Eureka Robotics, an automation company based in Singapore, has developed their products based on research from MIT and Nanyang Technological University, reports Catherine Shu for TechCrunch. “It [Eureka Robotics] focuses on robotic software and systems to automate tasks that require High Accuracy and high Agility (HAHA),” writes Shu. “Its robots are used for precision handling, assembly, inspection, drilling and other tasks.”
Robots constructed by 32 students competed in the annual 2.007 Robot Competition, which was held in person for the first time in three years, reports Lisa Gresci for 7 News. “The atmosphere is absolutely electric,” explains third year student Joshua Rohrbaugh. “It’s really amazing we can celebrate this kind of academic competition in this kind of way. It’s almost like a sporting event and that gets me hyped up.”
CSAIL researchers have developed a robotic arm equipped with a sensorized soft brush that can untangle hair, reports Douglas Belkin for The Wall Street Journal. “The laboratory brush is outfitted with sensors that detect tension," writes Belkin. “That tension reads as pain and is used to determine whether to use long strokes or shorter ones.”
TechCrunch reporter Devin Coldewey spotlights how MIT researchers have developed a machine learning technique for proposing new molecules for drug discovery that ensures suggested molecules can be synthesized in a lab. Coldewey also features how MIT scientists created a new method aimed at teaching robots how to interact with everyday objects.
Boston Globe reporter Robert Weisman spotlights how researchers at the MIT AgeLab are “designing prototypes of ‘smart homes’ for older residents, equipped with social robots, voice-activated speakers that give medication reminders, motion sensors embedded in carpets to detect falls, and intelligent doorbells that double as security cameras.”
TechCrunch reporter Brian Heater spotlights new MIT robotics research, including a team of CSAIL researchers “working on a system that utilizes a robotic arm to help people get dressed.” Heater notes that the “issue is one of robotic vision — specifically finding a method to give the system a better view of the human arm it’s working to dress.”
CSAIL researchers have developed a new technique that could enable robots to handle squishy objects like pizza dough, reports Brian Heater for TechCrunch. “The system is separated into a two-step process, in which the robot must first determine the task and then execute it using a tool like a rolling pin,” writes Heater. “The system, DiffSkill, involves teaching robots complex tasks in simulations.”
Boston Globe reporter Michael Blanding spotlights Prof. Hugh Herr’s work with Dr. Matthew Carty in developing a new amputation surgery called agonist-antagonist myoneural interface (AMI) procedure, which reconnects muscles to amplify electrical signals sent along the nerves. “My dream as a scientist is that a person with an arm amputation could play a Beethoven piece at normal speeds and dexterity – and for legs, that a person could dance ballet,” says Herr.
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.”
Prof. Julie Shah speaks with The Economist about her work developing systems to help robots operate safely and efficiently with humans. “Robots need to see us as more than just an obstacle to maneuver around,” says Shah. “They need to work with us and anticipate what we need.”
The Economist highlights new work by MIT researchers investigating the impact of automation on the labor market. A study by graduate student Joonas Tuhkuri finds that at Finnish firms “adoption of advanced technologies led to increases in hiring.” Meanwhile a new book by Profs. David Autor, David Mindell and Elisabeth Reynolds concludes that “even if robots do not create widespread joblessness, they may have helped create an environment where the rewards are ‘skewed towards the top.’”