Robotics

Maria Telleria ’08, SM’10, PhD ’13 speaks with Forbes contributor Stuart Anderson about her experience immigrating to the U.S. as a teenager, earning her PhD at MIT, and co-founding a company. “I don’t think I would have had these opportunities if I could not have come to the United States,” said Telleria. “I think it helped me grow by being exposed to two cultures. When you have had to think in two different ways, I think it makes you better understand other people and why they’re different. Coming to America has been an amazing opportunity.”

Forbes reporter Rob Toews spotlights Prof. Daniela Rus, director of CSAIL, and research affiliate Ramin Hasani and their work with liquid neural networks. “The ‘liquid’ in the name refers to the fact that the model’s weights are probabilistic rather than constant, allowing them to vary fluidly depending on the inputs the model is exposed to,” writes Toews.

Using techniques inspired by kirigami, a Japanese paper-cutting technique, MIT researchers have developed a “a novel method to manufacture plate lattices – high performance materials useful in automotive and aerospace designs,” reports Andrew Paul for Popular Science. “The kirigami-augmented plate lattices withstood three times as much force as standard aluminum corrugation designs,” writes Paul. “Such variations show immense promise for lightweight, shock-absorbing sections needed within cars, planes, and spacecraft." 

Prof. Daniela Rus, director of CSAIL, speaks with TechCrunch reporter Brain Heater about liquid neural networks and how this emerging technology could impact robotics. “The reason we started thinking about liquid networks has to do with some of the limitations of today’s AI systems,” says Rus, “which prevent them from being very effective for safety, critical systems and robotics. Most of the robotics applications are safety critical.”

Researchers at MIT have developed a soft robot that can be controlled by a weak magnetic field and travel through tiny spaces within the human body, reports Andrew Paul for Popular Science. “Because of their soft materials and relatively simple manipulation, researchers believe such mechanisms could be used in biomedical situations, such as inching through human blood vessels to deliver a drug at a precise location,” explains Paul.

Researchers at MIT have developed PIGINet (Plans, Images, Goal and Initial facts), a neural network designed to bring task and motion planning to home robotics, reports Brian Heater for Tech Crunch. “The system is largely focused on kitchen-based activities at present. It draws on simulated home environments to build plans that require interactions with various different elements of the environment, like counters, cabinets, the fridge, sinks, etc,” says Heater.

Researchers at MIT have developed a new artificial intelligence system aimed at helping autopilot avoid obstacles while maintaining a desirable flight path, reports Kyle Wiggers for TechCrunch. “Any old algorithm can propose wild changes to direction in order to not crash, but doing so while maintaining stability and not pulping anything inside is harder,” writes Wiggers.

MIT researchers have developed a new robotic gripper that is able to grasp objects using reflexes, reports Mashable. “The Robo-Gripper has proximity and contact sensors which allows it to react to surfaces near objects to better grab them. The technology may allow these machines to be used in homes or other unique, unstructured environments.”

MIT researchers have developed SoftZoo, “an open framework platform that simulated a variety of 3D model animals performing specific tasks in multiple environmental settings,” reports Andrew Paul for Popular Science. “This computational approach to co-designing the soft robot bodies and their brains (that is, their controllers) opens the door to rapidly creating customized machines that are designed for a specific task,” says CSAIL director, Prof. Daniela Rus.

Researchers at MIT have developed “SoftZoo,” a platform designed to “study the physics, look and locomotion and other aspects of different soft robot models,” reports Brian Heater for TechCrunch. “Dragonflies can perform very agile maneuvers that other flying creatures cannot complete because they have special structures on their wings that change their center of mass when they fly,” says graduate student Tsun-Hsuan Wang. “Our platform optimizes locomotion the same way a dragonfly is naturally more adept at working through its surroundings.”

TechCrunch reporter Brian Heater spotlights how MIT researchers have developed a new approach to robotic gripping that incorporates reflexes to help grasp and sort objects. “The new system is built around an arm with two multi-joint fingers,” writes Heater. “There’s a camera on the base and sensors on the tips that record feedback. The system uses that data to adjust accordingly.”

Popular Science reporter Jamie Dickman writes that using liquid neural networks, MIT researchers have “trained a drone to identify and navigate toward objects in varying environments.” Dickman notes that: “These robust networks enable the drone to adapt in real-time, even after initial training, allowing it to identify a target object despite changes in their environment.”

Researchers at MIT have developed a new type of autonomous drone that uses advanced neural networks to fly, reports Tony Ho Tran for The Daily Beast. “The new design allows the drone to make better decisions when flying through completely new environments,” writes Tran, “and could have future applications in self-driving cars, search and rescue operations, wildlife monitoring, or even diagnosing medical issues.”

Researchers at MIT have created a four-legged robot called DribbleBot, reports Caroline Goggin for WHDH. The robot “can dribble a soccer ball under the same conditions as humans, using onboard sensors to travel across different types of terrain.”

Popular Science reporter Andrew Paul spotlights how researchers from MIT CSAIL have developed a soccer-playing robot, dubbed DribbleBot, that can handle a variety of real-world terrains. “DribbleBot showcases extremely impressive strides in articulation and real-time environmental analysis. Using a combination of onboarding computing and sensing, the team’s four-legged athlete can reportedly handle gravel, grass, sand, snow, and pavement, as well as pick itself up if it falls.”