4 papers
Towards a Multi-Embodied Grasping Agent
Roman Freiberg, Alexander Qualmann, Ngo Anh Vien +1
Multi-embodiment grasping focuses on developing approaches that exhibit generalist behavior across diverse gripper designs. Existing methods often learn the kinematic structure of…
Diffusion for Multi-Embodiment Grasping
Roman Freiberg, Alexander Qualmann, Ngo Anh Vien +1
Grasping is a fundamental skill in robotics with diverse applications across medical, industrial, and domestic domains. However, current approaches for predicting valid grasps are…
Efficient End-to-End Detection of 6-DoF Grasps for Robotic Bin Picking
Yushi Liu, Alexander Qualmann, Zehao Yu +5
Bin picking is an important building block for many robotic systems, in logistics, production or in household use-cases. In recent years, machine learning methods for the predictio…
Pseudo-Labeling and Contextual Curriculum Learning for Online Grasp Learning in Robotic Bin Picking
Huy Le, Philipp Schillinger, Miroslav Gabriel +2
The prevailing grasp prediction methods predominantly rely on offline learning, overlooking the dynamic grasp learning that occurs during real-time adaptation to novel picking scen…