Papers
10
Total Citations
79
H-Index
6
About
Kai Furmans is a leading researcher in robotic bin picking, agile production systems, and intralogistics, with a focus on enabling automation in unstructured and uncertain environments. His major contributions include the development of MetaGraspNetV2 (27 citations), an all-in-one dataset that advances fast and reliable robotic bin picking through object relationship reasoning and dexterous grasping, addressing one of the most challenging tasks in robotics. He also pioneered the MotorFactory Blender add-on (15 citations) for generating large datasets of small electric motors, facilitating machine learning for automatic disassembly in remanufacturing. Furmans’ work on agile production systems using learning robots for remanufacturing (7 citations) and sample-efficient sim-to-real domain adaptation via MetaMVUC (6 citations) demonstrates his impact on bridging simulation and real-world applications. His gesture-controlled transportation robot “FiFi” (3 citations) and studies on energy modeling for automated guided vehicles (6 citations) highlight his contributions to sustainable intralogistics. With over 70 total citations across his top papers, Furmans’ research is pivotal for advancing flexible, learning-based automation in manufacturing and logistics.
Research Focus
Key Achievements
Top Papers
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- 6FiFi – Controlling an AGV by detection of gestures and persons6 citations · 2024
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