You can generate functional robot control policies by modeling the distribution of neural network weights themselves, and the learned weights naturally encode the robot's physical structure regardless of how they were trained.
This paper shows that neural networks controlling humanoid robots can be generatively modeled directly through their weights using diffusion models. The authors compress robot control network parameters into a latent space and train a diffusion transformer to model the distribution of these latents.