AI guides single-camera drone through hallways it's never seen before

Researchers at the University of Colorado recently demonstrated a system that helps robots figure out the direction of hiking trails from camera footage, and scientists at ETH Zurich described in a January paper a machine learning framework that aids four-legged robots in getting up from the ground when they trip and fall. But might such AI perform just as proficiently when applied to a drone rather than machines planted firmly on the ground? A team at the University of California at Berkeley set out to find out. In a newly published paper on the preprint server Arxiv ("Generalization through Simulation: Integrating Simulated and Real Data into Deep Reinforcement Learning for Vision-Based Autonomous Flight"), the team proposes a "hybrid" deep reinforcement learning algorithm that combines data from both a digital simulation and the real world to guide a quadcopter through carpeted corridors. "In this work, we … aim to devise a transfer learning algorithm where the physical behavior of the vehicle is learned," the paper's authors wrote. "In essence, real-world experience is used to learn how to fly, while simulated experience is used to learn how to generalize."