3 papers
cs.LG2026
Trajectory-Level Data Augmentation for Offline Reinforcement Learning
Tobias Schmähling, Matthias Burkhardt, Tobias Windisch
We propose a data augmentation method for offline reinforcement learning, motivated by active positioning problems. Particularly, our approach enables the training of off-policy mo…
cs.RO2025
Active Alignments of Lens Systems with Reinforcement Learning
Matthias Burkhardt, Tobias Schmähling, Pascal Stegmann +2
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-wo…
cs.LG2025
LineFlow: A Framework to Learn Active Control of Production Lines
Kai Müller, Martin Wenzel, Tobias Windisch
Many production lines require active control mechanisms, such as adaptive routing, worker reallocation, and rescheduling, to maintain optimal performance. However, designing these…