Researchers train autonomous drones using cross-modal simulated data

To fly autonomously, drones need to understand what they perceive in the environment and make decisions based on that information. A novel method developed by Carnegie Mellon University researchers allows drones to learn perception and action separately. The two-stage approach overcomes the “simulation-to-reality gap,” and creates a way to safely deploy drones trained entirely on simulated data into real-world course navigation.