Drones navigate unseen environments with liquid neural networks
MIT researchers exhibit a new advancement in autonomous drone navigation, using brain-inspired liquid neural networks that excel in out-of-distribution scenarios.
MIT researchers exhibit a new advancement in autonomous drone navigation, using brain-inspired liquid neural networks that excel in out-of-distribution scenarios.
Robotic arm equipped with a hairbrush helps with brushing tasks and could be an asset in assistive-care settings.
Assistant Professor Cathy Wu aims to help autonomous vehicles fulfill their promise by better understanding how to integrate them into the transportation system.
Robotic gripper with soft sensitive fingers developed at MIT can handle cables with unprecedented dexterity.
In a pair of papers from MIT CSAIL, two teams enable better sense and perception for soft robotic grippers.
CSAIL's Conduct-A-Bot system uses muscle signals to cue a drone’s movement, enabling more natural human-robot communication.
Weather’s a problem for autonomous cars. MIT’s new system shows promise by using “ground-penetrating radar” instead of cameras or lasers.
Developed at MIT’s Computer Science and Artificial Intelligence Laboratory, robots can self-assemble to form various structures with applications including inspection.
CSAIL system can mirror a user's motions and follow nonverbal commands by monitoring arm muscles.
Gripper device inspired by “origami magic ball” can grasp wide array of delicate and heavy objects.
Shape-shifting device from CSAIL can walk, roll, sail, and glide using recyclable exoskeletons.
Device provides information from a 3-D camera, via vibrating motors and a Braille interface.
System directs camera-equipped drones to maintain framing of an aerial shot.
Technique shrinks data sets while preserving their fundamental mathematical relationships.
Self-driving scooter demonstrated at MIT complements autonomous golf carts and city cars.