Helping robots fit into the human workplace
Founded by Anthony Jules ’92, SM ’92 and Professor Emeritus Rodney Brooks, Robust AI’s robots are designed to collaborate with humans in factories and warehouses.
Founded by Anthony Jules ’92, SM ’92 and Professor Emeritus Rodney Brooks, Robust AI’s robots are designed to collaborate with humans in factories and warehouses.
The new approach, known as FLEX, could lead to more intuitive, interactive household and assistive robots.
A new system identifies and corrects unreliable object descriptions stored in a robot’s memory, improving the robot’s ability to interact with its environment.
Members of the student-run MIT Combat Robotics Club learn through destruction.
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.
The “HardFlow” algorithm could help generative AI models produce high-quality outputs that obey strict requirements when “pretty close” doesn’t cut it.
Atlas Building Composites is commercializing MIT research to turn plastic waste into parts for buildings and other infrastructure.
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.