There are many words that I would never, ever use to describe a drone. Stealthy. Subtle. Whatever the opposite of obnoxious is. Much of this is because of the giant angry bee sound that drones tend to make, but it’s also the way that they look in flight: With uncannily linear movements and an even less canny ability to hover perfectly still, they tend to draw the eye as affronts to nature.

In a paper presented this week at RSS 2026 in Sydney, roboticists from Northwestern University, Evanston, Ill. demonstrated a drone called Phantom Twist that is essentially invisible to humans, being an order of magnitude more difficult to see in flight than a typical quadrotor. They accomplished this with the aid of computational design, and while the resulting hardware is, I would argue, also an order of magnitude more of an affront to nature than a typical quadrotor represents, it’s pretty amazing how well it works.

Phantom Twist spins so fast, it’s practically invisible.Michael Rubenstein/Northwestern University

The trick here is easy to see, even if the drone isn’t. By spinning in flight at between 15 and 25 Hz, Phantom Twist takes advantage of humans’ decidedly mediocre visual system to turn a solid spinning object into an opaque smear. Human eyes take some amount of time (typically about 100ms) to integrate what we see before sending the full scene off to our brains for processing. Moving objects can cause problems for this system, because if the movement is fast enough, our eyes are forced to average that motion across the scene, combining it with whatever is in the background and resulting in a transparent blur. This effect is called ‘persistence of vision.’ For something that spins like Phantom Twist, that motion blur comes from the drone’s rapid rotation and it works because most of the drone is cleverly designed to be empty space.

Drones that spin in flight are nothing new—we’ve covered a bunch of them in the past, including Picolissimo and any number of samsara drones inspired by the spinning flight of maple seeds. What makes Phantom Twist unique, and also very odd, is that the design was computationally optimized for low visibility.

Controlling how drones like this fly

Before we get into that, though, a quick note about how drones like this can even fly controllably, because it’s not at all obvious. With just a single motor and no control surfaces, the only possible control input is through the motor itself, and by pulsing the motor speed up or down at just the right time during each rotation, the drone can translate in any direction. Altitude control comes from changing overall motor thrust, and its spinning nature makes the drone passively stable.

Carbon fiber rods connect batteries, a controller, some counterweights, and a motor and propeller. The research robot also includes optical tracking tags.Michael Rubenstein/Northwestern University

The bits that you need for this kind of drone include the motor and propeller, a couple of batteries, a controller, some counterweights (which could be replaced with more batteries or payload), 0.8mm carbon fiber rods to tie it all together, and a connector for the handheld launcher that gets the whole thing up to speed. The actual arrangement of these components is surprisingly flexible, and that’s where the invisibility comes in.

“The design space is high dimensional,” explains Northwestern’s Michael Rubenstein. “It’s very difficult for a human to reason through all the tradeoffs between the physical constraints required for stable flight and the visual appearance of the spinning drone, and I don’t think we would have easily arrived at this low visibility design ourselves.”

The visibility (or not) of Phantom Twist is primarily driven by the extent to which different components line up with each other from the perspective of someone looking at the drone. The more components that line up with each other as the drone flies, the less background you see through the spinning drone, and the more visible the drone becomes. Because you might be looking at the drone from a number of different angles, and also because the drone has to be stable enough for controlled flight, there are a bunch of different things that need to be optimized all at once, which is why computational design is effective here.

Phantom Twist’s final design was generated using an iterative optimizer which had a goal of minimizing a metric called “Learned Perceptual Image Patch Similarity,” or LPIPS, while making sure that the design could still physically work. LPIPS is the difference between two images: a background image, and a background image with an overlay of the simulated spinning drone. The smaller that difference is, the more invisible that design is. It’s tricky for a human to consider all of the variables at once, but Rubenstein says that the final design does make intuitive sense, because “the automated pipeline prefers placements where components don’t visually overlap as it spins, or where the components are too close to the center of rotation.”

Two iterations of Phantom Twist drones are shown with their handheld launching mechanisms. The better optimized version (bottom row) relocates the launcher interface to remove components that are too close to the central axis, making them more visible.Michael Rubenstein/Northwestern University

Out of a starting set of around 20,000 feasible Phantom Twist configurations, the optimized design (the one that you see or don’t see in the pictures and videos) has a LPIPS score of 0.0104. A human-designed Phantom Twist is about twice as visible, with a LPIPS score of around 0.2, and a conventional quadrotor (of the same size) would be over ten times more visible. And there’s still a bit more optimization that could be done with the electrical wiring as well as increasing the baseline transparency of the components themselves.

Phantom Twist is currently controlled using an optical tracking system, which means that it’s not yet capable of flying outside of a controlled environment. But Rubenstein has built other drones along similar principles in the past, which have successfully flown outside, and he’s optimistic about using those techniques to break Phantom Twist out of the lab. The spinning behavior might even enable some useful sensing capabilities, he says. “An interesting possibility is mounting a camera on the spinning body. As the vehicle rotates, it could capture imagery in every direction, effectively creating a 360 degree view of its surroundings that could be used for onboard navigation and control.”

As for what a drone like Phantom Twist could be used for—assuming that the sound can be mitigated somewhat (and there are potential approaches to making that happen), a stealthy microdrone could do all sorts of things with covert surveillance being the most obvious application. For his part, Rubenstein says that he’s personally excited about the potential for watching wildlife, “where a less intrusive drone could observe animals while minimizing its impact on their natural behavior.” The elephants in particular would certainly appreciate that.

For a deeper dive into all the particulars of this project, read the paper: Computational Design of a Low-Visibility UAV Using a Human-Aligned Perceptual Metric, by Jingxian Wang, Chen Yu, David Matthews, Emma Alexander, Sam Kriegman, and Michael Rubenstein from Northwestern University, which is being presented this week at RSS 2026 in Sydney.