“Swift’s perception system and physics model assumes that the appearance of the environment and its physics are both consistent with what was observed during training,” says Scaramuzza. The other problem is that Swift trains on a specific setup and if conditions change – for example the wind changes direction – all its learning may be wasted. The neural network which navigates through the gates is trained specifically for that layout. “The current system only works for drone racing and for a specific racing track of which you perfectly know the map,” says Scaramuzza. This is far more challenging outdoors where there are changes of illumination, wind gusts and other variables to contend with.Īlso, Swift has to learn the course ahead of time to work out its flight path. Swift relies on having reliable information on the speed, location and orientation of the drone in real time. “There are many open challenges that prevent the current system being deployed in warfare scenarios,” he says. Scaramuzza notes that Swift could not easily be turned into a weapon. A super-skilled AI might push that rate far above 70%, negate any risk of jamming and enable fleets of smart FPV drones to attack simultaneously without human operators.įPV racing drones with RPG warheads assembled by Ukraine's Escadrone group Escadrone Slowing the Killer Robots Ukrainian sources say the training takes around a month to achieve proficiency, and many people fail the course.įPV success rates appear to vary wildly, with different sources citing 20%, 30%, 50% or 70% - much appears to depend on the exact situation, the presence of jamming, and the skill of the pilot.

They are cheap enough to use against individual footsoldiers and can dive into trenches. A $400 FPV with the warhead from an RPG RPG rocket launcher can knock out a tank, personnel carrier or artillery piece from several miles away, or chase down and destroy a truck traveling at high speed. That has significant implications for warfare, where FPV drones with explosive warheads are tremendously effective human-guided weapons. The implication is that AI-controlled FPVs can outperform humans. He notes that, as with AlphaGo, Swift was able to use moves - in this case flight trajectories - which the human champions did not even think were possible.

“The AI is superhuman because it discovers and flies the best maneuvers, also it is consistent and precise, which humans are not,” says Scaramuzza. Scaramuzza says this is the first time that an autonomous mobile robot achieved world-champion-level performance in a real-world competitive sport. Swift won several races against each of the human pilots and recorded the fastest track time. The human pilots had a week to practice on the course before going head-to-head against Swift. FPV pilots use video goggles and require fast reflexes and good training Regina Sablotny

Drone mobile system