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arXiv · 1906.02995

Object-Agnostic Suction Grasp Affordance Detection in Dense Cluster Using Self-Supervised Learning.docx

Mingshuo Han·Wenhai Liu.·Zhenyu Pan·Teng Xue·Quanquan Shao·Jin Ma·Weiming Wang
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Abstract

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.

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Mingshuo Han, Wenhai Liu., Zhenyu Pan, Teng Xue, Quanquan Shao, Jin Ma, Weiming Wang. 2019-06-07. Object-Agnostic Suction Grasp Affordance Detection in Dense Cluster Using Self-Supervised Learning.docx. https://arxiv.org/abs/1906.02995

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