Model-Guided Local Bayesian Optimization for Tuning of Interpretable Controllers in Injection Molding
The episode discusses a paper proposing a method to automatically optimize interpretable controllers during injection molding by using a physics model combined with Gaussian Processes. Hosts discuss how this hybrid approach balances finding good performance with safety by monitoring metrics like maximum cost and cumulative worsening, aiming for safer, self-tuning control laws.