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You may use this video for research and teaching purposes. Please cite the Chair of Cyber-Physical-Systems or the corresponding research paper. 

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You are interested in working with modern robots or want to understand how such machines ‘learn’?

If so, this bachelor thesis will enable you to dig into the fascinating world of robot learning. You will implement and apply modern machine learning algorithms in Python, Matlab or C++/ROS. 

Your learning or control algorithm will be evaluated in cyber-physical-systems. Find out which theses are currently supervised and offered. 

Links and Resources

• Winter Semester: 150.570 MUOnline .
• Sommer Semester: 150.570 MUOnline .
• Latex Templates for the thesis document.
• Instructions on how to write a thesis with us.

Location & Time

• Location: Laboratories at the Chair of Cyber-Physical-Systems.
• Dates: Regular, typ. weekly meetings, with your supervisor(s). 

Learning objectives / qualifications

• Students will work on controlling, modeling and simulating Cyber-Physical-Systems and autonomously learning systems.
• Students understand and can apply advanced model learning and reinforcement  techniques to real world problems.
• Students learn how to write scientific reports.

Literature

• The Probabilistic Machine Learning book by Univ.-Prof. Dr. Elmar Rueckert. 
• Bishop 2006. Pattern Recognition and Machine Learning, Springer. 
• Barber 2007. Bayesian Reasoning and Machine Learning, Cambridge University Press. 
• Murray, Li and Sastry 1994. A mathematical introduction to robotic manipulation, CRC Press. 
• B. Siciliano, L. Sciavicco 2009. Robotics: Modelling,Planning and Control, Springer.
• Kevin M. Lynch and Frank C. Park 2017. MODERN ROBOTICS, MECHANICS, PLANNING, AND CONTROL, Cambridge University Press.

You are interested in working with modern robots or want to understand how such machines ‘learn’?

If so, this project will enable you to dig into the fascinating world of robot learning.

The course provides a structured and well motivated overview over modern techniques and tools which enable the students to define learning problems in Cyber-Physical-Systems. 

Links and Resources

• MUOnline 
• Latex Template for the Assignments

Location & Time

• Location: Laboratories at the Chair of Cyber-Physical-Systems.
• Dates: Regular, typ. weekly meetings, with your supervisor(s). 

Learning objectives / qualifications

• Students get a practical experience in working, modeling and simulating Cyber-Physical-Systems.
• Students understand and can apply advanced model learning and reinforcement  techniques to real world problems.
• Students learn how to write scientific reports.

Literature

• The Probabilistic Machine Learning book by Univ.-Prof. Dr. Elmar Rueckert. 
• Bishop 2006. Pattern Recognition and Machine Learning, Springer. 
• Barber 2007. Bayesian Reasoning and Machine Learning, Cambridge University Press. 
• Murray, Li and Sastry 1994. A mathematical introduction to robotic manipulation, CRC Press. 
• B. Siciliano, L. Sciavicco 2009. Robotics: Modelling,Planning and Control, Springer.
• Kevin M. Lynch and Frank C. Park 2017. MODERN ROBOTICS, MECHANICS, PLANNING, AND CONTROL, Cambridge University Press.

This post provides information on whom to contact depending on your purpose.

Note that this post is continuously updated to keep the contact persons up-to-date. 

If you discover out-dated information, please contact our secretary. 

Bettina Sokol

• Bewerbungen Wissenschaftliches Personal. 

Bettina.Hotter@unileoben.ac.at

Für Absagen: xyz@unileoben.ac.at

Karin Taxacher

• Bewerbungen Nicht-Wissenschaftliches Personal. 

karina.taxacher@unileoben.ac.at

Sabine Fluch

• CISCO telephone stations for new employees. 

sabine.fluch@unileoben.ac.at

Kathrin Moitzi

• Transponder. 

kathrin.moitzi@unileoben.ac.at

Julia Schmidbauer

• Knowledge- and Technology Transfer & Business Partnerships

julia.schmidbauer@unileoben.ac.at

Moodle Courses

• MU Online Helpdesk, markus.hauser@unileoben.ac.at.

SAP GUI MAC

• ZID, Walter Kräftner, walter.kraeftner@unileoben.ac.at

This post discusses how to develop a low cost treadmill with a closed-loop feedback controller for reinforcement learning experiments.

MATLAB and JAVA code is linked.

Code & Links

• Pre-build java library
• Source code of the developer version

The Treadmill

• Get a standard household treadmill Samples
• Note: It should work with a DC-Motor, otherwise a different controller is needed!

The Controller and the Distance Sensor

• Pololu Jrk 21v3 USB Motor Controller with Feedback or stronger (max. 28V, 3A)
• Comes with a Windows Gui to specify the control gains
• Sharp distance sensor GP2Y0A21, 10 cm – 80 cm or similar
• USB cable
• Cable for the distance sensor
• Power cables for the treadmill
• Controller User Guide by Polo

The Matlab Interface

• Get the java library  build or the developer version, both from Sept 2015 created by E. Rueckert.
• Run the install script installFTSensor.m (which add the jar to your classpath.txt)
• Check the testFTSensor.m script which builds on the wrapper class MatlabFTCL5040Sensor (you need to add this file to your path)

LEGO EV3 for Robotic Tasks

We have five EV3 sets and use them for studying robot control, motion planning and visual navigation from depth images. 

We use our GitHup LEGO Python project for our developments. 

Tactile Sensing

Several special purpose sensors including depth image cameras (shown in the center in the image), IMUs, accelerometers, gyroscopes, sonic sensors (two are shown in the image), etc. can be connected to the EV3 brick. 

The EV3 systems can be used to explore neural sensor fusion approaches, embedded computing implementations and classical mobile robotics tasks.  

• Details to our LEGO Python GitHub project

Videos

https://vimeo.com/501651310https://vimeo.com/374166607

Human-inspired, Adult-size Dexterous Robot Hand

We use a adult-sized robot hand for learning grasping and object manipulation skills. The hand is mounted on our FRANKA EMIKA Panda robot. 

The hand has 19 degrees-of-freedom and uses 8 smart actuators for precise control (actuators contained inside the unit).

Under actuated design aims to provide the right balance between fine control and conformance to the shape of the objects.

Tactile Sensing

All actuators provide real time control and feedback of position, speed and current measurement (with direction), enabling inference of applied force.

Additional data including actuator temperature, (over)load status and PWM, a Palm ToF Distance sensor and optional Capacitive pads at the back of the palm complete the sensor array.

We also have five 3-axis force-torque sensors (FTS) (shown in the image) attached to each finger tip. The FTS measure contact force and shear forces with a resolution of 1mN / 0.1g.

• E-Manual of the RH8 hand.
• ROS Interface description of the RH8 hand.
• E-Manual of the 3Axis F/T Sensors 
• Details to our FRANKA ROS GitHub project.
• Details on the robot hardware can be found on the SEED Robotics page.

Videos

• Research videos using the robot will be presented here. 

Publications

2020

Xue, H.; Boettger, S.; Rottmann, N.; Pandya, H.; Bruder, R.; Neumann, G.; Schweikard, A.; Rueckert, E. Sample-Efficient Covariance Matrix Adaptation Evolutional Strategy via Simulated Rollouts in Neural Networks Proceedings Article In: International Conference on Advances in Signal Processing and Artificial Intelligence (ASPAI’ 2020), 2020. Links | BibTeX @inproceedings{Xue2020, title = {Sample-Efficient Covariance Matrix Adaptation Evolutional Strategy via Simulated Rollouts in Neural Networks}, author = {H. Xue and S. Boettger and N. Rottmann and H. Pandya and R. Bruder and G. Neumann and A. Schweikard and E. Rueckert}, url = {https://cps.unileoben.ac.at/wp/ASPAI2020Xue.pdf, Article File}, year = {2020}, date = {2020-06-30}, booktitle = {International Conference on Advances in Signal Processing and Artificial Intelligence (ASPAI’ 2020)}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } Close Article File Close

We are developing a repository for real-time control of the FRANKA EMIKA Panda 7-dof robot arm.

Our project is based on ROS and allows to teleoperate the robot arm in real-time using motion tracking data provided by OptiTrack’s Motive software. 

GitHub Project and Links

• https://github.com/ai-lab-science/Franka-ROS-TRAIN
• Details on the robot hardware
• Or on the developer site on world.franka.de
• ROS Gazebo Tutorial

Videos

• Research videos using the robot will be presented here. 

Publications

2020

Xue, H.; Boettger, S.; Rottmann, N.; Pandya, H.; Bruder, R.; Neumann, G.; Schweikard, A.; Rueckert, E. Sample-Efficient Covariance Matrix Adaptation Evolutional Strategy via Simulated Rollouts in Neural Networks Proceedings Article In: International Conference on Advances in Signal Processing and Artificial Intelligence (ASPAI’ 2020), 2020. Links | BibTeX @inproceedings{Xue2020, title = {Sample-Efficient Covariance Matrix Adaptation Evolutional Strategy via Simulated Rollouts in Neural Networks}, author = {H. Xue and S. Boettger and N. Rottmann and H. Pandya and R. Bruder and G. Neumann and A. Schweikard and E. Rueckert}, url = {https://cps.unileoben.ac.at/wp/ASPAI2020Xue.pdf, Article File}, year = {2020}, date = {2020-06-30}, booktitle = {International Conference on Advances in Signal Processing and Artificial Intelligence (ASPAI’ 2020)}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } Close Article File Close

FRANKA EMIKA’s Panda robot arm is a complient, light-weight robot arm with seven degrees-of-freedom.

Links

• ROS 2 support: https://github.com/mul-cps/franka_ros2
• wrist camera integration: https://github.com/mul-cps/mul_franka
• We use the C++ libfranka library in our own ROS 1 project for learning complex manipulation skills.
• Details on the robot hardware can be found on the FRANKA EMIKA page
• Or on the developer site on world.franka.de

Videos

• Research videos using the robot will be presented here. 

Publications

2020

Xue, H.; Boettger, S.; Rottmann, N.; Pandya, H.; Bruder, R.; Neumann, G.; Schweikard, A.; Rueckert, E. Sample-Efficient Covariance Matrix Adaptation Evolutional Strategy via Simulated Rollouts in Neural Networks Proceedings Article In: International Conference on Advances in Signal Processing and Artificial Intelligence (ASPAI’ 2020), 2020. Links | BibTeX @inproceedings{Xue2020, title = {Sample-Efficient Covariance Matrix Adaptation Evolutional Strategy via Simulated Rollouts in Neural Networks}, author = {H. Xue and S. Boettger and N. Rottmann and H. Pandya and R. Bruder and G. Neumann and A. Schweikard and E. Rueckert}, url = {https://cps.unileoben.ac.at/wp/ASPAI2020Xue.pdf, Article File}, year = {2020}, date = {2020-06-30}, booktitle = {International Conference on Advances in Signal Processing and Artificial Intelligence (ASPAI’ 2020)}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } Close Article File Close

Nils Rottmann, M.Sc. has developed a tutorial on using ROS and Gazebo. 

This tutorial was used in our humanoid robotics and machine learning lectures. 

GitHub Code & Links

• https://github.com/NRottmann/ROS_Gazebo_Tutorial

Weitere Links und Tutorials

• Python Videolectures
• Python Online Tutorials