MIT researchers have developed “a robotic system that can rotate different types of fruit and vegetable using its fingers on one hand, while the other arm is made to peel,” reports Alex Wilkins for New Scientist. “These additional steps of doing rotation are something which is very straightforward to humans, we don’t even think about it,” Prof. Pulkit Agrawal. “But for a robot, this becomes challenging.”
Researchers at MIT have developed a new method for “training home robots in simulation,” reports Brain Heater for TechCrunch. “Simulation has become a bedrock element of robot training in recent decades,” explains Heater. “It allows robots to try and fail at tasks thousands — or even millions — of times in the same amount of time it would take to do it once in the real world.”
Ivan Casadevantre MS '15 and Hasier Larrea MS '15 co-founded ORI Living – a furniture company that uses electromechanics to develop furniture systems designed for space efficiency. “You have to make those small spaces feel and act as if they were much larger,” says Larrea. “And that’s when we started thinking about robotics, thinking about engineering, and how we bring all those technologies to make it possible to live large in a smaller footprint.”
Using multimodal sensing and a soft robotic manipulator, MIT scientists have developed an automated system, called RoboGrocery, that can pack groceries of different sizes and weights, reports Brian Heater for TechCrunch. Heater explains that as the soft robotic gripper touches an item, “pressure sensors in the fingers determine that they are, in fact, delicate and therefore should not go at the bottom of the bag — something many of us no doubt learned the hard way. Next, it notes that the soup can is a more rigid structure and sticks it in the bottom of the bag.”
Researchers from MIT have developed RoboGrocery, a soft robotic system that “can determine how to pack a grocery item based on its weight, size and shape without causing damage to the item,” reports Jennifer Kite-Powell for Forbes. “This is more than just automation—it's a paradigm shift that enhances precision, reduces waste and adapts seamlessly to the diverse needs of modern retail logistics,” says Prof. Daniela Rus, director of CSAIL.
MIT scientists have developed a “four-fingered robotic hand which is capable of rotating balls and toys in any direction and orientation,” reports Maisie Lillywhite for BBC News. “The improvement in dexterity could have significant implications for automating tasks such as handling goods for supermarkets or sorting through waste for recycling,” Lillywhite writes.
DragonBot, an AI tool developed by the MIT Personal Robotics Group, “is designed to teach children empathy and social skills,” reports Neil Sahota for Forbes. “These interactions, while mediated through technology, are invaluable in nurturing the social and emotional growth of children,” writes Sahota.
Nature reporter Andrew Robinson reviews “The Heart and the Chip,” a new book by Prof. Daniela Rus and science writer Gregory Mone. The book “focuses on combining human and robotic strengths to pair ‘the heart and the chip’ in three interlinked fields: robotics, artificial intelligence and machine learning,” explains Robinson.
MIT researchers have developed a wearable backpack with spider-like limbs to help astronauts maintain stability in space, reports Jess Thomson for Newsweek. The new technology, called Supernumerary Robotic Limbs (SuperLimbs), “could be crucial in future missions to the moon, where gravity is only a sixth of that on Earth and astronauts may struggle to clamber up again after a fall due to their unwieldy space suits,” explains Thomson.
Researchers at MIT have developed SuperLimbs, a pair of wearable robotic limbs that “can physically support an astronaut and lift them back on their feet after a fall,” reports Brain Heater for TechCrunch. “The system, which is still in the prototype phase, responds directly to the wearer’s feedback,” writes Heater. “When sitting or lying down, it offers a constructive support to help them get back up while expending less energy — every extra bit helps in a situation like this.”
MIT researchers have developed a new type of spring-like device that uses a flexible element to help power biohybrid robots, reports Brian Heater for TechCrunch. “The muscle fiber/flexure system can be applied to various kinds of robots in different sizes,” Heater writes, adding that the researchers are, “focused on creating extremely small robots that could one day operate inside the body to perform minimally invasive procedures.”
MIT researchers have developed a new technique that uses a large language model to allow robots to self-correct after making a mistake, reports Brian Heater for TechCrunch. “Researchers behind the study note that while imitation learning (learning to do a task through observation) is popular in the world of home robotics, it often can’t account for the countless small environmental variations that can interfere with regular operation, thus requiring a system to restart from square one,” writes Heater. “The new research addresses this, in part, by breaking demonstrations into smaller subsets."
Prof. Pulkit Agrawal and graduate student Gabriel Margolis speak with The Economist’s Babbage podcast about the simulation research and technology used in developing intelligent machines. “Simulation is a digital twin of reality,” says Agrawal. “But simulation still doesn’t have data, it is a digital twin of the environment. So, what we do is something called reinforcement learning which is learning by trial and error which means that we can try out many different combinations.”
Reflex Robotics, a startup co-founded by several MIT alumni, has developed a remotely-operated humanoid robot capable of handling tasks such as grabbing an item off a shelf, reports Brian Heater for TechCrunch. The robot’s hardware “is an in-house design, featuring a ‘torso’ mounted to a base that allows the arms and sensors to dynamically move up and down,” explains Heater. “It makes for a surprisingly dexterous robot that can access shelves at a variety of heights, while maneuvering tight spaces. The system has a wheeled base, which is perfectly effective for navigating these kinds of layouts.”
Prof. Daniela Rus, director of CSAIL, speaks with The Economist’s Babbage podcast about the history and future of artificial neural networks and their role in large language models. “The early artificial neuron was a very simple mathematical model,” says Rus. “The computation was discrete and very simple, essentially a step function. You’re either above or below a value.”