Alexander Cebulla
Papers
3
Total Citations
28
H-Index
2
About
Alexander Cebulla is a robotics researcher at the forefront of intelligent automation for remanufacturing and disassembly. His work centers on agile production systems, where he addresses the critical challenge of enabling robots to operate under high uncertainty—particularly in the context of remanufacturing, where product conditions are unknown. Cebulla’s key contributions lie in simulation-to-reality (sim2real) transfer learning for point cloud segmentation, a technique that overcomes the difficulty of generating and annotating real-world data for deep learning. His 2023 paper on this topic has already garnered 19 citations, reflecting its practical impact. He has also advanced robotic assembly sequence planning (RASP) with a novel approach, “Assembly-by-Disassembly,” that minimizes assembly path lengths—work that pushes beyond mere feasibility to optimize efficiency. With additional publications in agile production systems and ongoing contributions to Industry 4.0, Cebulla is shaping the future of autonomous manufacturing, making complex, uncertain processes more adaptable and efficient.
Research Focus
Key Achievements
Top Papers
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