A Learning-based Robotic Bin-picking with Flexibly Customizable Grasping Conditions

摘要

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 conditions for any object can be flexibly customizable in the simulated environment to improve the real-world grasping actions. (2) Sensor input (RGB image) is directly regressed to grasping actions by using convolutional processing. Owing to these features, the system using the proposed method can grasp objects with geometric variations, semi-transparent objects, and objects with a biased center of gravity. Experimental results on a real robot system show that the proposed method exhibits a high grasp success rate for four types of objects with different physical and geometric properties as well as additional constraints of grasping condition.