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[-] jj4211@lemmy.world 2 points 2 days ago

It's not about the machinery struggling with gloves, it's just the difficulty of capturing the data when a human really needs to touch/feel.

Like that 'friction test', the point is when they use machine learning, they want to feed all the inputs and outputs from the human operator. They don't have a good way of capturing 'feel' (you can have a camera to capture what eyes see, you can have microphones to capture what the human hears). Similar on human inputs, for a car, they can instrument the pedals and steering and they pretty much got it all. If a human hand is doing something, well, if the human is local they might be able to make a glove to capture all that, but our sense of feeling can be fouled so easily that trying to instrument it tends to ruin it. If the human is remote then you can just get controller style inputs.

[-] partofthevoice@lemmy.zip 1 points 2 days ago

Yeah, this should be researched. How would you produce an analog signal to represent touch semantics?

You ever heard of that paper, *what the frog eye tells the frog brain” (Or something like that) ? It covers what’s effectively semantic information, not bits of data, getting sent over the central nerve. Edges, flashes, …

I wonder what the skin might tell the brain.

I also wonder if there would be a way to bounce a static field, or something, off the surface… maybe vibrate the material, etc,,, to approximate these sort of details.

this post was submitted on 26 Aug 2026
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