Why would neural networks be a particularly good framework for "embodied AI"? There is this claim around that the brain's cognitive capabilities are tightly linked to the way it processes sensorimotor information and that, in this or a similar sense, our intelligence is &quot;embodied&quot;. Lets assume, for the sake of argument, that this claim is correct (you may think the claim is too vague to even qualify for being correct, that it's &quot;not even false&quot;. If so, I would love to hear your ways of fleshing out the claim in such a way that it's specific enough to be true or false).
Then, since arguably at least chronologically in our evolution, most of our higher-level cognitive capabilities come after our brain's way of processing sensorimotor information, this brings up the question: what is it about the way that our brains function that make them particularly suitable for the processing of sensorimotor information? What makes our brains'  architecture particularly suitable for being an information processing unit inside a body?
This is my first question. And what I'm hoping for are answers that go beyond the a fortiori reply &quot;Our brain is so powerful and dynamic, it's great for any task, and so also for processing sensorimotor information&quot;.
My second question is basically the same, but, instead of the human brain, I want to ask for neural networks. What are the properties of neural networks that make them particularly suitable for processing the kind of information that is produced by a body?
Here are some of the reasons why people think neural networks are powerful:

The universal approximation theorem (of FFNNs)
Their ability to learn and self-organise
Robustness to local degrading of information
Their ability to abstract/coarse-grain/convolute features, etc.

While I see how these are real advantages when it comes to evolution picking its favorite model for an embodied AI, none of them (or their combination) seems to be unique to neural networks. So, they don't provide a satisfactory answer to my question.
What makes a neural network a more suitable structure for embodied AI than, say, having a literal Turing machine sitting inside our head, or any other structure that is capable of universal computation?
For instance, I really don't see how neural networks would be a particularly natural choice for dealing with geometric information. But geometric information is pretty vital when it comes to sensorimotor information, no?
To my mind the essential reason why neural networks and the brain are powerful is that they create a hierarchical model of data or of the world. If you ask why that makes them powerful, well, that's just the structure of the world. If you are stalked by a wolf, it's not like its upper jaw will attack you frontally, while his lower jaw will attack you from behind. If you want to respond to the threat with a feasible computational effort, you'll have to treat the wolf as one entity. Providing these kinds of entities or concepts from the raw bits and bytes of input is what a hierarchical representation does. 

Now, this is quite intuitive for sensory information: lashes, iris, eyebrow make up an eye, eyes, nose and mouth make up a face and so on. What is less obvious, is the fact that motor control works exactly the same way! Only in reverse. If you want to lift your arm, you'll just lift it. But for your brain to actually realise this move, the high level command has to be broken down into precise signals for every muscle involved. And this is done by propagating the command down the hierarchy. 

In the brain these two functions are strongly intertwined. You use constant sensory feedback to adapt your motor control and in many cases you'd be incapable of integrating your stream of sensory data into a coherent representation if you didn't have the additional information of what your body is doing to change that stream of data. Saccades are a good example for that. 

Of course this doesn't mean that our cognitive functions are dependent on the processing of sensorimotor information. I would be surprised if a pure thinking machine wouldn't be possible. There is however a specific version of this "embodied intelligence hypothesis" that sounds plausible to me: 

Creating high level cognitive concepts with unsupervised learning is a really difficult problem. Creating high level motor representation might be significantly easier. The reason is that there is more immediate useful feedback. I have been thinking about how to provide a scaffolding for the learning of a hierarchy of cognitive concepts and one thing I could imagine is that high level cognitive concepts basically hitch a ride with the motor concepts. Just think of what a pantomime can express with movement alone.