Kilian Kleeberger
Fraunhofer Institute for Manufacturing Engineering and Automation
Papers
5
Total Citations
348
H-Index
4
About
Kilian Kleeberger is a leading researcher at the intersection of machine learning and robotics, with a primary focus on vision-based robotic grasping and manipulation. His most impactful work, the 2020 survey "A Survey on Learning-Based Robotic Grasping," has garnered 274 citations, establishing itself as a definitive reference for model-free grasping approaches. Kleeberger’s contributions extend to addressing real-world industrial challenges, such as improving the robustness of random bin picking by using machine learning to detect and avoid entanglements in complex-geometry workpieces—a problem that plagues automated manufacturing. He also explores simulation-driven machine learning for robotics and automation, emphasizing how digital environments can accelerate adaptation to mass personalization in production. His work on automatic grasp pose generation for parallel jaw grippers and real-time instance detection with fast incremental learning further demonstrates his commitment to practical, deployable solutions. Through his research, Kleeberger bridges the gap between theoretical machine learning advances and tangible robotic applications, making his work essential reading for students and engineers seeking to understand how learning-based approaches are transforming industrial automation.
Research Focus
Key Achievements
Top Papers
- 1A Survey on Learning-Based Robotic Grasping274 citations · 2020
- 2Simulation-driven machine learning for robotics and automation37 citations · 2019
- 3
- 4Automatic Grasp Pose Generation for Parallel Jaw Grippers9 citations · 2022
- 5Real-time Instance Detection with Fast Incremental Learning4 citations · 2021