But then he goes on to vision, where the form that goes into vision processing today is an array of pixels. That's not much preprocessing. That's pretty much what existed at the image sensor. Older approaches to vision processing had feature extractors, with various human-defined feature sets. That was a dead end. Today's neural nets find their own features to extract.
Touch sensing suffers from sensor problems. A few high-detail skin-like sensors have been built. Ruggedness and wear are a big problem.
Consider, though, a rigid tool such as an end wrench. Humans can feel out the position of a bolt with an end wrench, get the wrench around the bolt, and apply pressure to tighten or loosen a nut. Yet the total information available is position plus six degrees of freedom of force. If the business end of your tool is rigid, the amount of info you can get from it is quite limited. That doesn't mean you can't get a lot done. (I fooled around with this idea pre-LLM era, but didn't get very far.) That's at least a way to get warmed up on the problem.
Here's a video of a surgeon practicing by folding paper cranes with small surgical tools.[1] These are rigid tools, so the amount of touch information available is limited. That's a good problem to work on.