IDSS hosts inaugural Learning for Dynamics and Control conference
L4DC explored an emerging scientific area at the intersection of real-time physical data, machine learning, control theory, and optimization.
L4DC explored an emerging scientific area at the intersection of real-time physical data, machine learning, control theory, and optimization.
At the annual MIT Ship Design and Technology Symposium, naval construction and engineering students presented their work on real-life naval design projects.
New approach quickly finds hidden objects in dense point clouds, for use in driverless cars or work spaces with robotic assistants.
Engineer and historian discusses how the MIT Schwarzman College of Computing might integrate technical and humanistic research and education.
Fleet of “roboats” could collect garbage or self-assemble into floating structures in Amsterdam’s many canals.
Autonomous control system “learns” to use simple maps and image data to navigate new, complex routes.
The DiCarlo lab finds that a recurrent architecture helps both artificial intelligence and our brains to better identify objects.
Collaboration will help MIT students become leaders in autonomous machines.
Through MIT Professional Education’s Advanced Study Program, Ernie Ho found the tools — and the community — he needed to realize his vision and launch his career.
Senior and Marshall Scholar Crystal Winston pursues her vision of a world where cars aren’t limited to roads.
Ninth annual Research Speed Dating event fosters intradepartmental collaboration and facilitates discussion of future efforts to solve global issues.
On-chip system that detects signals at sub-terahertz wavelengths could help steer driverless cars through fog and dust.
MIT associate professor of aeronautics and astronautics describes the seamless flow of people, things, and materials.
Algorithm could help autonomous underwater vehicles explore risky but scientifically-rewarding environments.
Model identifies instances when autonomous systems have learned from examples that may cause dangerous errors in the real world.