Science

Researchers have developed a novel motion planning system that allows robots to navigate complex home environments with increased speed, safety, and reliability.

A new study examines how well we can understand the reasoning behind artificial intelligence systems when they analyze and interpret works of art.

New research explores how combining the power of artificial intelligence with established learning principles can create more engaging and effective educational experiences.

Researchers have developed a new AI framework that accurately predicts the arrangement of molecules in organic crystals, a critical step for designing advanced materials.

A new reinforcement learning framework enables humanoid robots to acquire versatile motor skills by combining reference guidance with goal-conditioned learning.

A new framework empowers robots to understand and perform complex physical tasks in three dimensions by leveraging the power of large language models and multi-agent reasoning.

Researchers have developed a unified model that allows robots to better understand complex instructions and perform intricate tasks by combining visual perception, language understanding, and action planning.

New research demonstrates how incorporating models of others’ preferences into planning algorithms can foster cooperation and unlock more nuanced interactions between artificial agents.

A new systematic review explores the emerging concept of ‘agentic skills’ and how reusable procedural knowledge is key to unlocking the next generation of powerful and reliable AI agents.

New research demonstrates that standard reinforcement learning techniques, when carefully tuned, can reliably transfer policies learned in simulation to physical robots for stable and efficient online learning.