Learning suction graspability considering grasp quality and robot reachability for bin-picking

TL;DR

This paper introduces a geometric-based grasp quality and reachability evaluation method for bin-picking, trained via synthesized images, which improves grasp planning efficiency and achieves high picking speed.

Contribution

It proposes a novel intuitive geometric grasp quality metric and incorporates reachability assessment, enhancing grasp detection and reducing planning time.

Findings

The geometric grasp quality metric is competitive with physically-inspired models.

Incorporating reachability reduces motion planning computation time.

The system achieves 560 pieces per hour in bin-picking tasks.

Abstract

Deep learning has been widely used for inferring robust grasps. Although human-labeled RGB-D datasets were initially used to learn grasp configurations, preparation of this kind of large dataset is expensive. To address this problem, images were generated by a physical simulator, and a physically inspired model (e.g., a contact model between a suction vacuum cup and object) was used as a grasp quality evaluation metric to annotate the synthesized images. However, this kind of contact model is complicated and requires parameter identification by experiments to ensure real world performance. In addition, previous studies have not considered manipulator reachability such as when a grasp configuration with high grasp quality is unable to reach the target due to collisions or the physical limitations of the robot. In this study, we propose an intuitive geometric analytic-based grasp quality evaluation metric. We further incorporate a reachability evaluation metric. We annotate the pixel-wise grasp quality and reachability by the proposed evaluation metric on synthesized images in a simulator to train an auto-encoder--decoder called suction graspability U-Net++ (SG-U-Net++). Experiment results show that our intuitive grasp quality evaluation metric is competitive with a physically-inspired metric. Learning the reachability helps to reduce motion planning computation time by removing obviously unreachable candidates. The system achieves an overall picking speed of 560 PPH (pieces per hour).