qq_37595932的博客

基于深度学习的机器人抓取检测 采用康奈尔大学抓取数据集 We consider the problem of detecting robotic grasps in an RGB-D view of a scene containing objects. In this work, we apply a deep learning approach to solve this problem, which avoids time-consuming hand-design of features. This presents two main challenges. First, we need to evaluate a huge number of candidate grasps. In order to make detection fast and robust, we present a two-step cascaded system with two deep networks, where the top detections from the first are re-evaluated by the second. The first network has fewer features, is faster to run, and can effectively prune out unlikely candidate grasps. The second, with more features, is slower but has to run only on the top few detections. Second, we need to handle multimodal inputs effectively, for which we present a method that applies structured regularization on the weights based on multimodal group regularization. We show that our method improves performance on an RGBD robotic grasping dataset, and can be used to successfully execute grasps on two different robotic platforms.

多轴机械臂仿真软件。

wpf 3d tools工具箱、 可视化3d模型

计算机视觉教程 深度学习 卷积神经网络 。

kuka机器人模型 wpf框架 控制demo 四元数 欧拉角 。

首先安装vs2017，建立一个项目，通过nuget包安装tensorflowsharp，在引用中添加tensorflow，using tensorflow；