Author: Elmar Rueckert

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190.002 Cyber-Physical Systems Lab (2SH P, WS)

Elmar Rueckert teaching_pastlectures
The exercise will enable the application of modern machine learning techniques and tools in robotics / cyber-physical systems. The following topics will be covered in the course:
   – Kinematics, dynamics & simulation of CPS.
   – Data representations & model learning.
   – Control techniques, priorities & torque control.
   – Planning & cognitive reasoning.
   – Reinforcement learning and black-box optimization.

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

Exercises

Assignments / Team work

Location & Time

Latex Templates

Elmar Rueckert teaching_student_resources, wiki_road_to_thesis, wiki_undergradsgrads

CPS Latex Templates

Find below Latex templates for student reports, B.Sc. theses, M.Sc. theses and doctoral theses.

You may use these templates also for other lectures, courses, seminars or doctoral theses at other chairs at the Montanuniversität Leoben. However, do not remove the acknowledgement or copyright statement.

Student Report or Assignment

We provide a professional scientific student report template using double columns.

Get the latest CPS report template from our cloud.

Student Presentation

We provide a scientific student presentation template for project and thesis reports. Get the latest CPS presentation (latex beamer) template from our cloud.

B.Sc. Thesis

We provide a thesis template using minitocs in a standard report format.

Get the latest B.Sc. template from our cloud.

The latex template also provides basic instructions on the content and the structure of a thesis.

However, every thesis is unique and may be adapted acordingly.

M.Sc. Thesis

We provide a thesis template using minitocs in a standard report format.

Get the latest M.Sc. thesis template from our cloud.

Ph.D. Thesis

We provide a Ph.D. thesis template using minitocs in a standard report format.

Get the latest Ph.D. thesis template from our cloud.

In Introduction to Latex

If you have never used Latex, you find a brief intorduction in these slides on Latex.

Autonomes Industrieroboterlabor der Zukunft (AI-Robot-Lab)

Elmar Rueckert projects_thirdparty

Ausstattung:

  • 2 universal robotics UR3e Roboter,
  • 2 FANUC CRX10iA Roboter
  • eine Drehbank von ELMAG
  • eine Industriebohrfräse von ELMAG und
  • ein Rollenförderband.

Die Anforderungen an Industriebetriebe im Zeitalter der Digitalisierung sind enorm gestiegen und der Bedarf an individualisierten Losungen ist groß. Kleine Stückzahlen, komplexe Bauteilegruppen und der notwendige hohe grad der Automatisierung ist mit fest vorprogrammierten Roboterprogrammen nicht mehr umsetzbar.

Projektziel

Ziel des Projektes ist es sich als starken Partner für Forschungs- und Industriebetriebe zu positionieren. Dazu soll ein begehbares, autonomes Industrieroboterlabor aufgebaut werden, in dem Robotern praxisrelevante Arbeitsablaufe durch moderne Lernmethoden der kunstliche Intelligenz beigebracht werden. Werksmitarbeiter können innerhalb weniger Sekunden, durch Vorzeigen oder durch fuhrendes Anleiten, Maschinen komplexe Bewegungsabläufe beibringen. Für die Koordination multipler autonomer Robotereinheiten und die Prozessüberwachung werden moderne Datenmodellierungsmethoden entwickelt und über Tablets bedient.

Anwendungen

Die Anwendungsszenarien umfassen Manipulations- und Sortieraufgaben  an einem Rollenforderband, die automatische visuelle Objekterkennung und Vorhersage unter realen Industriebedingungen, der Warentransport durch mobile Roboter mit Greifarmen und die Bedienung komplexer Industriemaschinen, exemplarisch vorgefuhrt an einer Bohrfräsmaschine und an einer Kleindrehbank.

Kooperationen und öffentliche Events

Das autonome Industrieroboterlabor soll nachhaltig zu Kooperationen mit nationalen und regionalen Forschungs- und Industriepartnern fuhren und die Sichtbarkeit des Lehrstuhls fur Cyber-Physical-Systems (CPS) und der Montanuniversitat Leoben im Bereich der angewandten kunstlichen Intelligenz für CPS durch jährliche öffentliche Events steigern.

Der Lehrstuhl für Cyber-Pysical Systems

Elmar Rueckert research_printmedia , , ,

Der Lehrstuhl für Cyber-Physical-Systems widmet sich anwendungsorientierter Grundlagenforschung in den Bereichen der künstlichen Intelligenz, der Digitalisierung von Industrieprozessen und der Robotik. Ein Focus liegt dabei auf der Modelierung von intelligenten menschlichen Lernprozessen mit dem Ziel effiziente Lernmethoden und Vorhersagemodelle für cyber-physikalische Systeme zu entwickeln.

Gerade diese Schnittstelle zwischen fundamentaler Grundlagenforschung in tiefen neuronalen Netzen, probabilistischer Informationsverarbeitung und komplexen industriellen Anwendungen zeichnen den Lehrstuhl für Cyber-Physical-Systems aus.

Neben der Entwicklung von Algorithmen und Methoden zur Modelierung und Verarbeitung großer Datenmengen, baut der Lehrstuhl auch komplexe Roboter- und Sensorsysteme. Eines dieser Systeme wird in naher Zukunft autonom an der Universität navigieren, Besucher empfangen und mit ihnen über ein gelerntes Dialogsystem kommunizieren. Darüber hinaus entsteht gerade ein begehbares KI Roboter Labor, dass die anwendungsorientierte Grundlagenforschung anhand von Aufgaben mit Industrieroboterarmen an einem Rollenförderband greifbar macht.

Fotios (Fotis) Lygerakis, M.Eng.

Elmar Rueckert people_accademic

Hi! My name is Fotis and I am a doctoral student and university assistant at CPS since March 2022!

My goal is to advance machine learning and robotics, aiming to mimic human learning processes through abstraction, incremental conclusions, transfer learning, and creativity.

My research interests are centered on representation learning, visuotactile fusion and robot learning, with a focus on employing self-supervised learning methods, both contrastive and non-contrastive, as well as reinforcement learning techniques. Specifically, my work is dedicated to advancing the fields of representation learning and visuotactile robot learning, targeting manipulation tasks. This involves exploring innovative ways to enable robots to understand and interact with their environment through visual and tactile feedback, enhancing their ability to perform complex and tactile-rich manipulation tasks.

I am also contributing to human-robot interaction projects and develop interfaces for robotic manipulation.

Before starting my PhD at the University of Leoben, I held positions as a teaching assistant at the University of Texas at Arlington, a research assistant at Demokritos in Athens, and a research intern at Toshiba Research Europe in Cambridge. And before all that I received a Diploma in Electrical and Computer Engineering (equivalent to an Integrated Master in Engineering) from the Technical University of Crete (Greece) in 2019.

My work includes contributions to representation learning, reinforcement learning for robotic manipulation, healthcare robotics, and dialogue systems. I am also active in teaching, technical skill development, outreach, reviewing, and conference activities, committing to the scientific community to the best of my powers.

Research Interests​

  • Representation Learning: Methods for developing robust and informative representations of high-dimensional data, including self-supervised learning techniques such as contrastive learning and variational inference.
  • Multimodal Fusion: Integrating visual and tactile sensory inputs to create comprehensive representations, enhancing robot learning and manipulation capabilities.
  • Reinforcement Learning: Development of reinforcement learning algorithms that handle complex environments effectively, including algorithms that enhance the robot’s decision-making capabilities by balancing exploration and exploitation, and leveraging intrinsic motivation.
  • Imitation Learning: Leveraging pre-existing and collecting new datasets for training can significantly reduce the need for extensive real-time training and ensure comprehensive learning. This direction addresses inherent problems in Behavior Cloning and Offline RL, such as the distribution shift and the lack of diverse scenarios.
  • Practical Machine Learning Applications
    • Robotics in Healthcare: Assistive Technologies, Dialogue Systems
    • Industrial Automation: Inspection and Process Optimization
  •  

Theses and Internship Supervision

Thesis Topics

  • Self-supervised Visual Representation Learning
  • Reinforcement Learning algorithms and Robot Learning with Sim2Real transfer
  • Augmented Reality Robot Interfaces
  • Human Robot Interaction

Current & Past Theses

  • [M.Sc. Thesis/Internship] ROS2-based Human-Robot Interaction Framework with Sign Language, Iye Szin Ang, August 2023
  • [M.Sc. Thesis] Development of a Graphical User Interface and Deep Learning Methods for Automated Inspection in a Continuous Casting Steel Plant, Melanie Neubauer, March 2023
 

Students who wish to do their thesis under my supervision, shall choose their subject within the list of my research interests above. Feel free to contact me via email for further clarifications or directions.

Teaching

  • 190.013 Introduction to Machine Learning Lab, Summer Semester 2023
  • 190.015 Applied Machine and Deep Learning, Winter Semester 2023
  • Proud founder of the Neural Coffee Reading Group.

Consultation

Companies, fellow colleagues or students who wish consultation on any of my research interests or background can contact me via email: fotios.lygerakis@unileoben.ac.at

Contact

M.Eng. Fotios Lygerakis
Doctoral Student supervised by Univ.-Prof. Dr. Elmar Rueckert
Montanuniversität Leoben
Franz-Josef-Straße 18, 
8700 Leoben, Austria 

Phone:  +43 3842 402 – 1901 (Secretary of CPS)
Email: fotios.lygerakis@unileoben.ac.at
Chat: WEBEX

Publications

2024

Lygerakis, Fotios; Dave, Vedant; Rueckert, Elmar

M2CURL: Sample-Efficient Multimodal Reinforcement Learning via Self-Supervised Representation Learning for Robotic Manipulation Proceedings Article

In: IEEE International Conference on Ubiquitous Robots (UR 2024), IEEE 2024.

Links | BibTeX

M2CURL: Sample-Efficient Multimodal Reinforcement Learning via Self-Supervised Representation Learning for Robotic Manipulation

Dave*, Vedant; Lygerakis*, Fotios; Rueckert, Elmar

Multimodal Visual-Tactile Representation Learning through Self-Supervised Contrastive Pre-Training Proceedings Article

In: IEEE International Conference on Robotics and Automation (ICRA 2024)., 2024, (* equal contribution).

Links | BibTeX

Multimodal Visual-Tactile Representation Learning through Self-Supervised Contrastive Pre-Training

2023

Lygerakis, Fotios; Rueckert, Elmar

CR-VAE: Contrastive Regularization on Variational Autoencoders for Preventing Posterior Collapse Proceedings Article

In: Asian Conference of Artificial Intelligence Technology (ACAIT)., IEEE, 2023.

Links | BibTeX

CR-VAE: Contrastive Regularization on Variational Autoencoders for Preventing Posterior Collapse

2021

Lygerakis, Fotios; Dagioglou, Maria; Karkaletsis, Vangelis

Accelerating Human-Agent Collaborative Reinforcement Learning Conference

In Proceedings of the 14th PErvasive Technologies Related to Assistive Environments Conference (PETRA '21), Association for Computing Machinery, New York, NY, USA, 90–92, 2021.

Links | BibTeX

Accelerating Human-Agent Collaborative Reinforcement Learning

Banerjee, Debapriya; Lygerakis, Fotios; Makedon, Fillia

Sequential Late Fusion Technique for Multi-modal Sentiment Analysis Conference

In Proceedings of the 14th PErvasive Technologies Related to Assistive Environments Conference (PETRA '21), Association for Computing Machinery, New York, NY, USA, 264–265. , 2021.

Links | BibTeX

Sequential Late Fusion Technique for Multi-modal Sentiment Analysis

Kyrarini, Maria; Lygerakis, Fotios; Rajavenkatanarayanan, Akilesh; Sevastopoulos, Christos; Nambiappan, Harish Ram; Chaitanya, Kodur Krishna; Babu, Ashwin Ramesh; Mathew, Joanne; Makedon, Fillia

A Survey of Robots in Healthcare Journal Article

In: Technologies, vol. 9, iss. 8, 2021.

Links | BibTeX

 A Survey of Robots in Healthcare

2020

Lygerakis, Fotios; Tsitos, Athanasios C; Dagioglou, Maria; Makedon, Fillia; Karkaletsis, Vangelis

Evaluation of 3D markerless pose estimation accuracy using openpose and depth information from a single RGB-D camera Conference

In Proceedings of the 13th ACM International Conference on PErvasive Technologies Related to Assistive Environments (PETRA '20), Article 75, 1–6 Association for Computing Machinery, New York, NY, USA, 2020.

Links | BibTeX

Evaluation of 3D markerless pose estimation accuracy using openpose and depth information from a single RGB-D camera

Diakoloukas, Vassilios; Lygerakis, Fotios; Lagoudakis, Michail G; Kotti, Margarita

Variational Denoising Autoencoders and Least-Squares Policy Iteration for Statistical Dialogue Manager Journal Article

In: IEEE Signal Processing Letters , vol. 27, pp. 960-964, 2020.

Links | BibTeX

Variational Denoising Autoencoders and Least-Squares Policy Iteration for Statistical Dialogue Manager

2019

Lygerakis, Fotios; Diakoloulas, Vassilios; Lagoudakis, Michail; Kotti, Margarita

Robust Belief State Space Representation for Statistical Dialogue Managers Using Deep Autoencoders Conference

2019 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU), 2019.

Links | BibTeX

Robust Belief State Space Representation for Statistical Dialogue Managers Using Deep Autoencoders

NextCloud Setup

Elmar Rueckert wiki_hard_software

This guide describes how to connect to our nextcloud apps.

Nextcloud Clients

MAC OS iCal Calendar App

  • Go to Settings/Internet Accouns
  • Select ‘Other’ and ‘CalDAV’
  • Select ‘Advanced’ as installation option.
  • Enter your user name and password
  • Use the following server address: cloud.cps.unileoben.ac.at
  • Use the path: /remote.php/dav/principals/users/YourNCUserName/
  • Use the Port: 443

 

WEBEX Setup @ MUL

Elmar Rueckert wiki_hard_software

This guide describes how to setup webex using our univeristy accounts.

WEBEX Clients

Connection Settings

Use the following connection settings:

  • use your university email, e.g., firstname.lastname@unileoben.ac.at
  • The server link is: https://unileoben.webex.com
  • When you connect for the first time, you need to enter your p-number and MUOnline password!
  • Your personal room will be: https://unileoben.webex.com/meet/firstname.lastname

Mrs. Regina Schelch (Secretary)

Elmar Rueckert people_secretariat

Secretrary

Short bio: Mrs. Regina Schelch joined the CPS team in February 2022. 

 

Research Interests

  • Cyber-Physical-Systems 
  • Modern Technologies 
  • Learning Machines and Robotics

Contact

Mrs. Regina Schelch
Sekretariat des Lehrstuhls für Cyber-Physical-Systems
Montanuniversität Leoben
Franz-Josef-Straße 18, 
8700 Leoben, Austria 

Phone:  +43 3842 402 – 1901
Email:   regina.schelch@unileoben.ac.at 
Web:  https://cps.unileoben.ac.at

140.186 & 560.150 Seminar Master Work – Mechanical Engineering (3SH SE, WS & SS)

Elmar Rueckert teaching_currentseminars

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

If so, this bachelor seminar 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

Location & Time

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.

140.185 & 560.100 Seminar Bachelor Work – Mechanical Engineering (8SH SE, WS & SS)

Elmar Rueckert teaching_currentseminars

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

If so, this bachelor seminar 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

Location & Time

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.