Tobias Meisen
Papers
14
Total Citations
204
H-Index
8
About
Tobias Meisen is a prominent researcher at the intersection of artificial intelligence, industrial automation, and smart manufacturing, with particular expertise in reinforcement learning for robotic systems and Cyber-Physical Production Systems (CPPS). His most influential contribution, "Motion Planning for Industrial Robots using Reinforcement Learning" (2017, 78 citations), laid important groundwork for applying adaptive AI techniques to flexible manufacturing in the context of Industry 4.0, demonstrating how robots could learn motion planning autonomously rather than relying on rigid, manually programmed routines. Subsequent works refined this vision further, introducing Bézier curve-based smooth trajectory generation and direct sensory input processing via convolutional neural networks, collectively accumulating dozens of additional citations. Meisen has also advanced the field of transfer learning in industrial contexts, addressing the critical gap between machine learning's theoretical promise and its real-world manufacturing adoption. Beyond robotics and AI, he has contributed meaningfully to engineering education, pioneering the use of mixed reality and virtual experiments to enhance remote laboratory experiences. Across his body of work, Meisen consistently bridges cutting-edge computational methods with practical industrial challenges, making him a notable voice in intelligent automation research.
Research Focus
Key Achievements
Top Papers
- 1Motion Planning for Industrial Robots using Reinforcement Learning78 citations · 2017
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- 3Insights and Example Use Cases on Industrial Transfer Learning18 citations · 2022
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- 8Interoperability in Smart Automation of Cyber Physical Systems11 citations · 2016
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