MIT CSAIL scientists created an algorithm to solve one of the hardest tasks in computer vision: assigning a label to every pixel in the world, without human supervision.

A new machine-learning system may someday help driverless cars predict the next moves of nearby drivers, cyclists, and pedestrians in real-time.

MIT engineers Edward Adelson and Sandra Liu duo develop a robotic gripper with rich sensory capabilities.

A new robotic manipulation course provides a broad survey of state-of-the-art robotics, equipping students to identify and solve the field’s biggest problems.

A new technique compares the reasoning of a machine-learning model to that of a human, so the user can see patterns in the model’s behavior.

MIT researchers design a robot that has a trick or two up its sleeve.

MIT researchers performed a large-scale, high resolution study of the cells in human breast milk, allowing them to track how these cells change over time in nursing mothers.

An efficient machine-learning method uses chemical knowledge to create a learnable grammar with production rules to build synthesizable monomers and polymers.

Researchers from MIT and elsewhere have created a framework that could enable a robot to effectively complete complex manipulation tasks with deformable objects, like dough or cloth, that require many tools and take a long time to complete.

A new technique for removing bias in datasets can enable machine-learning models to make loan approval predictions that are both fair and accurate.