Impact Statement: On the basis of using representative game theoretic algorithms to solve multi-agent task allocation problems, our research integrates reinforcement learning, and presenting a novel ...

At the high level, the real-time task assignment problem within a heterogeneous multi-agent system is formulated as a sequence optimization model which considers multiple constraints. At the low level ...

To make better use of given limited labels, we propose a novel object detection approach that takes advantage of both multi-task learning (MTL) and self-supervised learning (SSL ... Then these ...

A new study published in Translational Psychiatry suggests that combining virtual reality, eye tracking, head movement data, ...

EPFL researchers have discovered key "units" in large AI models that seem to be important for language, mirroring the brain's ...

🦉 OWL is a cutting-edge framework for multi-agent collaboration that pushes the boundaries of task automation, built on top of the CAMEL-AI Framework. Our vision is to revolutionize how AI agents ...

A new paper from Microsoft Research and Salesforce finds that even the most capable Large Language Models (LLMs) fall apart ...

Mixture-of-Experts (MoE) models are revolutionizing the way we scale AI. By activating only a subset of a model’s components at any given time, MoEs offer a novel approach to managing the trade-off ...

Estimating the pose of hand-held objects is a critical and challenging problem in robotics and computer vision. While leveraging multi-modal RGB and depth data is a promising solution, existing ...