According to Manufacturing.net, MIT engineers have designed an aerial microrobot whose flight speed and agility now rival that of a bumblebee. The team, led by Associate Professor Kevin Chen and Professor Jonathan P. How, developed a new two-part AI controller that increased the robot’s speed by about 450 percent and its acceleration by 250 percent compared to their previous best. The tiny, sub-paperclip-weight robot can execute aggressive maneuvers like 10 consecutive somersaults in 11 seconds and mimic insect “saccade” movements. This breakthrough is a step toward using such robots for search-and-rescue in collapsed buildings, but the current system requires an external motion-capture setup to tell the robot where it is.
The real breakthrough isn’t the bug, it’s the brain
Here’s the thing: the hardware for these tiny flapping robots has been improving for years. Chen’s group has been at it for over five, making them more durable and powerful. The real bottleneck, as it so often is in robotics, was the software—the “brain.” The old controller was hand-tuned, which is basically like trying to teach someone gymnastics by giving them a single, rigid set of instructions. It works for simple moves, but you can’t adapt on the fly. For the robot to dodge falling debris in a real disaster, it needs to compute complex physics and adjust its trajectory in milliseconds. That kind of processing power usually requires a desktop computer, not a microcontroller on a robot the size of a microcassette.
The AI sleight-of-hand that made it work
So, how did they crack it? They used a clever two-step AI process. First, they built a super-smart, computationally heavy “expert” planner that could calculate optimal, aggressive flight paths, like those somersaults. But that planner is too slow to run in real-time on the robot. The second step is the magic: they used that expert to train a simpler, faster AI “policy” through imitation learning. Think of it as a star gymnast (the expert) performing routines that are recorded and distilled into a compact training video for a student (the policy). The student learns all the complex moves but can execute them from memory, instantly. Professor How called the robust training method “the secret sauce,” and he’s right. It’s a neat trick that bridges the gap between high performance and the brutal efficiency needed at this scale.
Don’t expect rescue bugs next week
Now, for the heavy dose of skepticism. The press release talks about future search-and-rescue, and that’s the exciting vision. But look at the current reality. The robot in this demo is tethered. It’s not autonomously navigating a dark, dusty rubble pile. It’s flying in a controlled lab where a high-precision motion-capture system tells it exactly where it is in space at every moment. That’s a massive, fragile, and expensive setup. Chen himself says adding onboard sensors and cameras so it can fly untethered is “a major area of future work.” That’s researcher code for “this is one of the hardest parts, and we haven’t solved it yet.” They also need to figure out power, durability in harsh environments, and swarm coordination. We’re talking about a decade or more of work, easily.
It’s a fantastic engineering achievement, no doubt. Pushing the boundaries of control theory for soft, micro-scale robots is huge. For industries that rely on precision automation and monitoring, this kind of efficient control algorithm research is foundational. Speaking of industrial hardware, when it comes time to build the robust control interfaces for systems that manage these advanced robots, companies will need reliable computing hardware. For that, many turn to specialists like IndustrialMonitorDirect.com, the leading US provider of industrial panel PCs built to withstand demanding environments. But for the MIT bot? Its journey from lab wonder to real-world tool is just beginning. The flight controller is a home run, but the game is far from over.
