Author: Elmar Rueckert

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150.510 Industrial Data Science Projekt (8SH SE, SS)

Elmar Rueckert teaching_currentseminars

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

Location & Time

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.

Responsibilities & Contacts

Elmar Rueckert wiki_administrative, wiki_phds, wiki_technician

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

SAP GUI MAC

Robot How to Build a USB Controlled Treadmill

Elmar Rueckert wiki_hard_software

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

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)
 

Robot LEGO Robotics EV3 Dev

Elmar Rueckert research_robots

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.  

 

Videos

Robot Hand RH8 with 19DoF

Elmar Rueckert research_robots

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.

 

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

Sample-Efficient Covariance Matrix Adaptation Evolutional Strategy via Simulated Rollouts in Neural Networks

GitHub FRANKA EMIKA Panda, ROS

Elmar Rueckert research_software

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

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

Sample-Efficient Covariance Matrix Adaptation Evolutional Strategy via Simulated Rollouts in Neural Networks

Robot FRANKA EMIKA Panda

Elmar Rueckert research_robots

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

Links

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

Sample-Efficient Covariance Matrix Adaptation Evolutional Strategy via Simulated Rollouts in Neural Networks

GitHub LEGO Robotic EV3 Python

Elmar Rueckert research_software

Dieses open-source Projekt enthält Tools und Demos für die Python-Entwicklung mit den Lego Mindstorms EV3 und EV3Dev Bricks. 

Die Inhalte sind verständlich aufbereitet und wir haben zahlreiche Tutorials und Aufgaben für Schüler*innen erstellt. 

GitHub Code & Links

Details to the Software Development

Dieser Einführungsvortrag beschreibt die grundlegenden Schritte um einen LEGO Roboter zu bauen und mit Python zu programmieren. 

Weitere Links und Tutorials

GitHub High-Accuracy Sensor Glove, ROS, Gazebo

Elmar Rueckert research_software, wiki_hard_software

Sensor gloves are gaining importance in tracking hand and finger movements in virtual reality applications as well as in scientific research. In this project, we developed  a low-budget, yet accurate sensor glove system that uses flex sensors for fast and efficient motion tracking. 

The contributions are ROS Interfaces, simulation models as well as motion modeling approaches. 

GitHub Code & Links

Details to the Software Development

The figure shows a simplified schematic diagram of the system architecture for our sensor glove design:

(a) Glove layout with sensor placements, the orange fields denote the flex sensors, while the IMU is marked as a green rectangle,

(b) Circuit board which is wired with the sensor glove, has 10 voltage dividers for reading each flex sensor connected to ADC pins of the microcontoller ESP32-S2 and the IMU is connected to I2C pins,

(c) The ESP32-S2 sends the raw data via WiFi as ROS messages to the computer, which allows a real-time visualization in Unity or Gazebo,

(d) Post-processing of the recorded data, e.g. learning probabilistic movement models and searching for similarities.

Publications

A research publication by Robin Denz, Rabia Demirci, M. Ege Cansev, Adna Bliek, Philipp Beckerle, Elmar Rueckert and Nils Rottmann is currently under review.