The method uses language-based inputs instead of costly visual data to direct a robot through a multistep navigation task.

DenseAV, developed at MIT, learns to parse and understand the meaning of language just by watching videos of people talking, with potential applications in multimedia search, language learning, and robotics.

MIT CSAIL’s frugal deep-learning model infers the hidden physical properties of objects, then adapts to find the most stable grasps for robots in unstructured environments like homes and fulfillment centers.

With generative AI models, researchers combined robotics data from different sources to help robots learn better.

A new approach could streamline virtual training processes or aid clinicians in reviewing diagnostic videos.

“Alchemist” system adjusts the material attributes of specific objects within images to potentially modify video game models to fit different environments, fine-tune VFX, and diversify robotic training.

MIT CSAIL researchers enhance robotic precision with sophisticated tactile sensors in the palm and agile fingers, setting the stage for improvements in human-robot interaction and prosthetic technology.

The Trustnet browser extension lets individuals assess the accuracy of any content on any website.

A new technique that can automatically classify phases of physical systems could help scientists investigate novel materials.

A new “consensus game,” developed by MIT CSAIL researchers, elevates AI’s text comprehension and generation skills.