• Last week, work continued on the ROS framework. There were problems with the inverse kinematics solver, because the Jaccobian matrix is generated incorrectly by the used Python packages. For the time being we will only work with the Impedance Effort Controller from Franka.
• Furthermore, the CPS Lecture Repository was created and the CoppeliaSim simulation file and the Python script to operate it were programmed. In addition, a description of how to work with the simulation was written.
• Further details were discussed in the ROS framework. The framework should work with different simulation programs (CoppeliaSim, IssacSim). The goal is to implement an RL environment, so that an initial solution can be given with the pen on the tablet. This initial solution (trajectory) should be used as motion primitives for the RL. The weights of the MPs are to be learned. Furthermore, it should be possible to manipulate the MP in real time, so that the user can further refine the trajectory in the learning process. The Gym library will be used for the evaluation. For the experiment, the lathe and the milling machine will be used (button press, lever operation, etc.). 
• A research on related work shall be done, especially if such systems/frameworks have already been developed with ROS2. 
• Not all sensors, HW devices, robots, etc. need to be implemented by myself, but there need to be a template for the different parts, so every one can extend the frame work.
• XBox Controller: The Xbox controller is to be used for teleoperation for the robots, as well as the possibility for recording experiments should be implemented. E.g. fixation of a configuration, so that the same experiment can be recorded again and again from the same configuration or the possibility to separate a recording by pressing a button, so that the experiment has to be started only once and not for every run again.

Three main projects:

1. ROS CPS Framework
2. Reimplementation GNN for Motion Planning
3. Literature research

The development of a ROS/ROS2 framework to control the CPS robots.
Furthermore, an interface for the use of a tablet is to be programmed in ROS2. On the tablet trajectories can be designed (movement primitives) which can be changed directly with the pen. For this a simple GUI is needed for the display and manipulation of trajectories as well as the display of the robot in 3D in real time. As well as the use of the CPS glove.
This program is intended to be an application of active learning methods, especially for shared and preference learning.
Finally, a connection will be made to the methods of Probabilistic Inference, see GNN for Motion Planning.
If possible, a comprehensive platform including ROS Mobile should be developed.

Thema: Probabilistic Motion planning

Goal: Model based planning via message passing and Kalman Smoothing(KS)
(planning as Inference)

Experiments:

1) 1-D cart+sensor-simulation – proof of concept( physics+dynamics sim, planning and KS)
2) 2-D robot sim as kinematic chain with beam sensor and orientations constraints
3) CopelliaSim (+ ROS with own planning algorithm)
4) Franka Panda

Roadmap:
1) Toy task – 1-D physics and dynamics simulation of the cart
2) Toy task – baseline planning (positioning via Euler-distance)
3) Toy task – implementing Kalman Smoother
4) Toy task 2 – 2-D Robot arm (2 links with endeffector)
5) Toy task 2 – Planning in Cartesian space – Inverse kinematic + Kalman Smoother
6) CoppeliaSim of Franka with 3-D printed tool – simple tool guiding (orientation!!)
7) Real world testing with Franka
optional: ROS Implementation of the planning algoritm

Literature: Bayesian Modeling and Machine Learning (Chapter 23, p 493 – KS)