“Autonomous navigation in environments where conditions are constantly changing is restricted to very low speeds,” explains Matthias Müller, Lead of Embodied AI Lab at Intel Labs. “This makes drones unable to operate efficiently in real-world situations where something unexpected may block their path and time matters.”
That’s obviously a big impediment to safely rolling out drones for commercial use. The solution seems to be harnessing the decision-making abilities of expert pilots to train drones to function autonomously.
“In partnership with the University of Zurich, we were able to show how a drone trained exclusively in simulation by imitating an expert pilot is able to perform in challenging real-world scenarios and environments that weren’t used during the training of the convolutional network,” says Müller.
“The trained autonomous drone was able to fly through previously unseen environments, such as forests, buildings and trains, keeping speeds up to 40 km/h, without crashing into trees, walls or any other obstacles – all while relying only on its onboard cameras and computation.”
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