Robotic Grasping Strategies Based on Classification of Orientation State of Objects

Robotic grasping has been studied for years, but still has lots of room for improvement due to its requirement of sufficient robustness to achieve a high success rate. This paper proposes grasping strategies that produce reliable grasping poses without human labeling. All the training and testing processes are performed in a simulation environment. We then further evaluate the quality of the grasping strategy produced by the system. High success rate result shows its potential of application in industrial production lines, helping the robot arms perform high-quality grasping with picking or similar tasks.