how to make the inner workings of machine learning models accessible to everyone, without requiring programming expertise.

New research demonstrates that artificial intelligence can accurately anticipate human actions and their timing with remarkably little training data.

A novel approach combines statistical analysis with agent-based modeling to accurately forecast how large language models will perform on diverse tasks.