Simon Roggendorf
Papers
4
Total Citations
104
H-Index
3
About
Simon Roggendorf is a leading researcher at the intersection of robotics, artificial intelligence, and smart manufacturing, with a core focus on enabling flexible and adaptive production systems for Industry 4.0. His work primarily addresses the challenges of motion planning, human-robot collaboration, and automated assembly in high-variance environments. Roggendorf’s most impactful contribution is his pioneering application of reinforcement learning to industrial robot motion planning (78 citations), which demonstrated how RL can make production systems both robust and economically efficient. He has further advanced the field by developing methods for consolidating product lifecycle information in human-robot collaborative assembly (16 citations), and by tackling the Sim-to-Real transfer problem for compliance-based robotic assembly operations (8 citations). His research on simulation-based planning of grasping processes has also contributed to reducing programming effort in end-of-line assembly. Through his work, Roggendorf is helping to bridge the gap between cutting-edge AI techniques and practical industrial applications, making him a key figure in the ongoing transformation of manufacturing towards more intelligent, collaborative, and adaptive systems.
Research Focus
Key Achievements
Top Papers
- 1Motion Planning for Industrial Robots using Reinforcement Learning78 citations · 2017
- 2
- 3
- 4Simulation-Based Planning of Grasping Processes for Assembly Robots2 citations · 2018