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Creating a URDF robot model from scratch for Cool1000 robotic manipulator

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In this post i explain the process of creation of a URDF robot model for Cool1000 arm. At the end of this post you should be able to understand the concept of links, joints, types of joints, physical and collision properties to be added for simulation in Gazebo. A detailed tutorials for creation of URDF files can be found in the urdf_tutorials . Joints are the moving positions in a robotic arm that can move in a rotational or translational motion. Hence, the joints could be specified in a URDF as one among the types fixed, revolute, prismatic or continuous. A revolute joint (usually for motors) has rotational motion with the joint limits specified, a prismatic joint has translational motion with the joint limits specified in meters, a continuous joint has rotational motion with no joint limits being specified. Links refer to the rigid body that connects two joints. Collision properties refer to the collision meshes to be added into the model so as to detect a collision betwee

Dex-Net installation procedure on system installed with Anaconda and ROS

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This blog explains installation procedure for Dex-Net on a system installed with Anaconda and ROS. If you don't have or willing to uninstall Anaconda and work with the Dexnet then you can follow the installation procedure as explained in berkeley automation documentation . I wanted to retain Anaconda, Jupyter installation on my system, hence i've documented the procedure so that it will help people who are in a similar situation. Most part of this blog is similar to the procedure provided in berkeley automation blog, it has been added with few workarounds for the problems that you might come across. Dex-Net package is a Python API for working with the dex-net database. It lets you manage HDF5 database , supports creation of databases with custom datasets of your own objects. The HDF5 databases consists of 3D object models, parallel-jaw grasps, and grasp robustness metrics used in the Dex-Net 2.0 paper . Download the datasets GQ-CNN Training Datasets   Pre-t