They allowed the connectome to control a lego robot [1] and it showed some signs of working around obstacles (robot backs up when it encounters a wall). Sounds simple, but now we can say that only 300 neurons are needed to be able to react to the environment in this way, which is a huge stepping stone in understanding.
Ok, but what functional knowledge did we gain? Not "run this neural net, and an interesting thing happens". How are those 300 neurons implementing interesting motor behavior? What behavior are they really implementing? What's the algorithm, what's its goal?
The connectome model only addresses the third and lowest levels of Marr's analysis of a cognitive/biological system.
1. Computational: What does the system aim to do? What problem does it solve?
2. Algorithmic: How does the system solve or approximately solve that problem? How does it accomplish its purpose as a part of the organism?
3. Implementation: How are cells and/or organs put together to implement that solution?
You can have a very accurate picture of (3), and still lack any solid knowledge about (2) or (1). You can also sometimes have a clear picture of (2) and (3), but overgeneralize and wind up with a near-tautological theory for (1) -- that's the accusation being thrown at certain parts of theoretical neuroscience today.
You talk like the project is over, there is still a lot to learn and we aren't going to figure out everything immediately. A simulation provides an amazing test bed for ideas though; now you can see it working, you can pause it, change things, do experiments, and so on.
And functionally, what did that tell us?