Science

CAVER autonomously builds an understanding of the physical world through audiovisual exploration, opening doors for more adaptable and intelligent robotic systems.

A new multi-agent system tackles complex scientific problems by coordinating specialized AI to achieve human-level reasoning across diverse domains.

A hierarchical vision-language model empowers robots to intelligently select and execute complex assembly tasks from natural language commands.

As artificial intelligence rapidly advances, national defense strategies must adapt to a landscape of unprecedented technological disruption.

A new framework combines vision and tactile sensing to enable robots to reliably track and manipulate complex, moving objects.

A new report explores how artificial intelligence can enhance—not replace—meaningful interaction in education.

This research introduces a method for robots to efficiently learn and represent how objects relate to each other, improving their ability to manipulate the world.

Moving beyond explaining AI decisions, this review explores how to create inherently interpretable systems.

A new model reveals a surprisingly simple principle driving collective behavior across diverse systems, offering insights for building more resilient and adaptable robotic swarms.

A new functional architecture aims to prevent errors and ensure reliable results in AI systems designed to automate scientific discovery.