Papers
3
Total Citations
113
H-Index
3
About
Marcel Albus is a leading researcher in industrial robotics, focusing on the intersection of safety, productivity, and reconfigurable manufacturing. His work addresses two critical bottlenecks in modern production: safe human-robot collaboration (HRC) and efficient assembly line reconfiguration. In his highly cited 2020 paper, "Towards Safe Human-Robot Collaboration Using Deep Reinforcement Learning" (62 citations), Albus pioneered the use of deep reinforcement learning to dynamically balance safety constraints with productivity, reducing the need for overly conservative safety measures and lengthy risk assessments. This work has significant implications for flexible, human-centric factory floors. More recently, Albus has tackled the practical challenge of brownfield assembly lines—existing facilities with pre-installed resources. His 2023 paper (35 citations) and 2024 follow-up (16 citations) introduce novel optimization frameworks, including a modified genetic algorithm, for resource reconfiguration in constrained environments. By moving beyond the ideal of greenfield design, Albus provides scalable solutions for real-world manufacturing, enabling rapid adaptation to new products without costly overhauls. His contributions are shaping the next generation of agile, safe, and cost-effective industrial automation.
Research Focus
Key Achievements
Top Papers
- 1Towards Safe Human-Robot Collaboration Using Deep Reinforcement Learning62 citations · 2020
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