Advantages of training Neural Networks based on analytic success criteria

What is the reason to train a Neural Network to estimate a task’s success (i.e. robotic grasp planning) using a simulator that is based on analytic grasp quality metrics?

Isn’t a perfectly trained NN going to essentially output the same probability of task success as the analytic grasp quality metrics that were used to train it? What benefits does this NN have with respect to just directly using said analytic grasp quality metrics to determine whether a certain grasp candidate is good or bad? Analytic metrics are by definition deterministic, so I fail to understand the reason for using them to train a NN that will ultimately output the same result.

This approach is used in high-caliber works like the Dex-Net2 from Berkeley Automation. I am rather new to the field and the only reason I can think of is computational efficiency in production?