An insect-scale flying robot developed at MIT became 447% faster after researchers replaced a hand-tuned controller with a two-stage AI system. The tiny machine also achieved a 255% increase in acceleration and completed 10 consecutive somersaults in 11 seconds while staying within roughly four to five centimetres of its planned path. The work is described by MIT News and in a paper published in Science Advances.
The achievement matters because very small flying robots face an awkward engineering trade-off. They can fit into spaces a normal drone cannot reach, but their size leaves little room for batteries, computers and sensors. A sophisticated controller may be excellent in simulation and still be too computationally heavy to run fast enough for real flight.
The researchers trained a fast controller from a slower expert
The paper, “Aerobatic maneuvers in insect-scale flapping-wing aerial robots via deep-learned robust tube model predictive control”, combines two forms of control. First, a computationally demanding model-predictive controller plans difficult manoeuvres while respecting the forces and torques the robot can physically produce. The researchers then use imitation learning to train a deep-learning policy to copy that expert behaviour quickly enough for real-time control.
It is similar to having a slow expert work out many difficult examples in advance, then training a faster apprentice to reproduce the decisions. The real-time policy receives information about the robot’s position and produces commands for thrust and torque without having to solve the full optimisation problem from scratch during every moment of flight.
The training method is important because repeated aerobatic manoeuvres amplify tiny errors. A single flip can succeed even if the robot finishes slightly out of position. Ten flips in a row require the controller to correct those deviations continuously so the next manoeuvre starts from the right state.
The robot flew faster and handled disturbances
In the experiments, the two-stage controller increased flight speed by 447% and acceleration by 255% compared with the team’s earlier demonstrations. The robot performed the repeated flips even when wind disturbances pushed against it. Its path remained close to the planned trajectory.
The researchers also demonstrated a rapid movement similar to an insect saccade, where the body pitches sharply, accelerates to a new position and then pitches the other way to stop. In insects, such movements help with navigation and vision. Replicating them could become useful when the robots eventually carry onboard cameras.
The robot is still tethered and depends on external computing
The most important limitation is easy to miss in the spectacular flight figures. The controller currently runs on an external computer and the microrobot is not yet a fully autonomous search-and-rescue machine. MIT says future work includes adding onboard cameras and sensors so the robot can operate outside a motion-capture environment.
Power is another challenge for insect-scale flight. Extremely small robots have very little room for batteries while their flapping actuators need energy. A laboratory demonstration can therefore prove agility long before the complete package is ready to fly independently through a damaged building.
Why tiny robots are attractive for rescue work
After an earthquake or structural collapse, rescuers may need to inspect spaces too small or unstable for a person or conventional drone. An insect-sized aircraft could theoretically move through narrow gaps, carry a camera and search several routes without disturbing rubble as much as a larger machine. Fast control matters because the environment is unlikely to be smooth or still.
LiveAIWire has covered other efforts to make robots more practical outside controlled demonstrations, including a robot dog running a full marathon on one battery and a paper-thin muscle-powered swimming robot. The MIT work tackles another physical limit: how to make a tiny flying body react quickly enough to remain stable during aggressive movement.
AI is useful here because the physics are hard
Flapping-wing robots are more complicated to control than a simple point moving through space. Aerodynamic forces change rapidly with wing motion and body angle, and small manufacturing differences can matter. A controller built around a perfect mathematical model may fail when the real robot behaves slightly differently.
The learning stage helps compress a robust expert controller into a faster policy that can tolerate uncertainty. It does not remove physics from the problem. In fact, the expert planner relies heavily on a dynamic model and constraints. The AI is useful because it learns to reproduce the expensive optimisation quickly, not because the machine has somehow learned to ignore aerodynamics.
The next step is autonomy, not another flip record
The researchers say they want to add sensing and explore how groups of the microrobots could avoid collisions and coordinate. Those goals are harder than scripted aerobatics. A robot in rubble has to understand where it is, detect obstacles, choose where to go and survive unexpected contact while carrying its own sensing and computation.
That makes the work complementary to research on robot learning. LiveAIWire recently reported on microscopic robots that respond to their physical environment. At very small scales, intelligence is constrained by the body. The best algorithm is useful only if the robot can power it, sense enough of the world and physically execute the commands.
For now, the 447% speed increase is a laboratory result under controlled conditions. It is still impressive because it shows that better control software can unlock performance already latent in the hardware. The tiny robot did not need a completely new body to become dramatically more agile. It needed a controller capable of using that body closer to its limits.
That is why the 10 flips are more than a stunt. Repeated aerobatics force the system to recover from small errors again and again. If the same robustness can be combined with onboard sensing and untethered power, insect-scale robots could eventually become useful machines rather than extraordinary laboratory flyers.
Small robots have to solve the whole flight problem
Speed is only one part of turning an insect-scale flying robot into a useful autonomous machine. The robot must also know where it is, detect obstacles, carry enough energy, run its control system and communicate without adding so much weight that it can no longer fly. Large drones can solve many of those problems with bigger batteries, cameras and computers. At this scale, every added component competes with the tiny payload budget.
That makes the separation between control research and full autonomy important. MIT’s experiments show what the new controller can do when it has the information and computing support it needs. They do not show a self-contained robot independently navigating a building or field. Moving the sensing and computation onboard will require hardware advances as well as efficient software, and endurance will matter just as much as acrobatics for many practical jobs.
The 447% improvement also needs its baseline. It is a comparison with the earlier flight speed achieved by this research platform, not a claim that the robot is now 447% faster than every other microrobot. Laboratory performance numbers are most useful when they show how much one design change unlocked within the same system. Here, the result suggests that control software had been a significant bottleneck.
If that bottleneck continues to fall, the applications become easier to imagine. Very small flyers could inspect machinery, enter gaps that larger drones cannot reach or work in groups where losing one inexpensive unit is less serious than losing a large aircraft. Those possibilities remain future uses rather than capabilities demonstrated in this experiment. The present achievement is narrower but still striking: a lightweight artificial insect can execute rapid, repeated manoeuvres with much tighter control than before.
There is also a lesson for robotics more broadly. Better hardware does not automatically deliver better behaviour, and better AI cannot ignore physical limits. Progress often comes from co-designing the body, actuators and controller so each part makes the others more useful. In a machine this small, that relationship is impossible to hide.
Swarm use would introduce another layer of difficulty. Dozens of tiny flyers would need to avoid one another, share enough information to coordinate and cope with units dropping out without creating a communications burden larger than the task itself. That is one reason laboratory agility matters even before those systems exist: a robot that can recover quickly and follow a precise path gives future coordination software a more reliable physical platform to work with.
About the Author
Stuart Kerr is Technology Correspondent at LiveAIWire, covering artificial intelligence, cybersecurity and the social impact of emerging technology. LiveAIWire is an independent, human-led technology publication using AI-assisted research, editorial production and original AI-assisted editorial illustrations under his direction.