SE(3)における把持姿勢検出のための効率的で正確な候補生成
Efficient and Accurate Candidate Generation for Grasp Pose Detection in SE(3)
3D空間での把持姿勢検出において、高品質な把持候補を効率的に生成する新しい手法を提案し、主要な3D把持検出ベースラインを大幅に上回る性能を示した論文。
著者: Andreas ten Pas, Colin Keil, Robert Platt
分類: cs.RO
原文アブストラクト
Grasp detection of novel objects in unstructured environments is a key capability in robotic manipulation. For 2D grasp detection problems where grasps are assumed to lie in the plane, it is common to design a fully convolutional neural network that predicts grasps over an entire image in one step. However, this is not possible for grasp pose detection where grasp poses are assumed to exist in SE(3). In this case, it is common to approach the problem in two steps: grasp candidate generation and candidate classification. Since grasp candidate classification is typically expensive, the problem becomes one of efficiently identifying high quality candidate grasps. This paper proposes a new grasp candidate generation method that significantly outperforms major 3D grasp detection baselines. Supplementary material is available at https://atenpas.github.io/psn/.
