After a large number of virtual flights, the learning AI was then ready to take on real-world flying. It was flown in difficult situations where there were branches and other obstacles, or narrow openings to negotiate, with poor visibility and limited computing power.... [+]The UZH pilot was flow against two state-of-the-art drone piloting programs.
The latency of the two other systems was 65 and 19 milliseconds for a sample task, the UZH system managed 10 milliseconds. While this may not be consistent for every piloting challenge, it shows the potential of the approach. Scaramuzza notes that this is the first time such a system has been trained in a completely simulated environment and then used its learning in the physical world. While it could be useful for training delivery drones and air taxis to negotiate the urban landscape in all weathers and lightning conditions – cheaply and without the risk of accidents — there may also be far greater potential.
The technique suggests that guided machine learning in virtual environments could be a way of quickly and cheaply training all sorts of robotic systems. For example, a robotic system for