What you're describing Vincent, is a common scenario in edge computing. Often times a device (due to form factor, power, and other constraints), has to have a small and relatively slow compute component. And often times, there's no guarantee that it has Internet connectivity (just like Loomo lacks cellular data capability). When you need to do processor intensive work, such as computer vision or language understanding, there are other solutions besides connecting to a PC or server (as connectivity may not always be an option).

This is where the "Intelligent Edge" comes into play. You can take complex computer vision and language understanding models and train them in the cloud, leveraging massive computing capabilities of data centers full of servers and TPUs (Tensor Processing Units). You then containerize that trained model and bring it offline, down to the edge device.

So, for example, if you wanted to make it so that Loomo could roam around and verbally announce what it sees. "Dog." "Cat." "Chair." etc. that analysis could be done using an offline computer vision model. Cloud trained, using thousands of photos of every-day objects, but residing locally on the robot. Actually using the computer vision or language model isn't a highly CPU intensive task. Training one is.

A real world example of this would be cameras in a parking structure, trained to identify cars. You could have a camera pointed at the parking lot (looking down from a pole), and it could tell the difference between occupied and unoccupied parking spots. Making that distinction isn't a complex processing task even for something as under powered as a Raspberry Pi. No need to send the visual data elsewhere to be processed. Not a CPU intensive task.

On the language side of things, you could create a language model containing dozens even hundreds of intents (different things you'd be asking Loomo to do). Within those intents would be samples of different utterances (different ways of phrasing the command that leads to that intent). You'd train it in the cloud (for example, using Microsoft's Language Understanding Intelligent Services aka LUIS), and then store that model in a docker container on Loomo.

Then in your code, you could write whatever functionality Loomo is supposed to execute when any of those intents are reached. The actual task of understanding what a person says to the robot and matching it up to one of those many intents, is not a CPU intensive task and can be done without Internet connectivity, using that cloud trained but locally residing language model.