A new study leverages machine learning and explainable AI to dissect the complex gene expression changes in Multiple Sclerosis, integrating data across multiple tissues and cell types.
Researchers have developed a novel control strategy enabling more precise and reliable task execution for underactuated soft robotic systems.
A new dataset reveals how people react to medical robot errors and what recovery strategies they prefer, paving the way for more intuitive and trustworthy robotic assistants.
A new human-centered tool aims to transform the laborious process of synthesizing research, shifting focus from tedious tasks to strategic knowledge discovery.
Researchers have developed a new approach allowing robots to plan and execute navigation tasks by ‘imagining’ potential routes through videos generated from simple language commands.
The discovery of a previously unknown structure in GdNiSn4 reveals limitations in current artificial intelligence models for predicting material compositions.
Researchers have developed a new framework enabling legged robots to autonomously manipulate and open a wide variety of complex, previously unseen articulated objects.
New research explores how large language models perform when asked to generate explanations – a task known as abductive reasoning – and finds they struggle more than with straightforward logical deduction.
Researchers have developed a new system for generating 3D environments specifically designed for the needs of embodied agents like robots.
New research demonstrates how transformer models can extract relationships between mathematical concepts from text, offering a pathway to more understandable AI in STEM fields.