60 citations · 64 across the 25 of their papers we have counts for
4 papers · 1 filter
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…
Efficient Off-Policy Learning for High-Dimensional Action Spaces
Fabian Otto, Philipp Becker, Ngo Anh Vien +1
Existing off-policy reinforcement learning algorithms often rely on an explicit state-action-value function representation, which can be problematic in high-dimensional action spac…