Robotics

Research Assistants Maisy Lam and Laura Dodds speak with Tech Briefs reporter Andrew Corselli about their work developing MiFly, a new approach that “enables a drone to self-localize, or determine its position, in indoor, dark, and low-visibility environments.” Dodds explains: “Our high-level idea was we can place a millimeter wave sensor on the drone, and it can localize itself with respect to a sticker that we place on the wall, a millimeter wave tag. This would allow us to provide a localization system in these challenging environments with minimal infrastructure.”

Tye Brady SM '99 speaks with Fortune reporter John Kell about his career in robotic development and the role of generative AI in future advancements. “We’re using generative AI in just about everything that we’re doing inside of robotics,” says Brady. 

Prof. Caitlin Mueller and her colleagues has been honored as recipients of the journal Technology | Architecture + Design’s 2025 TAD Research Contribution Award, reports Josh Niland for Archinect. The awarded projects delve “into the possibility of robotics and mixed reality (MR) fabrication strategies and an application of data visualization and circular economy concepts to enable sustainable housing,” explains Niland. 

In this interview (in Spanish), graduate students Suhan Kim and Yi-Hsuan (Nemo) Hsiao speak with Telemundo correspondent Miriam Arias about their work developing insect-sized robots to assist with agricultural needs. “There might be one year where you have a lot of bees in the field that help you pollinate everything. Maybe the next year, it might be affected by the temperature or something [and] you just don’t have enough bees to help you do so,” explains Hsiao. 

MIT engineers have developed a new training method to help ensure the safe operation of multiagent systems, including robots, search-and-rescue drones and self-driving cars, reports Jijo Malayil for Interesting Engineering. The new approach “doesn’t focus on rigid paths but rather enables agents to continuously map their safety margins—the boundaries within which they must stay,” writes Malayil. 

Graduate students Suhan Kim and Yi-Hsuan (Nemo) Hsiao speak with Tech Briefs reporter Andrew Corselli about their work developing insect-sized robots capable of artificial pollination. “Typical drones use electromagnetic motors plus propellers. But, our system is a little different in that we are primarily using an artificial muscle,” explains Kim. 

Researchers from MIT and elsewhere have develop insect-sized robots that could one day be used to help with farming practices like artificial pollination, reports Alice Rizzo for Reuters. "These type of robots will open up a very new type of use case," says graduate student Suhan Kim. "We can start thinking of using our robot, if it works well, for tools like indoor farming."

Researchers at MIT have developed an insect-like, flying robot capable of performing acrobatic maneuvers and hovering in the air for up to 15 minutes without failing, reports Alex Wilkins for New Scientist. “By having a hugely increased [flying] lifetime, we were able to work on the controller parts so that the robot can achieve precise trajectory tracking, plus aggressive maneuvers like somersaults,” says graduate student Suhan Kim. 

Prof. Daron Acemoglu highlights the economic and societal implications of integrating automation in the workforce, reports Taylor Nicole Rogers for The Financial Times. “Acemoglu says that robots’ current capabilities mean that those most at risk of being displaced are in blue-collar jobs and lack college degrees, which may make it difficult for them to shift into the high-tech roles likely to be created by automation,” writes Rogers. 

Prof. Daniela Rus, director of CSAIL, speaks with NBC Boston reporter Colton Bradford about her work developing a new AI system aimed at making grocery shopping easier, more personalized and more efficient. “I think there is an important synergy between what people can do and what machines can do,” says Rus. “You can think of it as machines have speed, but people have wisdom. Machines can lift heavy things, but people can reason about what to do with those heavy things.” 

Using a new technique developed to examine the risks of multimodal large language models used in robots, MIT researchers were able to have a “simulated robot arm do unsafe things like knocking items off a table or throwing them by describing actions in ways that the LLM did not recognize as harmful and reject,” writes Will Knight for Wired. “With LLMs a few wrong words don’t matter as much,” explains Prof. Pulkit Agrawal. “In robotics a few wrong actions can compound and result in task failure more easily.”

Prof. Daniela Rus, director of CSAIL, and Prof. Russ Tedrake speak with the Financial Times about how advances in AI have made it possible for robots to learn new skills and perform complex tasks. “All these cool things that we only dreamed of, we can now begin to realize,” says Rus. “Now we have to make sure that what we do with all these superpowers is good.”

Researchers at MIT have developed a robot capable of assembling “building blocks called voxels to build an object with almost any shape,” reports Alex Wilkins for New Scientist. “You can get furniture-scale objects really fast in a very sustainable way, because you can reuse these modular components and ask a robot to reassemble them into different large-scale objects,” says graduate student Alexander Htet Kyaw.

Researchers at MIT have developed a new virtual training program for four-legged robots by taking “popular computer simulation software that follows the principles of real-world physics and inserting a generative AI model to produce artificial environments,” reports Jeremy Hsu for New Scientist. “Despite never being able to ‘see’ the real world during training, the robot successfully chased real-world balls and climbed over objects 88 per cent of the time after the AI-enhanced training,” writes Hsu. "When the robot relied solely on training by a human teacher, it only succeeded 15 per cent of the time.”

Researchers at MIT have developed a new model for training robots dubbed Heterogeneous Pretrained Transformers (HPT), reports Brain Heater for TechCrunch. The new model “pulls together information from different sensors and different environments,” explains Heater. “A transformer was then used to pull together the data into training models. The larger the transformer, the better the output. Users then input the robot design, configuration, and the job they want done.”