New research demonstrates that training robots with a wider range of data-including visual, linguistic, and cross-robot experiences-drastically improves their ability to generalize to new tasks.
A new framework combines the power of computational pathology with clinical data to accelerate the identification and validation of meaningful biomarkers.
Researchers have developed a deep learning architecture that enables socially assistive robots to proactively understand and help with multiple daily tasks, even those they haven’t seen before.
A new multi-agent AI framework is streamlining the process of polymer research, from predicting material properties to generating novel designs.
As supernumerary robotic limbs move closer to everyday use, establishing clear spatial boundaries is critical for safe, intuitive, and comfortable human-robot interaction.
A new system, QUASAR, is demonstrating the potential for large language models to autonomously design and execute complex computational chemistry experiments, paving the way for accelerated scientific discovery.
A new control architecture seamlessly integrates macro and micro manipulation, offering improved force control and performance in complex assembly tasks.
Strategic prompt design is proving to be a powerful technique for unlocking the full potential of artificial intelligence across diverse data science applications.
As robots become increasingly autonomous, ensuring their software is rigorously tested is paramount, and this review examines the current state of robotics testing practices.
Effective AI oversight demands a fundamental shift from debating what regulations are needed to building the legal systems that will actually deliver them.