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Machine learning really almost nothing in common with most types of human learning. The only type of learning that has similarities is associative learning (think Pavlovs dogs studies).

The human learning situation you describe works quite differently, though: The student sees either the device alone or the teacher using the device to demonstrate its functionality. This is the moment most of the actual learning happens: The student creates internal concepts of the device and its interactions with the surroundings. As a result the student can immediately use the decive more or less correctly. What's left is just some finetuning of parameters like movement vectors, movement speed, applied pressure etc.

If the student would work like ML, it would: hold the device in random ways, like on the cord, the disc, the actual grip. After a bunch of right/wrong responses she would settle on using the grip mostly. Then (or in parallel) the student would try out random surfaces to use the device on: the own hand (wrong), the face of the teacher (wrong), the wall (wrong), the wood (right), the table (wrong) etc. After a bunch of retries she would settle on using the device on the wood mostly.

It's easy to overlook the actual cognitive accomplishments of us humans in menial tasks like this one because most of it happens unconsciously. It's not the "I" that is creating the cognitive concepts.