This rendering shows a robot being built layer-by-layer using the new process. The black spheres represent the material that the printer uses. The material is then cured by UV light, represented in blue. At the top of the image are the cameras that scan the procedure and adjust accordingly. Image: Moritz Hocher

Computer vision enables contact-free 3D printing, letting engineers print with high-performance materials they couldn’t use before.

This 3D printer can watch itself fabricate objects

MIT researchers are using generative AI models to help robots more efficiently solve complex object manipulation problems, such as packing a box with different objects. Image: Courtesy of the researchers

Researchers coaxed a family of generative AI models to work together to solve multistep robot manipulation problems.

New technique helps robots pack objects into a tight space

MIT researchers developed an AI technique that enables a robot to develop complex plans for manipulating an object using its entire hand, not just the fingertips. This model can generate effective plans in about a minute using a standard laptop. Here, a robot attempts to rotate a bucket 180 degrees. Image: Courtesy of the researchers

With a new technique, a robot can reason efficiently about moving objects using more than just its fingertips.

AI helps robots manipulate objects with their whole bodies

A machine-learning model can identify all the pixels in an image that represent a given material. Developed at MIT, the technique could someday be used in computer vision systems that help robots interact with objects in the real world. Pictured is an artist’s interpretation of the new system. Image: MIT News with iStock

This machine-learning method could assist with robotic scene understanding, image editing, or online recommendation systems.

Researchers use AI to identify similar materials in images

Computer scientists at MIT joined forces with oceanographers to develop a machine-learning model that incorporates knowledge from fluid dynamics to generate more accurate predictions about the velocities of ocean currents. This figure shows drifting buoy trajectories in the Gulf of Mexico superimposed on surface currents. The red dots mark the buoys’ positions on March 9, 2016, and the tails are 14 days long. Image: Edward Ryan and Tamay Özgökmen from the University of Miami.

A new machine-learning model makes more accurate predictions about ocean currents, which could help with tracking plastic pollution and oil spills, and aid in search and rescue.

A better way to study ocean currents

Researchers from MIT and Rice University have created a computer vision technique that leverages reflections to image the world by using them to turn glossy objects into “cameras,” enabling a user to see the world as if they were looking through the “lenses” of everyday objects like a ceramic coffee mug or a metallic paper weight. Credit: Courtesy of the researchers

Adam Zewe

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Latest Posts

This 3D printer can watch itself fabricate objects

New technique helps robots pack objects into a tight space

AI helps robots manipulate objects with their whole bodies

Researchers use AI to identify similar materials in images

A better way to study ocean currents

Using reflections to see the world from new points of view