About us

Bin-Picking at Fraunhofer IPA


Our Bin-Picking solution

We provide solutions for the singulation of chaotically stored objects.


Deep Grasping

Our research project »Deep Grasping« aims to increase the autonomy and performance of bin-picking solutions using deep learning and simulation.



Kilian Kleeberger, Markus Völk, Marius Moosmann, Erik Thiessenhusen, Florian Roth, Richard Bormann, and Marco F. Huber, "Transferring Experience from Simulation to the Real World for Precise Pick-And-Place Tasks in Highly Cluttered Scenes," in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Caesars Forum, Las Vegas, NV, USA, 2020. ArXiv

Kilian Kleeberger and Marco F. Huber, "Single Shot 6D Object Pose Estimation," in IEEE International Conference on Robotics and Automation (ICRA), Palais des Congrès de Paris, France, 2020. ArXiv DOI

Kilian Kleeberger, Christian Landgraf, and Marco F. Huber, "Large-Scale 6D Object Pose Estimation Dataset for Industrial Bin-Picking," in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), The Venetian Macao, Macau, China, 2019. ArXiv DOI



Kilian Kleeberger, Richard Bormann, Werner Kraus, and Marco F. Huber, "A Survey on Learning-Based Robotic Grasping," Current Robotics Reports, vol. 1, no. 4, pp. 239–249, 2020. DOI

Mohamed El-Shamouty, Kilian Kleeberger, Arik Lämmle, and Marco Huber, "Simulation-driven machine learning for robotics and automation," tm – Technisches Messen, vol. 86, no. 11, pp. 673–684, 2019. DOI