• If you haven't created a catkin_workspace, do so

cd ~
mkdir your_ws
cd your_ws
mkdir src

• Download the packages in the src folder

cd ~
git clone https://github.com/roboticslab-uc3m/roboespas

• Modify .bashrc to use this workspace and set the ROS_IP and ROS_WORKSPACE env variables. Change the IP for your IP (check it using ifconfig).

export ROS_WORKSPACE=~/your_ws    #edit for your workspace
export ROS_MASTER_URI=http://192.168.1.53:11311   #edit for your IP
export ROS_IP=192.168.1.53  #edit for your IP

• Compile the workspace

roscd
cd src/
catkin_make

• Re-generate messages (jump to 3 if you have not generate matlab messages before)

1. Delete previously created messages. In Matlab prompt

rosgenmsg('/home/user/your_ws/src/roboespas')

Open javaclasspath.txt file that you are linked and delete all the content. Delete /home/user/your_ws/src/roboespas/matlab_gen folder.

2. Restart Matlab

3. Generate messages

rosgenmsg('/home/user/your_ws/src/roboespas')

Follow the instructions (add to javaclasspath.txt the files given, and add to path the folder) 4. Restart Matlab.

Torque command example

Option 1: Control iiwa_gazebo without using iiwa_command action server

This way Matlab will send joint torque values point by point.

• Execute in a terminal: roslaunch iiwa_command iiwa_command_gazebo.launch. Although you won't use iiwa_command action server, you need to use the launcher to load config parameters.

• In Matlab, modify the IP in the file TorqueControlExample.m (lines 6-7) inside the TorqueControlExample folder, then play the script and watch IIWA in Gazebo simulator moving.

Option 2:Control iiwa_gazebo using iiwa_command action lib

This way Matlab will send joint torque values all together as a trajectory.

• Execute in a terminal: roslaunch iiwa_command iiwa_command_gazebo.launch.

• In Matlab, modify the IP in the file TrajectoryTorqueControlExample.m (lines 6-7) inside the TorqueControlExample folder, then play the script and watch IIWA in Gazebo simulator moving.

Other files used

Dataset trajectories:

https://drive.google.com/drive/folders/1mvBHLigwaf9ykC70j0tyhNbo1mdIiZAx

Extreme Learning Machine

https://es.mathworks.com/matlabcentral/fileexchange/69812-extreme-learning-machine-for-classification-and-regression