feat: Get hostname and ip from env

README.rst

@@ -28,12 +28,12 @@ This project extends the previous work presented by `Schneider et al. 2024 <http
 
 .. image:: ../diagram/diagram.png
 
-Three instances of Franka Emika Robots are present within the RWTH Aachen University Cluster of Excellence Internet of Production. 
+Three instances of Franka Emika Robots are present within the RWTH Aachen University Cluster of Excellence Internet of Production.\ 
 To generate meaningful knowledge from data gather on these three instances--situtated at the Chair for Laser Technology (LLT), Chair for Machine Tools (WZL MT) and the Institute for Textile Technology (ITA)--automated data storage and management is required. 
 Thus, automated data generation procedures have been implemented and are conducted on each robot instances. Data is automatically tagged with meta data on pushed to NRW RDS by means of RWTH Aachen University `meta data platform coscine <https://coscine.rwth-aachen.de/p/iop-ws-a.iii-fer-wwl-demo-80937714/>`_.
 This platform both provides the generated data (Trajectory Data resource) as well as imporant metadata such as the source code used for data generation and the universal unique identfier of the robot instance.
-This data is used to train an inverse dynamics model of the robotic systems. Agents registered to a weight and biases sweep server continously get send new model hyperparameter sets from the sweep server, pull up to date data from NRW RDS and push well performing models back to NRW RDS (Inverse Dynamics Models resource)
-Train models may be used at the participating institutions fully embracing the Data-to-knowledge pipeline demonstrated within the World Wide Lab.
+This data is used to train an inverse dynamics model of the robotic systems. Agents registered to a weight and biases sweep server continously get send new model hyperparameter sets from the sweep server, pull up to date data from NRW RDS and push well performing models back to NRW RDS (Inverse Dynamics Models resource).
+Trained models are part of the AI-Toolbox of Workstream A.III and may be used at the participating institutions fully embracing the Data-to-knowledge pipeline demonstrated within the World Wide Lab.
 
 .. TODO Update this according to acutal deep learning implementation
 

franka_lock_unlock/README.rst

@@ -7,11 +7,13 @@ Franka Lock Unlock
    This package is forked from: https://github.com/jk-ethz/franka_lock_unlock
 
 .. Attention:: 
-   You will need to change the password to your password.
+   You will need to change the password, username and IP.
 
 .. code-block:: bash
 
    export FRANKA_PASSWORD="your_password_here"
+   export FRANKA_IP="your_IP_here"
+   export FRANKA_USERNAME="your_username_here"
 
 ******************
 Automated Behavior

franka_lock_unlock/entrypoint.sh

@@ -2,4 +2,4 @@
 # Source your completion script
 source /app/franka_lock_unlock/frankalockunlock-completion.bash
 # Execute the Python script, replacing the current shell process with Python
-exec frankalockunlock "$FRANKA_PASSWORD" --wait --request --persistent --unlock --lock --fci
+exec frankalockunlock "$FRANKA_PASSWORD" --hostname "$FRANKA_IP" --username "$FRANKA_USERNAME" --wait --request --persistent --unlock --lock --fci