Delft University Team Develops MonoRace: AI Drone Beats Human Champions at Over 100 km/h Using Single Camera
Researchers from Delft University of Technology developed the MonoRace system, which defeated human pilots in a real race at 102 km/h using only a single camera and no GPS or pre-mapped data.
A research team from Delft University of Technology in the Netherlands has unveiled a breakthrough result: their artificial intelligence drone system, named "MonoRace," defeated professional human pilots in the 2025 Abu Dhabi Autonomous Racing League. The study was published on October 14, 2026, in the journal Science Robotics.
The core advantage of the MonoRace system lies in its minimal hardware dependencies. It operates without GPS, external motion capture systems, stereo cameras, or pre-loaded track maps. Instead, it relies solely on a single front-facing rolling-shutter camera, an inertial measurement unit (IMU), and a small onboard computer to perceive the environment and control flight in real time. In the final race, the system completed two laps of an 11-gate course in 16.56 seconds, reaching a top speed of approximately 102 km/h, surpassing all competing human champions.
This performance was achieved through reinforcement learning-based training. Given the prohibitive cost of millions of trial-and-error flights in the real world, researchers conducted extensive training in simulation environments. Guido de Croon, a professor at Delft University of Technology and co-author of the paper, noted that the system learned to fly from scratch. Initial training focused on obstacle avoidance and basic navigation, gradually progressing to higher speeds and complex maneuvers. To enhance generalization, the training process randomized variables such as drone weight, voltage, drag, and sensor noise, ensuring the neural network could adapt to physical differences across various hardware units.
In actual deployment, MonoRace was mounted on a lightweight drone weighing 966 grams, with its internal micro-neural network issuing motor control commands 500 times per second. This achievement not only demonstrates AI's capability for real-time decision-making in high-speed dynamic environments but also provides significant empirical evidence for the development of "physical AI": robots can achieve precise motion control exceeding human capabilities even under constrained sensor and computational resources.