Rapid adaptation of deep learning teaches drones to survive any weather
Drones, or autonomous flying machines, must master real-world weather and wind patterns in order to be genuinely helpful. At the moment, drones are either piloted by humans using remote controls or are flown in controlled environments with no wind. Although drones have been trained to fly in formation in wide spaces, those flights are typically carried out in perfect circumstances. However, in order for drones to autonomously carry out necessary but commonplace tasks like airlifting injured drivers from a car accident or delivering packages, they must be able to adjust to changing wind conditions in real time. This is known as "rolling with the punches" in meteorological terms. To address this issue, a team of Caltech engineers created Neural-Fly, a deep-learning technique that enables drones to adapt to novel and unanticipated wind conditions in real time by just altering a few crucial parameters. A paper that was released on May 4 in Science Robotics describes Neural-Fly. S...