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I know this nit-picks but... it didn't "learn" anything.

It progressively improved its algorithm using a series of feedback sessions.

It _improved_ but it didn't _learn_. Someone pre-programmed basic control and feedback parameters.

Don't get me wrong—this is still amazing and has useful applications. But can we please stop calling this improvement/refining/tuning process "learning"?

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Author here. We did not pre-programm basic control or feedback parameters. The trained policy (fully connected neural network) takes the state estimate (position, orientation, linear and angular velocity) and a history of the previous actions as the input and directly outputs the PWM/RPM setpoint (uint16 that sets the pwm interval).

The code is open source, you can verify it yourself ;)

https://github.com/arplaboratory/learning-to-fly

Do you have any idea what level of effort would be involved to modify the quadcopter? Or, say, use a different one? Would it require a lot of experience developing physical models for simulation?

Yeah my thoughts exactly, but you know how it goes in robotics, the buzzier the word the better!

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