Marcel Zeilinger
Papers
1
Total Citations
2
H-Index
1
About
Marcel Zeilinger is a robotics researcher whose work focuses on bridging the gap between simulation and real-world automation, particularly in industrial logistics and manufacturing. His primary research areas include computer vision, robotic manipulation, and synthetic data generation for perception systems. Zeilinger’s most notable contribution is his pioneering approach to occlusion-robust object recognition, where he demonstrated that neural networks trained exclusively on synthetic data can effectively generalize to real-world industrial environments—a breakthrough that significantly reduces the need for costly manual data annotation. His 2023 paper on automated pallet handling, which has garnered early citations, showcases how this technique enables robots to reliably detect and manipulate pallets even under challenging visual occlusions common in warehouse settings. By developing methods that allow robots to learn from artificially generated scenes, Zeilinger is helping to democratize automation for small and medium-sized enterprises. His work represents a critical step toward flexible, vision-guided robotic systems that can adapt to diverse production and transport scenarios without extensive reprogramming or real-world data collection.
Research Focus
Key Achievements
Top Papers
- 1