A soft robot could help doctors diagnose and treat disorders of the small intestine
MIT engineers designed a robotic enteroscope that can inch through the hard-to-reach small intestine, making biopsies and drug delivery easier.
READ WIDELY. SEE CONNECTIONS.
Good ideas don’t stay in one category. Read across them.
Browser storage is unavailable. Saving and remembered preferences are disabled; you can still read every article.
THE STREAM
Science · Publishers: MIT News
A mix of topics and publishers, with recent articles first within each. Curated background stays in the stream; news uses a 90-day window. Missing dates are shown explicitly. Older collected articles remain available in Saved and Connections.
MIT engineers designed a robotic enteroscope that can inch through the hard-to-reach small intestine, making biopsies and drug delivery easier.
The modular approach could be used to erect emergency shelters or fabricate aerospace components more quickly and with less material than some techniques.
SanaHeal has developed a biologically compatible adhesive that sticks to wet tissues to simplify a variety of surgeries.
This new method, called “SANDO,” could help disaster-response robots navigate dangerous, unknown environments.
Associate Professor Cathy Wu uses reinforcement learning to help map out improvements to transportation and other multifaceted systems.
MIT engineers’ new aquabot offers a way to design small, efficient “biohybrid” robots.
The autonomous system could speed up testing of high-tech materials for applications such as solar cells, sensors, video displays, and quantum technologies.
Atlas Building Composites is commercializing MIT research to turn plastic waste into parts for buildings and other infrastructure.
The “HardFlow” algorithm could help generative AI models produce high-quality outputs that obey strict requirements when “pretty close” doesn’t cut it.
The work is part of a multiyear effort to develop a distributed sensor network for relaying data collected from this critical region.
A new method, called CW-Net, translates the reasoning process of an autonomous vehicle’s AI system into understandable concepts that explain its behavior.
The new, more intuitive system could speed up the training process for excavator operators.