Fraunhofer Institute for Manufacturing Engineering and Automation IPAPublications
Conference:
Kilian Kleeberger, Jonathan Schnitzler, Muhammad Usman Khalid, Richard Bormann, Werner Kraus, and Marco F. Huber, "Precise Object Placement with Pose Distance Estimations for Different Objects and Grippers," in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Prague, Czech Republic, 2021. ArXiv DOI
Kilian Kleeberger, Florian Roth, Richard Bormann, and Marco F. Huber, "Automatic Grasp Pose Generation for Parallel Jaw Grippers," in 16th International Conference on Intelligent Autonomous Systems (IAS-16), Singapore, 2021. ArXiv DOI
Kilian Kleeberger, Markus Völk, Richard Bormann, and Marco F. Huber, "Investigations on Output Parameterizations of Neural Networks for Single Shot 6D Object Pose Estimation," in IEEE International Conference on Robotics and Automation (ICRA), Xi'an, China, 2021. ArXiv DOI
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 DOI
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
Journal:
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