Your SDLs sound advanced, but how often do they actually fail in practice? If a robotic arm jams or a fluidic system clogs, how do you recover without human intervention?
You praise Bayesian Optimization, but does it ever get trapped in bad local optima? How do you ensure it doesn’t just regurgitate biases from flawed training data?
How many ‘autonomous’ runs still require manual troubleshooting? If the AI suggests a bizarre reaction, who double-checks it’s not nonsense?
Your tech is impressive, but where are the warts? Without hard numbers on failure rates, bias, and real-world scalability, this feels more like a manifesto than a roadmap.