Object-Agnostic Suction Grasp Affordance Detection in Dense Cluster Using Self-Supervised Learning.docx
This addresses robotic picking in warehouse logistics, but is incremental as it builds on existing CNN and self-supervised learning approaches.
The paper tackles the problem of suction grasp detection in dense object clusters for warehouse logistics by proposing a two-step CNN-based method with self-supervised learning. In experiments, it achieved 95% picking accuracy for known objects and 90% for unseen objects with similar geometry over 1500 picks.
In this paper we study grasp problem in dense cluster, a challenging task in warehouse logistics scenario. By introducing a two-step robust suction affordance detection method, we focus on using vacuum suction pad to clear up a box filled with seen and unseen objects. Two CNN based neural networks are proposed. A Fast Region Estimation Network (FRE-Net) predicts which region contains pickable objects, and a Suction Grasp Point Affordance network (SGPA-Net) determines which point in that region is pickable. So as to enable such two networks, we design a self-supervised learning pipeline to accumulate data, train and test the performance of our method. In both virtual and real environment, within 1500 picks (~5 hours), we reach a picking accuracy of 95% for known objects and 90% for unseen objects with similar geometry features.