That's interesting to think about. I'm not familiar with the literature on this but I'm 100% sure there's some interesting work on it (and related fields such a

That's interesting to think about. I'm not familiar with the literature on this but I'm 100% sure there's some interesting work on it (and related fields such as distributed and federated learning). I guess the simplest solution would be "centralized" - periodically aggregate all the raw data from all robots, train a model with all the data, redistribute the model. In that case there wouldn't be any "individuality", but (maybe again, by analogy with evolution) one could think it'd be advantageous to have some. But even if all the models all the same, the robots might be different types and operate in different environments, which raises issues of generalizability, transferability and specialization. Either way the centralized would have some scaling problems, naturally - some way to transfer/aggregate experience (possibly "peer to peer") without resorting to training from raw data then becomes attractive, and I'm sure something people are working on. It does turn out that at least in some recent LLMs, weights appear to be sort of linear and people have been using that to merge them with fairly naive methods with good results.