Scalable and Time-Efficient Bin-Picking for Unknown Objects in Dense Clutter

The task of fully automated picking of novel bin objects that are placed in a densely cluttered pile poses a significant challenge. It becomes even more challenging if the objects are of various shapes, sizes, colors, and textures. Generally, grasp planning for a given scene begins with sampling several grasp poses, which are then evaluated to determine the final grasp pose for the robot action. With the increment in clutter level in the bin, the fraction of graspable locations becomes smaller. Hence, for a scalable grasp pose planning strategy, the pose sampling method should be intelligent enough to find suitable grasp regions in a time-efficient manner, irrespective of the amount of clutter and the workspace size. In this paper, we present a scalable robotic bin-picking method (SE-RoB) that performs equally well amidst the increasing level of clutter in the scene. In a real-world challenging bin-picking setup, our proposed method has shown significantly better performance (
$13\%$
improvement) compared to the state-of-the-art methods in terms of grasp success rate in a time-efficient manner (6 Hz inference speed).
Note to Practitioners
—Picking objects from a cluttered pile can be challenging, especially when dealing with objects of different shapes, sizes, colors, and textures. We have created a new method called SE-RoB that can help robots pick up unknown objects from a pile reliably and in a time-efficient manner. In our experiments, we tested this method in real-world situations where the pile of objects was densely cluttered and had many different types of objects. SE-RoB worked much better compared to the state-of-the-art methods. Additionally, this method works at around 6 Hz inference speed, which means it is suitable for real-life scenarios such as warehouse automation. We believe that SE-RoB can be a valuable tool for practitioners who are looking to automate their bin-picking processes using robots.