Jonas Dirr
Papers
8
Total Citations
31
H-Index
4
About
Jonas Dirr is a robotics researcher advancing the automation of complex industrial assembly tasks, with a primary focus on deformable linear objects (DLOs) such as cables and wires. His work addresses one of manufacturing's most persistent challenges: enabling robots to reliably perceive, grasp, and manipulate non-rigid materials that lack fixed geometry. Dirr's major contributions span the full perception-to-action pipeline, including developing cut-paste image generation techniques for instance segmentation in robotic picking (9 citations), introducing novel evaluation metrics specifically designed for grasping deformable objects (4 citations), and creating object-oriented grasp planning strategies for bin picking of DLOs (2 citations). His research on automated cable assembly (4 citations) and intelligent viewpoint generation for robot vision systems (5 citations) has direct industrial relevance, tackling the transition from manual to automated processes in battery module interconnection and electrical connector mating. Dirr's work is characterized by its practical orientation, combining deep learning-based perception with mechanical design innovations such as automatically generated gripper jaws for sensitive connector mating. His growing citation record reflects the increasing importance of flexible automation in addressing labor shortages and efficiency demands in modern manufacturing.
Research Focus
Key Achievements
Top Papers
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- 3Concept for Robot-Based Cable Assembly Regarding Industrial Production4 citations · 2022
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