Researchers use simulation to teach drones to catch objects

AI researchers from the Allen Institute of Artificial Intelligence and the University of Washington have trained a drone agent with a box on top to catch a range of 20 objects in a simulated environment. In trials, the drone had the lowest catch success rate with toilet paper (0%) and the highest with toasters (64.4%). Other objects included alarm clocks, heads of lettuce, books, and basketballs. Overall, the system's success rate in catching objects outpaces two variations of a current position predictor model for 3D spaces, as well as a frequently cited reinforcement learning framework proposed in 2016 by Google AI researchers. For the study, a launcher threw each object two meters (6.5 feet) toward a drone agent.