Active Alignments of Lens Systems with Reinforcement Learning
This addresses a critical problem in camera manufacturing by improving alignment efficiency and reducing manual intervention, though it appears incremental as it applies RL to a specific domain.
The paper tackles the challenge of aligning lens systems in camera manufacturing by proposing a reinforcement learning approach that operates in pixel space, eliminating the need for expert-designed concepts, and shows it surpasses other methods in speed, precision, and robustness.
Aligning a lens system relative to an imager is a critical challenge in camera manufacturing. While optimal alignment can be mathematically computed under ideal conditions, real-world deviations caused by manufacturing tolerances often render this approach impractical. Measuring these tolerances can be costly or even infeasible, and neglecting them may result in suboptimal alignments. We propose a reinforcement learning (RL) approach that learns exclusively in the pixel space of the sensor output, eliminating the need to develop expert-designed alignment concepts. We conduct an extensive benchmark study and show that our approach surpasses other methods in speed, precision, and robustness. We further introduce relign, a realistic, freely explorable, open-source simulation utilizing physically based rendering that models optical systems with non-deterministic manufacturing tolerances and noise in robotic alignment movement. It provides an interface to popular machine learning frameworks, enabling seamless experimentation and development. Our work highlights the potential of RL in a manufacturing environment to enhance efficiency of optical alignments while minimizing the need for manual intervention.