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MIT engineers design an aerial microrobot that can fly as fast as a bumblebee

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MIT engineers design an aerial microrobot that can fly as fast as a bumblebee


In the future, tiny flying robots could be deployed to aid in the search for survivors trapped beneath the rubble after a devastating earthquake. Like real insects, these robots could flit through tight spaces larger robots can’t reach, while simultaneously dodging stationary obstacles and pieces of falling rubble.

So far, aerial microrobots have only been able to fly slowly along smooth trajectories, far from the swift, agile flight of real insects — until now.

MIT researchers have demonstrated aerial microrobots that can fly with speed and agility that is comparable to their biological counterparts. A collaborative team designed a new AI-based controller for the robotic bug that enabled it to follow gymnastic flight paths, such as executing continuous body flips.

With a two-part control scheme that combines high performance with computational efficiency, the robot’s speed and acceleration increased by about 450 percent and 250 percent, respectively, compared to the researchers’ best previous demonstrations.

The speedy robot was agile enough to complete 10 consecutive somersaults in 11 seconds, even when wind disturbances threatened to push it off course.

A microrobot flips 10 times in 11 seconds.

Credit: Courtesy of the Soft and Micro Robotics Laboratory

“We want to be able to use these robots in scenarios that more traditional quad copter robots would have trouble flying into, but that insects could navigate. Now, with our bioinspired control framework, the flight performance of our robot is comparable to insects in terms of speed, acceleration, and the pitching angle. This is quite an exciting step toward that future goal,” says Kevin Chen, an associate professor in the Department of Electrical Engineering and Computer Science (EECS), head of the Soft and Micro Robotics Laboratory within the Research Laboratory of Electronics (RLE), and co-senior author of a paper on the robot.

Chen is joined on the paper by co-lead authors Yi-Hsuan Hsiao, an EECS MIT graduate student; Andrea Tagliabue PhD ’24; and Owen Matteson, a graduate student in the Department of Aeronautics and Astronautics (AeroAstro); as well as EECS graduate student Suhan Kim; Tong Zhao MEng ’23; and co-senior author Jonathan P. How, the Ford Professor of Engineering in the Department of Aeronautics and Astronautics and a principal investigator in the Laboratory for Information and Decision Systems (LIDS). The research appears today in Science Advances.

An AI controller

Chen’s group has been building robotic insects for more than five years.

They recently developed a more durable version of their tiny robot, a microcassette-sized device that weighs less than a paperclip. The new version utilizes larger, flapping wings that enable more agile movements. They are powered by a set of squishy artificial muscles that flap the wings at an extremely fast rate.

But the controller — the “brain” of the robot that determines its position and tells it where to fly — was hand-tuned by a human, limiting the robot’s performance.

For the robot to fly quickly and aggressively like a real insect, it needed a more robust controller that could account for uncertainty and perform complex optimizations quickly.

Such a controller would be too computationally intensive to be deployed in real time, especially with the complicated aerodynamics of the lightweight robot.

To overcome this challenge, Chen’s group joined forces with How’s team and, together, they crafted a two-step, AI-driven control scheme that provides the robustness necessary for complex, rapid maneuvers, and the computational efficiency needed for real-time deployment.

“The hardware advances pushed the controller so there was more we could do on the software side, but at the same time, as the controller developed, there was more they could do with the hardware. As Kevin’s team demonstrates new capabilities, we demonstrate that we can utilize them,” How says.

For the first step, the team built what is known as a model-predictive controller. This type of powerful controller uses a dynamic, mathematical model to predict the behavior of the robot and plan the optimal series of actions to safely follow a trajectory.

While computationally intensive, it can plan challenging maneuvers like aerial somersaults, rapid turns, and aggressive body tilting. This high-performance planner is also designed to consider constraints on the force and torque the robot could apply, which is essential for avoiding collisions.

For instance, to perform multiple flips in a row, the robot would need to decelerate in such a way that its initial conditions are exactly right for doing the flip again.

“If small errors creep in, and you try to repeat that flip 10 times with those small errors, the robot will just crash. We need to have robust flight control,” How says.

They use this expert planner to train a “policy” based on a deep-learning model, to control the robot in real time, through a process called imitation learning. A policy is the robot’s decision-making engine, which tells the robot where and how to fly.

Essentially, the imitation-learning process compresses the powerful controller into a computationally efficient AI model that can run very fast.

The key was having a smart way to create just enough training data, which would teach the policy everything it needs to know for aggressive maneuvers.

“The robust training method is the secret sauce of this technique,” How explains.

The AI-driven policy takes robot positions as inputs and outputs control commands in real time, such as thrust force and torques.

Insect-like performance

In their experiments, this two-step approach enabled the insect-scale robot to fly 447 percent faster while exhibiting a 255 percent increase in acceleration. The robot was able to complete 10 somersaults in 11 seconds, and the tiny robot never strayed more than 4 or 5 centimeters off its planned trajectory.

“This work demonstrates that soft and microrobots, traditionally limited in speed, can now leverage advanced control algorithms to achieve agility approaching that of natural insects and larger robots, opening up new opportunities for multimodal locomotion,” says Hsiao.

The researchers were also able to demonstrate saccade movement, which occurs when insects pitch very aggressively, fly rapidly to a certain position, and then pitch the other way to stop. This rapid acceleration and deceleration help insects localize themselves and see clearly.

“This bio-mimicking flight behavior could help us in the future when we start putting cameras and sensors on board the robot,” Chen says.

Adding sensors and cameras so the microrobots can fly outdoors, without being attached to a complex motion capture system, will be a major area of future work.

The researchers also want to study how onboard sensors could help the robots avoid colliding with one another or coordinate navigation.

“For the micro-robotics community, I hope this paper signals a paradigm shift by showing that we can develop a new control architecture that is high-performing and efficient at the same time,” says Chen.

“This work is especially impressive because these robots still perform precise flips and fast turns despite the large uncertainties that come from relatively large fabrication tolerances in small-scale manufacturing, wind gusts of more than 1 meter per second, and even its power tether wrapping around the robot as it performs repeated flips,” says Sarah Bergbreiter, a professor of mechanical engineering at Carnegie Mellon University, who was not involved with this work.

“Although the controller currently runs on an external computer rather than onboard the robot, the authors demonstrate that similar, but less precise, control policies may be feasible even with the more limited computation available on an insect-scale robot. This is exciting because it points toward future insect-scale robots with agility approaching that of their biological counterparts,” she adds.

This research is funded, in part, by the National Science Foundation (NSF), the Office of Naval Research, Air Force Office of Scientific Research, MathWorks, and the Zakhartchenko Fellowship.



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Bluesky CEO Jay Graber Is Stepping Down

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Bluesky CEO Jay Graber Is Stepping Down


Jay Graber is stepping down as head of Bluesky, the social media platform exclusively announced to WIRED. Venture capitalist Toni Schneider will be the interim CEO until a permanent replacement is found.

“As Bluesky matures, the company needs a seasoned operator focused on scaling and execution, while I return to what I do best: building new things,” Graber wrote in a statement about the personnel change.

Graber joined Bluesky in 2019, when it was a research project within Twitter focused on developing a decentralized framework for the social web. She became the company’s first chief executive officer in 2021, when it spun out into an independent entity. She oversaw the platform’s remarkable rise and the growing pains it experienced as it transformed from a quirky Twitter offshoot to a full-fledged alternative to X.

Schneider tells WIRED that he intends to help Bluesky “become not just the best open social app, but the foundation for a whole new generation of user-owned networks.”

Schneider, who will continue working as a partner at the venture capital firm True Ventures while at Bluesky, was previously CEO of the WordPress parent company, Automattic, from 2006 to 2014. He also served as its CEO again in 2024 while top executive Matt Mullenweg went on a sabbatical. During that time, Schneider met Graber and became an adviser to Bluesky’s leadership. In a blog post announcing his new role, Schneider said he plans to emphasize scaling, describing his job as “to help set up Bluesky’s next phase of growth.”

This isn’t the end for Graber and Bluesky. She will transition to become the company’s chief innovation officer, a role focused on Bluesky’s technology stack rather than its business operations. The position was created for her. Graber, who began her career as a software engineer, has always sounded the most enthusiastic when discussing Bluesky’s technology rather than its revenue streams.

Bluesky’s board of directors will appoint the next permanent CEO. The members include Jabber founder Jeremie Miller, crypto-focused VC Kinjal Shah, TechDirt founder Mike Masnick, and Graber. (Twitter founder Jack Dorsey was originally part of the board but quit in 2024.) This means Graber will have input on her successor. The talent search is still in early stages.

It’s a pivotal moment for Bluesky. The company found success by positioning itself as a progressive replacement for Elon Musk’s X. That helped fuel the platform’s rise as X’s hard-right ideological turn prompted some users to seek new social networks. In 2025, Bluesky grew from 25 million users to over 40 million, according to its annual Transparency Report. Its team is optimistic it can continue expanding while staying true to its roots. Masnick says Schneider’s tenure at Automattic “proves you build a real business around open software.”

As far as social platforms go, though, it’s still a niche offering, and one perpetually subject to pundit-class grumblings about how it’s too woke or not woke enough. (Just last week, in a conversation with WIRED, Dorsey said he wasn’t happy with the platform because of “ideology.”) Meta’s competing app, Threads, has roughly 400 million users, or approximately 10 times more active accounts than Bluesky. Even if it’s not interested in chasing the type of hockey-stick growth traditionally favored in Silicon Valley, the company does need to convince more people and institutions to use its platform if it wants to stake a claim to the role of digital commons.



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Espresso Machines Are Like Guitars: The Rich Don’t Win

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Coffee is the original biohack and the nation’s most popular productivity tool. As we adjust to the changeover to daylight saving time, the caffeine-addicted WIRED Reviews team is writing about our favorite coffee brewing routines and devices. Today, reviewer Peter Cottell expounds on why espresso machines don’t have to be any fancier than a Casabrews 5700. Look out for other Java.Base stories about other WIRED writers’ favorite brewing methods.

There’s a slogan in the guitar world that claims “tone is stored in the fingers.” It’s a reductive notion that’s meant to urge upstart shredders to journey within for an ideal guitar sound that suits them best rather than spend a lifetime and tens of thousands of dollars on expensive pedals, amps, and a high-end guitar with a boomer’s signature engraved on the headstock. The irony of this phrase is that it’s usually muttered by the very geezers who can afford such gear; think Joe Bonamassa, John Mayer, and James Dolan, whom the guitar world refers to as “blues lawyers.”

Fancy coffee gear can get you pretty far, but it’s as useless as a $20,000 Les Paul without technique or inspiration. The punk boom of 1977 showed ambitious musicians that they could get pretty far with attitude and initiative. But it was amidst the egalitarian post-punk boom of the early ’80s that we learned practicing your instrument and keeping an open mind can lead to transcendence, financial circumstances be damned.

In the summer of 2008, I found myself unemployed with a communications degree from a large state college, so I took the next logical step and took a turn in the service industry. A local chain of coffee shops was the first employer to call me back, so off I went to become a barista despite having, until then, consumed a total of 2 cups of coffee in my entire life. I spent the first year drinking cold brew and working afternoon or evening shifts. Then I was moved to mornings, and I had to learn how to dial in an espresso machine. And everything changed forever.

I don’t recall the make or model of the machine, but you’ll get an idea of its form and function when you imagine a local second-wave shop with a ragged GVC aesthetic, a crowded bulletin board that’s overrun with business cards from sex pests turned yoga instructors, and a silly alliterative name like Jammin’ Java or Expresso Express. At the onset, “dialing in” consisted of jiggling the grind size on the grinder until it spit out a pile of grounds that yielded a shot anywhere between 20 and 40 seconds. There was no scale, and the temperature and pressure specs of the machine were a mystery, and no one cared about any of this because most of the espresso drinks we sold were doused in DaVinci syrup and 2 percent milk. It wasn’t until the hammer came down on everyone behind the counter’s overconsumption of expensive sugary drinks that I was forced to reckon with espresso. I spent the next three years figuring out how to coax something drinkable out of this cursed, faltering machine, and I finally reached the same conclusion as many before me: Espresso is universal. It is the base unit of caffeination. The binary code of the coffee world. The bottom brick of everything earthy, bitter, brown, and rich.

After my stint at the declining café in Ohio, I moved across the country and graduated to a bakery-coffee-shop hybrid in Portland, Oregon. While it wasn’t a bona fide third-wave shop, we were close enough to stalwarts on the scene like Heart and Stumptown, so we took coffee as seriously as we could. The morning crew was responsible for dialing in three different grinders: decaf, a blend, and a single origin. Walking to work before dawn in the silent fog was a meditative experience, no matter how hungover I was, and the process of taking notes while sipping shots and adjusting the grinder and extraction time ever so slightly is a morning ritual I would return to daily if I could. Then your coworker arrives, the stereo turns from ambient techno to Electric Wizard, the customers slowly trickle in, and all hell breaks loose. You become one with the machine.



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Anthropic Sues Department of Defense Over Supply-Chain Risk Designation

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Anthropic Sues Department of Defense Over Supply-Chain Risk Designation


Anthropic filed a federal lawsuit against the US Department of Defense and other federal agencies on Monday, challenging its designation of the AI company as a “supply-chain risk.”

The Pentagon formally sanctioned Anthropic last week, capping a weeks-long, publicly aired disagreement over limits on use of its generative AI technology for military applications such as autonomous weapons.

“We do not believe this action is legally sound, and we see no choice but to challenge it in court,” Anthropic CEO Dario Amodei wrote in a blog post on Thursday.

The lawsuit, which was filed in a federal court in California, requested that a judge reverse the designation and stop federal agencies from enforcing it. “The Constitution does not allow ​the government to wield its enormous power to punish a company for its protected speech,” Anthropic said in the filing. “Anthropic turns to the judiciary as a last resort to vindicate its rights and halt the Executive’s unlawful campaign of retaliation.”

The AI startup, which develops a suite of AI models called Claude, is facing the possibility of losing hundreds of millions of dollars in annual revenue from the Pentagon and the rest of the US government. It also may lose the business of software companies that incorporate Claude into services they sell to federal agencies. Several Anthropic customers have reportedly said they are pursuing alternatives due to the Defense Department’s risk designation.

Amodei wrote that the “vast majority” of Anthropic’s customers will not have to make changes. The US government’s designation “plainly applies only to the use of Claude by customers as a direct part of contracts with the” military, he said. General use of Anthropic technologies by military contractors should be unaffected.

The Department of Defense, which also goes by the Department of War, and the White House did not immediately respond to requests for comment about Anthropic’s lawsuit.

Attorneys with expertise in government contracting say Anthropic faces a difficult battle in court. The rules that authorize the Department of Defense to label a tech company as a supply-chain risk don’t allow for much in the way of an appeal. “It’s 100 percent in the government’s prerogative to set the parameters of a contract,” says Brett Johnson, a partner at the law firm Snell & Wilmer. The Pentagon, he says, also has the right to express that a product of concern, if used by any of its suppliers, “hurts the government’s ability to effectuate its mission.”

Anthropic’s best chance of success in court could be proving it was singled out, Johnson says. Soon after Defense Secretary Pete Hegseth announced that he was designating Anthropic a supply-chain risk, rival OpenAI announced it had struck a new contract with the Pentagon. That could be instrumental to Anthropic’s legal argument if the company can demonstrate it was seeking similar terms as the ChatGPT developer.

OpenAI said its deal included contractual and technical means of assuring its technology would not be used for mass domestic surveillance or to direct autonomous weapons systems. It added that it opposed the action against Anthropic and did know why its rival could not reach the same deal with the government.

Military Priority

Hegseth has prioritized military adoption of AI technologies, with posters recently seen in the Pentagon showing him pointing and that read, “I want you to use AI.” The dispute with Anthropic kicked up in January after Hegseth ordered several AI suppliers to agree that the department was free to use their technologies for any lawful purpose.

Anthropic, which is the only company currently providing AI chatbot and analysis tools for the military’s most sensitive use cases, pushed back. It contends that its technologies are not yet capable enough to be used for mass domestic surveillance of Americans or fully autonomous weapons. Hegseth has said Anthropic wants veto power over judgments that should be left to the Defense Department.



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