Robots

Writing for Wired, research scientist Kate Darling highlights the importance of addressing the fundamentally human behaviors that have been incorporated into AI chatbots. “Research in human-computer and human-robot interaction shows that we love to anthropomorphize—attribute humanlike qualities, behaviors, and emotions to—the nonhuman agents we interact with, especially if they mimic cues we recognize,” writes Darling. “And, thanks to recent advances in conversational AI, our machines are suddenly very skilled at one of those cues: language.”

Prof. Julie Shah speaks with Wall Street Journal reporter Lauren Weber about the implementation of automation in the work force. According to Shah, “when companies adopt automation successfully, they end up adding workers as they become more productive and fill more orders,” writes Weber. “And machines’ lack of flexibility has often resulted in what Shah calls ‘zero-sum automation,’ where gains in productivity are canceled out by the need for people to fix or reprogram robots and compensate for their drawbacks.” 

Researchers from MIT have developed, “nanoelectronics they hope can one day enter the brain and treat conditions like Alzheimer’s by monitoring some of these brain patterns,” reports Elizabeth Hlavinka for Salon. “Their device, which they call Cell Rover, serves as a sort of antenna that can help external devices monitor cells.”

Researchers from MIT and elsewhere have developed a new AI technique for teaching robots to pack items into a limited space while adhering to a range of constraints, reports Nick Hilden for Scientific American. “We want to have a learning-based method to solve constraints quickly because learning-based [AI] will solve faster, compared to traditional methods,” says graduate student Zhutian “Skye” Yang.

Four faculty members from across MIT - Professors Song Han, Simon Johnson, Yoon Kim and Rosalind Picard - speak with Curiosity Stream about the opportunities and risks posed by the rapid advancements in the field of AI. “We do want to think about which human capabilities we treasure,” says Picard. She adds that during the Covid-19 pandemic, “we saw a lot of loss of people's ability to communicate with one another face-to-face when their world moved online. I think we need to be thoughtful and intentional about what we're building with the technology and whether it's diminishing who we are or enhancing it.”

Prof. Russ Tedrake and Max Bajracharya '21 MEng '21 speak with TechCrunch reporter Brian Heater about the impact of generative AI on the future of robotics. “Generative AI has the potential to bring revolutionary new capabilities to robotics,” says Tedrake. “Not only are we able to communicate with robots in natural language, but connecting to internet-scale language and image data is giving robots a much more robust understanding and reasoning about the world.”

Researchers from MIT and elsewhere have developed a new 3D printing process that “allows users to create more elastic materials along with rigid ones using slow-curing polymers,” reports Tony Ho Tran for the Daily Beast. The researchers used the system to create a, “3D printed hand complete with bones, ligaments, and tendons. The new process also utilizes a laser sensor array developed by researchers at MIT that allows the printer to actually ‘see’ what it’s creating as it creates it.”

 Prof. Arnaud Costinot and Prof. Iván Werning speak with TechCrunch reporter Brian Heater about their research examining the potential impact of a robot tax on automation and jobs. “The potential wages people can earn may become more unequal with new technologies and the idea is that the tax can mitigate these effects,” Costinot and Werning explain. “In a sense, one can think of this as pre-distribution, affecting earnings before taxes, instead of redistribution.”

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 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 scientists have found that the “motions of undulating animals and the states of quantum objects can be described using strikingly similar equations,” writes Karmela Padavic-Callaghan for New Scientist. The similarity “allowed the team to use mathematical tools previously developed by quantum physicists to analyze the animals,” notes Padavic-Callaghan. “For instance, the team quantified how differently a snake-like robot and a C. elegans move and created a diagram that placed them on a spectrum of other undulating creatures.”

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.”