Learning Suction Graspability Considering Grasp Quality and Robot Reachability for Bin-Picking

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).

Analytic and Data-Driven Force Prediction for Vacuum-Based Granular Grippers

“…This could also be implemented online during the handling process in real-time in order to evaluate different objects for graspability. For this task, Jiang et al [16] propose intuitive suction grasp analytic metrics based on point clouds for small suction grippers. These metrics could be based on physical properties such as the smoothness of a surface.…”

Modular End-Effector System for Autonomous Robotic Maintenance & Repair

“…Therefore, the real-world dataset generation process is not well suitable for mobile manipulation platforms. While much research has been conducted to improve the performance of one gripper type [29]- [34], such as parallel jaw grippers or suction cup grippers, there is a lack of studies [26], [27], [35], [36] that investigate the effects of combining different gripper types or different gripper dimensions in a single grasping task. A comprehensive dataset and pipeline containing multiple gripper types and dimensions would not only allow researchers to evaluate the effectiveness of different combinations of gripper types but could also provide insight into the best practices for the design and utilization of multiple grippers for robotic grasping.…”

Multiple-Object Grasping Using a Multiple-Suction-Cup Vacuum Gripper in Cluttered Scenes

“…Most studies infer the single-object grasp point for a gripper with only a single suction cup using direct or indirect methods. Direct methods [9][10][11] use deep convolutional neural networks to directly infer the grasp point, while indirect methods [12][13][14] first infer the affordance map, which is a pixel-wise map indicating the graspability score for a single-cup vacuum gripper at each pixel, and then find the optimal grasp point in the map. Given that the affordance map contains all possible grasp points for a single suction cup, if all cups in a vacuum gripper have the same geometry (e.g., cup radius) and dynamics (e.g., suction force limit and friction coefficient), then we can search for a gripper pose where the center positions of at least two of the cups are located at non-zero pixels in the affordance map and satisfy the conditions described in Section 4 for grasping multiple objects or an object with a large surface area.…”

“…In this study, we propose an affordance-map-based grasp planner for a multiplesuction-cup vacuum gripper to grasp multiple objects or grasp an object with a large surface area. We propose a 3D convolution-based method, which takes advantage of the suction affordance map inferred by our prior work, suction graspability U-Net++ (SG-U-Net++) [14], to search for a gripper pose capable of grasping multiple objects or an object with a large surface area. Furthermore, unlike the control of a jaw gripper in which all fingers of the gripper are usually controlled to open or close simultaneously, the suction cups need to be controlled separately.…”

“…Validation of the grasp planner on previous affordance datasets, including Suction FCN [12], SuctionNet-1Billion [13], and SG-U-Net++ [14].…”