A Learning-based Robotic Bin-picking with Flexibly Customizable Grasping Conditions
Hiroki Tachikake,Wataru Watanabe,Hiroki Tachikake,Wataru Watanabe
A practical robotic bin-picking system requires a high grasp success rate for various objects. Also, the system must be capable of coping with various constraints and their changes flexibly. To resolve these issues, this study proposes a novel deep learning-based method that exploits a simulator to generate desired grasping actions. The features of this method are as follows: (1) Grasping conditio...
