{"id":384149,"date":"2026-07-22T05:02:23","date_gmt":"2026-07-22T03:02:23","guid":{"rendered":"https:\/\/prostartup.it\/mit-s-tiny-flying-robot-just-learned-to-move-like-a-real-insect\/"},"modified":"2026-07-22T05:02:23","modified_gmt":"2026-07-22T03:02:23","slug":"mit-s-tiny-flying-robot-just-learned-to-move-like-a-real-insect","status":"publish","type":"post","link":"https:\/\/prostartup.it\/ru\/mit-s-tiny-flying-robot-just-learned-to-move-like-a-real-insect\/","title":{"rendered":"MIT\u2019s Tiny Flying Robot Just Learned To Move Like a Real Insect"},"content":{"rendered":"
\n
\"Microrobot
Chen\u2019s group has been building robotic insects for more than five years. Credit: Courtesy of the Soft and Micro Robotics Laboratory<\/figcaption><\/figure>\n

Moving with the speed and agility of an insect, the tiny robot could someday assist in search-and-rescue missions.<\/strong><\/p>\n

After an earthquake, survivors can become trapped beneath piles of unstable debris where conventional rescue robots are too large to enter. Tiny flying machines modeled on insects could one day move through these narrow gaps while avoiding fixed barriers and falling rubble.<\/p>\n

Until recently, however, aerial microrobots could only travel slowly along simple, smooth paths, falling far short of the rapid and agile flight seen in insects \u2014 until now.<\/p>\n

AI unlocks insectlike aerial agility<\/h4>\n

MIT<\/span> researchers have demonstrated aerial microrobots that can match the speed and agility of their biological counterparts. They developed a new AI-based controller that allowed the robotic insect to perform demanding flight maneuvers, including repeated body flips.<\/p>\n

The two-part control system combines strong performance with computational efficiency. Compared with the researchers\u2019 best earlier demonstrations, it increased the robot\u2019s speed by about 450 percent and its acceleration by roughly 250 percent.<\/p>\n

\"Flying
A time-lapse photo shows a flying microrobot performing a flip. Credit: Courtesy of the Soft and Micro Robotics Laboratory<\/figcaption><\/figure>\n

The microrobot completed 10 consecutive somersaults in 11 seconds while maintaining control even as wind disturbances pushed against it.<\/p>\n

Flight performance approaches living insects<\/h4>\n

\u201cWe want to be able to use these robots in scenarios that more traditional quadcopter 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,\u201d 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.<\/p>\n

Chen is joined on the paper by co-lead authors Yi-Hsuan Hsiao, an EECS MIT graduate student; Andrea Tagliabue PhD \u201924; 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 \u201923; 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 was published in Science Advances<\/span><\/em>.<\/p>\n

A robotic insect no larger than a microcassette and weighing less than a paperclip can now perform rapid, demanding flight maneuvers. The achievement builds on more than five years of work by Chen\u2019s group to improve both the physical design and control of tiny flying robots.<\/p>\n

\"Insect
\u201cNow, with our bio-inspired control framework, the flight performance of our robot is comparable to insects in terms of speed, acceleration, and the pitching angle,\u201d says Kevin Chen. Credit: Courtesy of the Soft and Micro Robotics Laboratory<\/figcaption><\/figure>\n

Their latest model is more durable than earlier versions and has larger flapping wings for greater agility. Soft artificial muscles power those wings, driving them back and forth at extremely high speeds.<\/p>\n

The hardware had advanced, but the robot\u2019s controller \u2014 the \u201cbrain\u201d that tracks its position and directs its flight \u2014 remained a major limitation. Because a person had to tune it by hand, the system could not fully exploit the robot\u2019s improved abilities.<\/p>\n

Matching the fast, aggressive movements of an insect required a controller that could manage uncertainty and rapidly solve difficult optimization problems. Yet a system powerful enough to handle those calculations would ordinarily demand too much computing power for real-time flight, especially given the complex aerodynamics of such a lightweight machine.<\/p>\n

Software catches up with stronger hardware<\/h4>\n

Chen\u2019s group partnered with How\u2019s team to overcome that conflict. Together, they created a two-step, AI-driven control system that combines the reliability needed for fast, complicated maneuvers with the efficiency required to operate in real time.<\/p>\n

\u201cThe 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\u2019s team demonstrates new capabilities, we demonstrate that we can utilize them,\u201d How says.<\/p>\n

An expert planner trains the AI<\/h4>\n

The first part of the system is a model-predictive controller. Using a mathematical model of the robot\u2019s motion, it forecasts how the machine will behave and selects the best sequence of actions for safely following a planned route.<\/p>\n

This powerful planner can work out difficult maneuvers such as consecutive somersaults, sharp turns, and steep body tilts, although its calculations require substantial computing power. It also accounts for limits on the forces and torques the robot can generate, helping it maintain control and avoid collisions.<\/p>\n

Repeated flips are especially demanding because the robot must finish each rotation in the correct position and at the correct speed to begin the next one.<\/p>\n

\u201cIf 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,\u201d How says.<\/p>\n

Instead of running the demanding planner directly during flight, the researchers used it as an expert teacher. Through imitation learning, it trained a \u201cpolicy\u201d built from a deep-learning model. This policy serves as the robot\u2019s decision-making engine, determining where it should go and how it should move.<\/p>\n

\"Wind
Even when wind disturbances threatened to push it off course, a speedy robot was agile enough to complete 10 consecutive somersaults in 11 seconds. Credit: Courtesy of the Soft and Micro Robotics Laboratory<\/figcaption><\/figure>\n

In effect, imitation learning transfers the capabilities of the computationally intensive planner into a faster AI model that can operate in real time.<\/p>\n

The challenge was generating enough useful training data to prepare the policy for aggressive maneuvers without producing more data than necessary.<\/p>\n

\u201cThe robust training method is the secret sauce of this technique,\u201d How explains.<\/p>\n

During flight, the AI-driven policy receives the robot\u2019s position and immediately produces control instructions, including the required thrust force and torques.<\/p>\n

Speed rises without sacrificing control<\/h4>\n

Tests showed that the two-step system allowed the insect-scale robot to fly 447 percent faster and accelerate 255 percent more quickly than before. It completed 10 somersaults in 11 seconds while remaining within 4 or 5 centimeters of its intended path.<\/p>\n

\u201cThis 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,\u201d says Hsiao.<\/p>\n

The robot also reproduced a movement known as a saccade. During this maneuver, insects tilt sharply, accelerate toward a location, and then pitch in the opposite direction to stop. This rapid change in speed helps them determine their position and maintain clear vision.<\/p>\n

\u201cThis bio-mimicking flight behavior could help us in the future when we start putting cameras and sensors on board the robot,\u201d Chen says.<\/p>\n

Sensors could unlock independent flight<\/h4>\n

A major next step will be equipping the microrobots with cameras and sensors so they can navigate outdoors without relying on an elaborate motion capture system.<\/p>\n

The researchers also plan to explore whether onboard sensors could help groups of robots coordinate their movements and avoid crashing into one another.<\/p>\n

\u201cFor 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,\u201d says Chen.<\/p>\n

Reference: \u201cAerobatic maneuvers in insect-scale flapping-wing aerial robots via deep-learned robust tube model predictive control\u201d by Yi-Hsuan Hsiao, Andrea Tagliabue, Owen Matteson, Suhan Kim, Tong Zhao, Jonathan P. How and YuFeng Chen, 3 December 2025, Science Advances<\/i>.
DOI: 10.1126\/sciadv.aea8716<\/p>\n

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.<\/p>\n

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