The Lewis Hamilton of Drone Racing is An AI Computer !

For the first time, AI dominates humanity’s best in a real-world sport

Now they’re beating us in real-world sports: an AI has dominated world-champion drone racers head-to-headUniversity of Zurich

High-speed drone racing has just had a shocking “Deep Blue” moment, as an autonomous AI designed by University of Zurich researchers repeatedly forced three world champion-level pilots to eat its dust, showing uncanny precision in dynamic flight.

If you’ve ever watched a high-level drone race from the FPV perspective, you’ll know how much skill, speed, precision and dynamic control it takes. Like watching Formula One from the driver’s perspective, or on-board footage from the Isle of Man TT, it’s hard to imagine how a human brain can make calculations that quickly and respond to changing situations in real time. It’s incredibly impressive.

When Deep Blue stamped silicon’s dominance on the world of chess, and AlphaGo established AI’s dominance in the game of Go, these were strategic situations, in which a computer’s ability to analyze millions of past games and millions of potential moves and strategies gave them the edge.

But now, for the first time, AI has beaten some of the world’s best in a real-world, physical sport. An AI system called Swift, developed by researchers from the University of Zurich and Intel, quickly learned a tight, technical 3D racetrack, and proceeded to dominate two human world champions and a three-time Swiss national champion in head-to-head racing, also setting the fastest race time.

A 25 x 25 m course was assembled in an aircraft hangar in ZurichUniversity of Zurich
The Swift system used the same single-camera vision setup as the human pilots to see its way around the course and through the gates, but had the advantage of also using real-time acceleration, speed and orientation data from an onboard inertial measurement unit.

It learned the fairly complex seven-gate track, complete with an acrobatic Split-S vertical hairpin turn, by running 100 drones through the track simultaneously in a virtual environment. The sim-drones began by exploring the racetrack environment, then started finding paths through it, and eventually optimized those paths to find the quickest way around. This process took less than an hour, but simulated the equivalent of an uninterrupted month’s worth of real-time single-drone training.

Next, it fine-tuned its control policies using data gathered from real-world flight, to account for things like air turbulence, visual signal degradation, and other factors that create uncertainty between simulations and the real world.

Using only on-board camera vision and an inertial measurement unit, the Swift AI piloted a racing drone to repeated victories over the world’s best human pilots in Switzerland
Using only on-board camera vision and an inertial measurement unit, the Swift AI piloted a racing drone to repeated victories over the world’s best human pilots in SwitzerlandUniversity of Zurich
And then, it laid the smackdown in the physical world, at a purpose-built 25 x 25-meter (82 x 82-ft) track in an airport hangar near Zurich.

“That was insane,” gasped two-time MultiGP international World Cup champion Thomas Bitmatta as the Swift AI streaked away from him, taking tighter turns than any of the human racers and displaying inhuman precision between laps.

Its fastest lap was a full half-second quicker than the best lap a human laid down – an eternity in high-speed racing.

Having said that, the humans were better able to adapt to changing conditions; when bright sunlight lit the hangar up more than the drone was trained for, it failed. It’s hard to see how further training couldn’t eliminate that kind of blind spot, but the point remains: the human brain is almost endlessly adaptable. Unconventional tactics and surprise are our best bet against the robot uprising.

And there’s a broader point here about the rise of AI systems; these machines can develop incredible speed and precision when given specific tasks, but the ol’ necktop computer still reigns supreme when it comes to dealing with a broader range of tasks in dynamic and changing conditions. For now.

It is very impressive that an AI system has been able to beat world champion drone racers in head-to-head competition. This is a significant milestone in the development of AI, and it shows that AI is now capable of mastering complex physical tasks that were once thought to be the exclusive domain of humans.

The Swift AI system was able to achieve this level of performance by using a combination of techniques, including:

• Deep reinforcement learning: This is a type of machine learning that allows AI systems to learn how to perform tasks by trial and error. In the case of Swift, the system was trained on a virtual environment that simulated the real-world drone racing track.
• Inertial measurement unit (IMU): This is a sensor that measures the acceleration, speed, and orientation of an object. The IMU data was used by Swift to track its position and velocity in the real world.
• Visual odometry: This is a technique for estimating the position and orientation of an object from its camera images. The visual odometry data was used by Swift to track its position and velocity in the real world, and to avoid obstacles.

The Swift AI system is still under development, but it has the potential to revolutionize the sport of drone racing. It could also be used to develop autonomous drones for a variety of other applications, such as search and rescue, delivery, and security.

The article also mentions that the human pilots were better able to adapt to changing conditions than the Swift AI system. This is an important point to consider, as it shows that AI systems are not yet perfect. They can be fooled by unexpected changes in the environment, and they can sometimes make mistakes. However, as AI systems continue to develop, they are likely to become more robust and adaptable.

Overall, the development of the Swift AI system is a significant milestone in the field of AI. It shows that AI is now capable of mastering complex physical tasks that were once thought to be the exclusive domain of humans. This has the potential to revolutionize a variety of industries, and it could also lead to the development of new and innovative applications for AI.

summary

The best of humanity are defeated by AI in a real-world sport for the first time. They are currently defeating us in competitive sports: University of Zurich World-champion drone racers were defeated head-to-head by an AI A frightening “Deep Blue” moment has just occurred in high-speed drone racing, as an autonomous AI created by researchers at the University of Zurich repeatedly forced three world champion-level pilots to eat its dust while displaying incredible precision in dynamic flying. You will be aware of how much ability, speed, precision, and dynamic control it requires if you have ever witnessed a professional drone racing from the FPV perspective. It’s difficult to comprehend how a human brain can process information that quickly and adapt to changing circumstances while viewing on-board footage from events like the Isle of Man TT or Formula One from the driver’s perspective.

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