Forbes reporter Trond Arne Undheim spotlights the “Manufacturing at MIT Symposium: 2022 and Beyond” conference. “MIT appears to be renewing its manufacturing research and innovation efforts at a pivotal time, with a four-fold focus on technology, workforce development, policy efforts and innovation,” writes Undheim.
MIT scientists have created a new tool that can improve robotic wearables, reports Danica D’Souza for Mashable. “The tool provides a pipeline for digital creating pneumatic actuators – devices that power motion with compressed air in many wearables and robotics,” writes D’Souza.
CSAIL researchers have developed a robotic glove that utilizes pneumatic actuation to serve as an assistive wearable, reports Brian Heater for TechCrunch. “Soft pneumatic actuators are intrinsically compliant and flexible, and combined with intelligent materials, have become the backbone of many robots and assistive technologies – and rapid fabrication with our design tool can hopefully increase ease and ubiquity,” says graduate student Yiyue Luo.
Prof. Jessika Trancik speaks with Wall Street Journal reporter Nidhi Subbaraman about the dramatic drops in costs to manufacture and sell renewable technologies. Subbaraman notes that Trancik’s research shows that “the steep drop in solar and lithium-ion battery technology was enabled by market expansion policies as well as investment in research and development by governments and the private sector.”
The MIT AI Hardware Program seeks to bring together researchers from academia and industry to “examine each step of designing and manufacturing the hardware behind AI-powered technologies,” reports Emily Bamforth for EdScoop. “This program is about accelerating the development of new hardware to implement AI algorithms so we can do justice to the capabilities that computer scientists are developing,” explains Prof. Jesús del Alamo.
MIT researchers have developed reconfigurable, self-assembling robotic cubes embedded with electromagnets that allow the robots to easily change shape, reports John Koetsier for Forbes. “If each of those cubes can pivot with respect to their neighbors you can actually reconfigure your first 3D structure into any other arbitrary 3D structure,” explains graduate student Martin Nisser.
The MIT AI Hardware Program is aimed at bringing together academia and industry to develop energy-optimized machine-learning and quantum-computing systems, reports Katyanna Quach for The Register. “As progress in algorithms and data sets continues at a brisk pace, hardware must keep up or the promise of AI will not be realized,” explains Professor Jesús del Alamo. “That is why it is critically important that research takes place on AI hardware."
MIT researchers have utilized a new reinforcement learning technique to successfully train their mini cheetah robot into hitting its fastest speed ever, reports Matt Simon for Wired. “Rather than a human prescribing exactly how the robot should walk, the robot learns from a simulator and experience to essentially achieve the ability to run both forward and backward, and turn – very, very quickly,” says PhD student Gabriel Margolis.
MIT researchers have created a new computer algorithm that has allowed the mini cheetah to maximize its speed across varying types of terrain, reports Shi En Kim for Popular Science. “What we are interested in is, given the robotic hardware, how fast can [a robot] go?” says Prof. Pulkit Agrawal. “We didn’t want to constrain the robot in arbitrary ways.”
MIT researchers have used a new reinforcement learning system to teach robots how to acclimate to complex landscapes at high speeds, reports Emmett Smith for Mashable. “After hours of simulation training, MIT’s mini-cheetah robot broke a record with its fastest run yet,” writes Smith.
CSAIL researchers developed a new machine learning system to teach the MIT mini cheetah to run, reports James Vincent for The Verge. “Using reinforcement learning, they were able to achieve a new top-speed for the robot of 3.9m/s, or roughly 8.7mph,” writes Vincent.
Gizmodo reporter Andrew Liszewski writes that CSAIL researchers developed a new AI system to teach the MIT mini cheetah how to adapt its gait, allowing it to learn to run. Using AI and simulations, “in just three hours’ time, the robot experienced 100 days worth of virtual adventures over a diverse variety of terrains,” writes Liszewski, “and learned countless new techniques for modifying its gait so that it can still effectively loco-mote from point A to point B no matter what might be underfoot.”
Popular Science reporter Tatyana Woodall writes that CSAIL researchers have developed electromagnetic bot blocks that can reconfigure into various shapes and could potentially help astronauts build in space. “The electromagnetic lining of the 3D printed frames allows cubes to seamlessly attract, repel, or even turn themselves off,” writes Wood. “One cube takes a little over an hour to make, and only costs 60 cents.”
Writing for The Wall Street Journal, Prof. Yossi Sheffi notes that “just-in-time” (JIT) supply chains can help improve product quality and manufacturing processes, leading to reduced inefficiency. JIT “reinforces resilience because it strengthens the relationships along the supply chain between companies, their suppliers and customers,” writes Sheffi. “Close relationships allow companies to react collaboratively to supply-chain disruptions.”
A team of scientists from MIT and Facebook have created a new object tagging system called InfraredTags, reports Charlotte Hu for Popular Science. “InfraredTags uses infrared light-based barcodes and QR codes that embedded permanently into the bodies of 3D printed objects,” reports Hu.