Papers
18
Total Citations
769
H-Index
10
About
Kai Huebner is a robotics researcher whose work has fundamentally advanced the field of robot grasping and manipulation, with a particular focus on enabling autonomous systems to perceive and interact with objects in unstructured, real-world environments. His most influential contributions center on shape approximation techniques for grasp planning, most notably his development of minimum volume bounding box decomposition — a method that allows robots to efficiently approximate complex 3D object geometries for reliable grasp selection, earning over 174 citations. Building on this foundation, Huebner pioneered probabilistic and graphical model approaches to task-constrained grasping, demonstrating how robots can reason about not just *whether* to grasp an object, but *how* to do so according to specific task requirements — work that has collectively accumulated hundreds of citations across multiple publications. His research also spans active vision systems, tactile-based grasp stability assessment, and Bayesian Network learning for robotic applications. Huebner's contributions are notable for bridging perception, machine learning, and physical manipulation, making him a significant figure in the development of intelligent service robots capable of operating meaningfully alongside humans.
Research Focus
Key Achievements
Top Papers
- 1
- 2Selection of robot pre-grasps using box-based shape approximation110 citations · 2008
- 3
- 4Learning task constraints for robot grasping using graphical models109 citations · 2010
- 5Task-Based Robot Grasp Planning Using Probabilistic Inference78 citations · 2015
- 6
- 7Grasping known objects with humanoid robots: A box-based approach36 citations · 2009
- 8TOWARDS GRASP-ORIENTED VISUAL PERCEPTION FOR HUMANOID ROBOTS23 citations · 2009
- 9
- 10