Automatic Hanging Point Learning from Random Shape Generation and Physical Function Validation

Abstract

The purpose of this paper is the robotic hanging manipulation of an object of various shapes that is not limited to a specific category. To achieve this, we propose a method that allows the estimator to learn many different shapes with hanging points without any manual annotation. A random shape generator using GAN solves the limitation of the number of 3D models and can handle objects of various shapes. In addition, hanging is repeated in the dynamics simulation, and hanging points are automatically generated. A large amount of training data is generated by rendering random-textured objects with hanging points in the random simulation environment. A deep neural network trained with these data was able to estimate hanging points of an unknown category object in the real world and achieved hanging manipulation by a robot.