损失函数优化: -二元交叉熵损失( l_{bce,k} ):用于接触抓取成功预测。 -6-DoF抓取损失( l_{add-s} ):计算抓取器关键点在真实和预测姿态下的加权最小平均距离。 -抓取宽度预测损失( l_{width} ):加权多标签二元交叉熵损失。 总损失公式:l=αlbce,k+βladd−s+γlwidth 6. 抓取执行 抓取姿态转换:...
Therefore, we propose an end-to-end network that efficiently generates a distribution of 6-DoF parallel-jaw grasps directly from a depth recording of a scene. Our novel grasp representation treats 3D points of the recorded point cloud as potential grasp contacts. By rooting the full 6-DoF ...
Contact-GraspNet: Efficient 6-DoF Grasp Generation in Cluttered Scenes Martin Sundermeyer, Arsalan Mousavian, Rudolph Triebel, Dieter Fox ICRA 2021 paper, project page, video Installation This code has been tested with python 3.7, tensorflow 2.2, CUDA 11.1 Create the conda env conda env create -...
Volumetric grasping network: Real-time 6 dof grasp detection in clutter. Conference on Robot Learning, 2020. 3 [3] Ivan Dryanovski, William Morris, and Jizhong Xiao. Multi- volume occupancy grids: An efficient probabilistic 3D map- ping mo...
Contact-GraspNet can directly predict a 6-DoF grasp distribution from a raw scene point cloud. However, to obtain object-wise grasps, remove background grasps and to achieve denser proposals it is highly recommended to use (unknown) object segmentation [e.g. 1, 2] as preprocessing and then ...
Contact-GraspNet: Efficient 6-DoF Grasp Generation in Cluttered Scenes Martin Sundermeyer, Arsalan Mousavian, Rudolph Triebel, Dieter Fox ICRA 2021 paper, project page, video Installation This code has been tested with python 3.7, tensorflow 2.2, CUDA 11.1 Create the conda env conda env create -...
robot vision; deep learning; Industry 4.0; robot grasp; defect detection; YOLO1. Introduction Industry 4.0, also known as the Fourth Industrial Revolution, is a conceptual framework that is redefining the way industries operate, manufacture, and interact with the global economy. It emerges as a ...