Provided is a grip generation technique for a robot to pick up a part. A database of entity or surface models is provided for all objects and grippers to be evaluated. A gripper is selected and random initialization is performed, wherein random objects and poses are selected from an object database. An iterative optimization calculation is then performed in which hundreds of grips are calculated for each part using the surface contact between the part and the gripper, and sampling is performed for grip diversity and global optimization. Finally, a physical environment simulation is performed in which the grip of each part is mapped to the simulated heap of objects in the bin scene. Grip points and proximity directions from physical environment simulations are then used to train neural networks for grip learning in real-world robotic operations, where simulation results are associated with camera depth image data to identify high quality grips.
Efficient data generation for grip learning of common grippers