Efficient End-to-End Detection of 6-DoF Grasps for Robotic Bin Picking

Understand

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 prediction of 6-DoF grasps on diverse and unknown objects have shown promising progress.
• However, existing approaches only consider a single ground truth grasp orientation at a grasp location during training and therefore can only predict limited grasp orientations which leads to a reduced number of feasible grasps in bin picking with restricted reachability.
• In this paper, we propose a novel approach for learning dense and diverse 6-DoF grasps for parallel-jaw grippers in robotic bin picking.