Science

This review explores how Active Inference, implemented through reactive message passing, offers a principled architecture for creating robust and adaptable physical AI agents.

Researchers have developed a framework that combines the reasoning power of large AI models with the efficiency of smaller, edge-based systems to enhance multi-robot coordination and exploration.

A new vision proposes fully automating the cycle of experiment design, data generation, and model building to accelerate progress in understanding the mind.

As robots become increasingly integrated into our lives, researchers are exploring how to equip them with the ability to understand and respond to human emotions.

A new framework proposes harnessing the collective intelligence of AI agents to simulate scientific collaboration and accelerate discovery.

Researchers have released HortiMulti, a comprehensive dataset designed to push the boundaries of robot localization and mapping within the complex environments of agricultural polytunnels.

As generative AI models enter the realm of educational assessment, a rigorous examination of their validity is crucial to ensure fair and accurate scoring of complex student responses.

A new study reveals that how students are motivated-not just that they have motivation-profoundly impacts their adoption of generative AI tools for tasks like math and writing.

A new teleoperation framework, KUKAloha, blends human expertise with robotic precision to significantly improve the efficiency and safety of construction tasks.

Researchers have developed a streamlined, open-source system to accelerate the development of complex skills in humanoid robots, addressing key challenges in reinforcement learning and real-world deployment.