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Leap Hand

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The LEAP Hand is a low-cost, efficient, and anthropomorphic robotic hand designed for dexterous manipulation and robot learning. The hand is robust, durable, and capable of exerting large torques over extended periods. With a novel kinematic structure that retains all degrees of freedom in any finger position, it supports a wide range of manipulation tasks, including grasping, teleoperation, and in-hand object rotation. The LEAP Hand is open-source, with detailed assembly instructions, simulation tools, and APIs, making it accessible and scalable for research and development.

Videos

  • Research videos using the robot will be presented here. 

 

Publications

Sorry, no publications matched your criteria.



Dr. Christian Rauch

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Short Bio

Dr. Christian Rauch is a Research Scientist at Bosch Corporate Research in the Robot Learning group. He received his Ph.D. degree in 2020 in Robotics and Autonomous Systems at the University of Edinburgh. He has over 10 years of experience in applying machine learning techniques to robotic perception and manipulation tasks in unstructured environments. In his current research he focuses on the application of Foundation Models to robotic manipulation tasks in industrial and domestic contexts.

We are pleased to announce that Dr. Christian Rauch will join the CPS team in February 2025.

Research Interests​

Machine learning, Robot Learning, Robot Vision, Perception and Manipulation.

Contact

Dr. Christian Rauch
Research Group Leader at the Chair of Cyber-Physical-Systems
Montanuniversität Leoben
Franz-Josef-Straße 18, 
8700 Leoben, Austria 

Phone:  +43 3842 402 – 1901
Email: Christian.Rauch@unileoben.ac.at
Chat: https://unileoben.webex.com/meet/christian.rauch

Selected Publications



Richard Marecek

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Student Assistant at the Montanuniversität Leoben

MarecekRichard

Short bio: Richard Marecek started at CPS in October  2024.

He is a bachelor student at Montanuniversität Leoben in the program Industrial Data Science. His research interests include industrial process modeling based on large sensor arrays and machine learning algorithms. 

Research Interests

  • Industrial Process Modeling 
  • Machine Learning
  • Python Programming

Contact

Richard Marecek
Student Assistant at the Chair of Cyber-Physical-Systems
Montanuniversität Leoben
Franz-Josef-Straße 18,
8700 Leoben, Austria 

Email: richard.marecek@stud.unileoben.ac.at



Rino Morina

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Student Assistant at the Montanuniversität Leoben

RinoMorina

Short bio: Rino Morina started at CPS in October  2024.

Rino Morina is a bachelor student at Montanuniversität Leoben in the program Industrial Data Science. His research interests include data modeling based on machine learning algorithms including neural networks. 

Research Interests

  • Data Modeling 
  • Machine Learning
  • Python Programming

Contact

Rino Morina
Student Assistant at the Chair of Cyber-Physical-Systems
Montanuniversität Leoben
Franz-Josef-Straße 18,
8700 Leoben, Austria 

Email: rino.morina@stud.unileoben.ac.at



Julia Schmelz

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Student Assistant at the Montanuniversität Leoben

Portrait_Schmelz

Short bio: Julia Schmelz started at CPS in October  2024.

Julia Schmelz is a master student at Montanuniversität Leoben in the program Industrial Data Science. Her research interests include data modeling based on machine learning algorithms including neural networks. 

Research Interests

  • Data Modeling 
  • Machine Learning
  • Python Programming

Contact

Julia Schmelz
Student Assistant at the Chair of Cyber-Physical-Systems
Montanuniversität Leoben
Franz-Josef-Straße 18,
8700 Leoben, Austria 

Email: julia-maria.schmelz@stud.unileoben.ac.at



Enrollment as a student

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This post is intention to help future international PhD students to ease their enrollment process.

Step 1: Find a mentor and prepare your documents

You need to find a topic, working title, individual curriculum and supervising team (supervisor and mentor) yourself to be able to fill out the form. According to our curriulum for the doctoral studies the mentor needs to have a venia docendi. 

If your degrees are not from a EU country, you need a full authentication or an Apostille of your documents (and the translations, if your documents are not in English), please inform yourself if you’d need either an authentication or Apostille for Austria! This is valid for both, Master’s documents as well as bachelor’s!

If you can not obtain an authentication/Apostille we also accept a translation of your 4 academical documents by an officially licenced translator in Austria instead.

Step 2: Applications form

You have also required to fulfil 20 ECTS, they have to be split in main and secondary subject:

  • Courses that don’t have anything to do with your subjects (language courses etc.) you can do up to 4 ECTS

  • Privatissima (talking to your prof, SE type) for a maximum of 4 ECTS

  • There is no limit on IV courses

  • And you can also have stuff (eg. summer school) from other unis, however you have to go through the equality process (they will check if it really is equal to the ects here)

These stated in the curriculum, which is in German.

Example course:

 

Step 3: Online registration for graduates of Non-Austrian University

Create a basic account in MUonline of the university. You have to create a student account even you have a staff account.

p/s: Sign out your staff account when you’re in the process of creating a basic account.

Detailed admission process at here.

Step 4: Waiting and make appointment to verify the real document



CPS 5-Finger Robot Hand 2024

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Self-made, dexterous 5-finger 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.

Webserver GUI for Control

Within a student project, an ESP32 based web-server was developed for controlling the hand, see the git repository. 

Videos

  • Research videos using the robot will be presented here. 

 

Publications

Sorry, no publications matched your criteria.



190.015 Applied Machine and Deep Learning (5SH IL, WS)

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Course Content

In the first week, advanced machine and deep learning methods like multi-layer-perceptrons, convolutional neural networks, variational autoencoder, transformers, simultaneous navigation and mapping approaches, and more will be presented.

These methods can be tested using interactive tools like for example using   https://playground.tensorflow.org. To deepen the knowledge, students will answer well-crafted scientific questions using latex handouts alone or in teams of two students in the lecture room. 

Additionally, Jupyter notebook files were prepared to implement advanced machine and deep learning approaches without installing any software. For all participants of the course user accounts will be created using our JupyterHub at https://jupyter.cps.unileoben.ac.at. The accounts will remain active till the end of the semester. 

Prerequisites & If you Miss Course Contents

During the first week, a laptop or tablet will be needed to use the interactive tools and the Jupyter notebooks. 

Webex Online Sessions of the 1st Week

Find here the link to the online stream during the first week in October, 2024: https://unileoben.webex.com/unileoben/j.php?MTID=m5492385776dd885ca5dde72e52563c61

When you miss some course contens

If you miss some course contents due to overlapping events, you can watch recordings of the sessions online. All recordings will be hosted via Moodle at https://moodle.unileoben.ac.at/course/view.php?id=3082.

 

Course Description

Modern machine learning methods and in particular deep learning methods are entering almost all areas of engineering. 

The integrated course enables the students to apply these methods in the application domains of their study.

For this purpose, current problems from the industry are investigated and the possibilities of machine and deep learning methods are tested.

Students gain a deep understanding of method implementations, how data must be prepared, which criteria are relevant for selecting learning methods, and how evaluations must be performed in order to interpret the results in a meaningful way.

Initially, the basics of learning methods are developed in 5-6 lecture units. Then, students select one of the listed industrial problems and work on it alone or as a team (with extended assignments). The project work is accompanied by weekly tutorials with tips and tricks. Finally, the project results are discussed in a written report and presented for a final 10-15min.

Grading is based on the quality of the code, the report, and the final short presentation.

Among others, one of the following industry problems can be chosen:

1. Application and comparison of deep neural networks for steel quality prediction in continuous casting plants with data from the ‘Stahl- und Walzwerk Marienhütte GmbH Graz’.

2. Predictive maintenance of bearing shells using frequency analysis in decision trees and deep neural networks based on acoustic measurement data.

3. Motion analysis and path planning for human-machine interaction in logistics tasks with mobile robots of the Chair of CPS.

4. Autonomous navigation and mapping with RGB-D cameras of the four-legged robot Unitree Go1 for excavation inspection in mining.

The project list is continuously extended.

Links and Resources

Location & Time

  • Location: HS 3 Studienzentrum
  • Dates: 01.10.2024 – 07.10.2024, see the course schedule above.
  • Location: Digital Science Center (Roseggerstraße 11, 8700 Leoben)
  • Date: 22.01.2025, 10:15 – 15:15, final presentation

 

Kickoff meeting of project

All meetings will be conducted at CPS chair. The time please refer to the email, contact us if reschedule is needed.

Previous Knowledge Expected

Formal Prerequisite: Basic Python programming skills, Fundamentals of Statistics.

Recommended Prerequisites: Introduction to Machine Learning (“190.012” and “190.013”).

Slides

Learning objectives / qualifications

  • Implement or independently adapt modern machine learning methods and in particular deep learning methods in Python.
  • Analyze data of complex industrial problems, process (filter) the data, and divide it into training- and test data sets such that a meaningful interpretation is possible.
  • Define criteria and metrics to evaluate evaluations and predictions and generate statistics.
  • Develop, evaluate, and discuss meaningful experiments and evaluations.
  • Identify and describe assumptions, problems, and ideas for improvement of practical learning problems.

Grading

Continuous assessment: During the lectures and the tutorials 0-20 bonus points can be collected through active participation.

Project assignments: Alone or in small groups (2-3 students) one of the listed projects has to be implemented. A written report in form of a git repository wiki page have to be submitted.
– For the implementation (Python Code) 0-40 Points can be obtained.
– For the wiki page report, 0-60 Points will be given.

Grading scheme: 0-49,9 Points (5), 50-65,9 Points (4), 66-79 Points (3), 80-91 Points (2), 92-100 Points (1).

With an overall score of up to 79%, an additional oral performance review MAY (!) also be required if the positive performance record is not clear. In this case, you will be informed as soon as the grades are released. If you have already received a grade via MU online, you will not be invited to another oral performance review.

Literature

Machine Learning and Data-modelling:

– Rueckert Elmar 2022. An Introduction to Probabilistic Machine Learning, https://cloud.cps.unileoben.ac.at/index.php/s/iDztK2ByLCLxWZA

– James-A. Goulet 2020. Probabilistic Machine Learning for Civil Engineers. MIT Press.

– Bishop 2006. Pattern Recognition and Machine Learning, Springer.

Learning method Programming in Python:

– Sebastian Raschka, YuxiH. Liu and Vahid Mirjalili 2022. Machine Learning with PyTorch and Scikit- Learn. Packt Publishing Ltd, UK.

– Matthieu Deru and Alassane Ndiaye 2020. Deep Learning mit TensorFlow, Keras und TensorFlow.js., Rheinwerk-verlag, DE. 

Problem specific Literature:

– B. Siciliano, L.Sciavicco 2009. Robotics: Modelling, Planning and Control, Springer.

– Kevin M. Lynch and FrankC. Park 2017. MODERN ROBOTICS, MECHANICS, PLANNING, AND CONTROL, Cambridge University Press.

– E.T. Turkogan 1996. Fundamentals of Steelmaking. Maney Publishing,UK.



190.018 Introduction to Machine Learning (4SH VU, SS)

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This course is based on the Machine Learning book by Univ.-Prof. Dr. Elmar Rueckert. 

It is written for experienced undergraduates or for first
semester graduate students.

This lecture with integrated exercises provides the basic knowledge for the application of modern machine learning methods. It includes an introduction to the basics of data modeling and probability theory. Classical probabilistic linear and non-linear regression methods are derived and discussed using practical examples.

Links and Resources

Location & Time

Lecture

 

Exercise

 

Slides

Course Topics

  1. Introduction to Machine Learning (Data and modelling fundamentals)
  2. Introduction to Probability Theory (Statistics refresher, Bayes Theorem, Common Probability distributions, Gaussian Calculus).
  3. Linear Probabilistic Regression (Linear models, Maximum Likelihood, Bayes & Logistic Regression).
  4. Nonlinear Probabilistic Regression (Radial basis function networks, Gaussian Processes, Recent research results in Robotic Movement Primitives, Hierarchical Bayesian & Mixture Models).
  5. Probabilistic Inference for Time Series (Time series data, basis function models, learning).

Learning objectives / qualifications

  • Students get a comprehensive understanding of basic probability theory concepts and methods.
  • Students learn to analyze the challenges in a task and to identify promising machine learning approaches.
  • Students will understand the difference between deterministic and probabilistic algorithms and can define underlying assumptions and requirements.
  • Students understand and can apply advanced regression, inference and optimization techniques to real world problems.
  • Students know how to analyze the models’ results, improve the model parameters and can interpret the model predictions and their relevance.
  • Students understand how the basic concepts are used in current state-of-the-art research in robot movement primitive learning and in neural planning.

Grading

The course will be graded based on a written exam (100 Points). 50% of all questions need to be answered correctly to be positive. The exam will take place in the classroom, or online, depending on the current university regulations.

In addition, up to 10 bonus points obtained in regular quiz sessions in the classroom, and 20% of the achieved points of the Machine Learning Lab will be added to your exam result. Note that bonus points can only be obtained when attending the lectures in person. 

Grading scheme: 0-49.9Pts (5), 50-65.9Pts (4), 66-79Pts (3), 80-91Pts (2), 92-100Pts (1).

Forthcoming exam dates are:

  • XX.06.2025 at 13:15 HS 1 Studienzentrum
  • XX.10.2025 at 13:15 – 14:45  (location not fixed)
  • More dates upon request via email to cps@unileoben.ac.at (send your request one month in advance to the desired exam date).

Literature

  • The Probabilistic Machine Learning book by Univ.-Prof. Dr. Elmar Rueckert. 
  • James-A. Goulet. Probabilistic Machine Learning for Civil Engineers. ISBN 978-0-262-53870-1.
  • Daphne Koller, Nir Friedman. Probabilistic Graphical Models: Principles and Techniques. ISBN 978-0-262-01319-2
  • Christopher M. Bishop. Pattern Recognition and Machine Learning. Springer (2006). ISBN 978-0-387-31073-2.
  • David Barber. Bayesian Reasoning and Machine Learning, Cambridge University Press (2012). ISBN 978-0-521-51814-7.
  • Kevin P. Murphy. Machine Learning: A Probabilistic Perspective. ISBN 978-0-262-01802-9

Note that all books are available at our library or at the chair of CPS. 



Geislinger GmbH

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Laufende Projekte, Bachelor- und Masterarbeiten

  • Automatisierungslösungen in der Produktion