Graduate student Anna Waldman-Brown writes for Wired about the future of automation technology and how it can impact labor dynamics in the future. “While some scholars believe that our fates are predetermined by the technologies themselves, emerging evidence indicates that we may have considerable influence over how such machines are employed within our factories and offices – if we can only figure out how to wield this power,” writes Waldman-Brown.
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.
An MIT Media Lab team led by Ariel Ekblaw, director of the Space Exploration Initiative, has developed a robotic swarm of self-assembling robotic tiles that could be used for future in-orbit construction, reports Eric Mack for CNET. “If all goes well, MIT and [Ariel] Ekblaw hope that the technology will eventually be used for geodesic dome habitats beyond Earth, microgravity concert halls and space cathedrals,” writes Mack.
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.”
Chiamaka Agbasi-Porter, the K-12 STEM outreach coordinator for Lincoln Lab, speaks with CBS Boston about her mission to help inspire young people to pursue STEM interests through the Lincoln Laboratory Radar Introduction for Student Engineers (LLRISE) program. “I think of it as a community,” said Agbasi-Porter, “we are a village that is helping our kids advance and move forward in their careers.”
New research by Professor Erik Demaine, lecturer Zachary Abel, robotics engineer Martin Demaine and their colleagues explores whether it is possible to “take any polyhedral (or flat-sided) shape that’s finite (like a cube, rather than a sphere or the endless plane) and fold it flat using creases," writes Rachel Crowell for Quanta Magazine. “By moving finite to infinite ‘conceptual’ slices, they created a procedure that, taken to its mathematical extreme, produced the flattened object they were looking for,” Crowell explains.
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.
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.”
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.”