Papers
14
Total Citations
495
H-Index
11
About
Arne Wahrburg is a robotics researcher whose work centers on force estimation, force control, and human-robot interaction for robotic manipulators. He is perhaps best known for pioneering sensor-free approaches to contact force estimation, developing Kalman filter-based methods that extract Cartesian contact forces and torques directly from motor currents and joint signals — eliminating the need for costly dedicated force-torque sensors. His 2017 paper on motor-current-based force estimation has garnered 181 citations, establishing it as a landmark contribution to the field, while his earlier foundational work on the topic has accumulated nearly 100 additional citations. Beyond estimation, Wahrburg has made notable contributions to force control itself, proposing model predictive control frameworks for admittance control and iterative learning strategies for assembly in unstructured environments. His research extends into the safety-critical domain of human-robot collaboration, where he has applied neural networks and data-efficient machine learning methods to classify and distinguish intended from unintended physical contact situations — a critical capability for next-generation collaborative robots. He has also contributed to the precise modeling of friction in strain wave gears, addressing a fundamental challenge in compact robotic systems. Across these areas, his work reflects a consistent focus on making robotic manipulation more capable, safe, and practically deployable in industrial settings.
Research Focus
Key Achievements
Top Papers
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- 3MPC-based admittance control for robotic manipulators42 citations · 2016
- 4Contact force estimation for robotic assembly using motor torques33 citations · 2014
- 5Using Neural Networks for Classifying Human-Robot Contact Situations29 citations · 2019
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- 8Data-Efficient Online Classification of Human-Robot Contact Situations16 citations · 2020
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