Grasp Point Estimation using Simulator-Generated Datasets Including Pose Information

We develop a system that automatically generates training datasets for object recognition models using a simulator.In this study, we incorporate pose information into the dataset.Using this system, we develop a method for estimating grasp points for objects that are difficult for robots to grasp, selecting a toy airplane as the target object.Three specific points are assigned to the object: the front, center, and back.In the grasp point estimation process, the center point is designated as the grasp point.The robot's arm achieves an appropriate grasp by moving perpendicularly to the line connecting the front and back points toward this grasp point.