# Active Alignments of Lens Systems with Reinforcement Learning

arXiv: https://arxiv.org/abs/2503.02075 · HTML: https://mlsanity.com/paper/2503.02075/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in RL for robot locomotion and manipulation, one of 15 directions in Deep reinforcement learning methods.
- Builds on 10 of its 35 references that are in the map.
- First posted on arXiv 3 Mar 2025.

## Placed in

- Deep reinforcement learning methods / [RL for robot locomotion and manipulation](https://mlsanity.com/deeprl/rl-for-robot-locomotion-and-manipulation/)

## Builds on (most cited first, 10 of 10)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Dream to Control: Learning Behaviors by Latent Imagination](https://mlsanity.com/paper/1912.01603/) (2019)
- [Curriculum Learning for Reinforcement Learning Domains: A Framework and Survey](https://mlsanity.com/paper/2003.04960/) (2020)
- [Why Generalization in RL is Difficult: Epistemic POMDPs and Implicit Partial Observability](https://mlsanity.com/paper/2107.06277/) (2021)
- [Recent Advances in Bayesian Optimization](https://mlsanity.com/paper/2206.03301/) (2022)
- [Data-Efficient Reinforcement Learning with Probabilistic Model Predictive Control](https://mlsanity.com/paper/1706.06491/) (2017)
- [Transfer Learning with Gaussian Processes for Bayesian Optimization](https://mlsanity.com/paper/2111.11223/) (2021)
- [A Survey on Reproducibility by Evaluating Deep Reinforcement Learning Algorithms on Real-World Robots](https://mlsanity.com/paper/1909.03772/) (2019)
- [Interferobot: aligning an optical interferometer by a reinforcement learning agent](https://mlsanity.com/paper/2006.02252/) (2020)
- [Sequential Model-Based Ensemble Optimization](https://mlsanity.com/paper/1402.0796/) (2014)

## Built on by (most cited first, 3 of 3)

- [A Framework for Closed-Loop Robotic Assembly, Alignment and Self-Recovery of Precision Optical Systems](https://mlsanity.com/paper/2603.21496/) (2026)
- [Towards Real-world Lens Active Alignment with Unlabeled Data via Domain Adaptation](https://mlsanity.com/paper/2601.03718/) (2026)
- [Trajectory-Level Data Augmentation for Offline Reinforcement Learning](https://mlsanity.com/paper/2605.13401/) (2026)

## Abstract (arXiv)

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.