Dominik Koch
Papers
1
Total Citations
5
H-Index
1
About
Dominik Koch is a pioneering researcher at the intersection of robotics, remanufacturing, and artificial intelligence, with a primary focus on advancing automated visual inspection systems. His most notable contribution is the development of a reinforcement learning (RL) approach to solve the View Planning Problem (VPP) in robotic inspection, a critical challenge for quality control in remanufacturing processes. In his highly cited 2025 paper, Koch significantly advanced prior frameworks by integrating a detailed robotic simulation environment with essential trajectory modules, enabling robots to autonomously determine optimal camera positions for defect detection. This work, which has already garnered 5 citations shortly after publication, bridges the gap between theoretical RL algorithms and practical industrial applications. Koch's research directly addresses the growing need for efficient, adaptive inspection in circular economy contexts, where remanufactured components require rigorous quality assurance. By combining reinforcement learning with realistic robot simulation, he has created a scalable solution that reduces manual programming effort while improving inspection accuracy. His innovative approach positions him at the forefront of intelligent manufacturing, offering a compelling pathway toward fully autonomous quality control systems in remanufacturing facilities.
Research Focus
Key Achievements
Top Papers
- 1