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Fotios (Fotis) Lygerakis, M.Eng.

Ph.D. Student at the Montanuniversität Leoben

Short bio:

Hi! I my name is Fotis and I have been a member of CPS since March 2022!

Nowadays, you will find my working on projects on

  • Unsupervised Visual Representation Learning
  • Robot Learning
  • Human-Robot Interfaces
  • Machine Learning applications for Steel Plants.

I received my Diploma (Master of Engineering) in Electrical and Computer Engineering from Technical University of Crete, Greece in 2019. Thereafter, I worked as teaching assistant at the University of Texas at Arlington, USA until December 2021.

Research Interests

  • Deep Learning
  • Unsupervised Visual Representation Learning
  • Reinforcement Learning
  • Robot Learning
  • Human-Robot Interfaces
  • Predictive Maintenance in Steel Plants

Contact & Quick Links

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 (Sekretariat CPS)
Email:   fotios.lygerakis@unileoben.ac.at 
Web Work: CPS-Page
Chat: WEBEX

Publications

Sorry, no publications matched your criteria.

Vedant Dave, M.Sc.

Ph.D. Student at the Montanuniversität Leoben

Short bio: Mr. Vedant Dave started at CPS on 23rd September 2021. 

He received his Master degree in Automation and Robotics from Technische Universität Dortmund in 2021 with the study focus on Robotics and Artificial Intelligence. His thesis was entitled “Model-agnostic Reinforcement Learning Solution for Autonomous Programming of Robotic Motion”, which took place at at Mercedes-Benz AG. In the thesis, he implemented Reinforcement learning for the motion planning of manipulators in complex environments. Before that, he did his Research internship at Bosch Center for Artificial Intelligence, where he worked on Probabilistic Movement Primitives on Riemannian Manifolds.

Research Interests

  • Movement Primitives
  • Probabilistic Learning
  • Deep Reinforcement Learning
  • Model Learning

Research Videos

Contact & Quick Links

M.Sc. Vedant Dave
Doctoral Student supervised by Univ.-Prof. Dr. Elmar Rueckert.
Montanuniversität Leoben
Franz-Josef-Straße 18, 
8700 Leoben, Austria 

Phone:  +43 3842 402 – 1903
Email:   vedant.dave@unileoben.ac.at 
Web Work: CPS-Page
Web Private: https://www.linkedin.com/in/vedant-dave-095629178/
Chat: WEBEX

CV of M.Sc. Vedant Dave
DBLP
Frontiers Network
GitHub
Google Citations
LinkedIn
ORCID
Research Gate

Publications

2022

Rozo*, Leonel; Dave*, Vedant

Orientation Probabilistic Movement Primitives on Riemannian Manifolds Conference

Conference on Robot Learning, vol. 164, 2022.

Abstract | Links | BibTeX

Orientation Probabilistic Movement Primitives on Riemannian Manifolds

Linus Nwankwo, M.Sc.

Ph.D. Student at the Montanuniversität Leoben

Short bio: Mr. Linus Nwankwo started at CPS in August 2021.  Prior to joining CPS as a PhD student, he worked as a research intern at the Department of Electrical and Computer EngineeringTechnische Universität Kaiserslautern, Germany.  In 2020, he obtained his Master of Science (M.Sc.) degree in Automation and Robotics, a speciality in control for Green Mechatronics (GreeM) at the University of Bourgogne Franche-Comte (UBFC), France. In his  M.Sc. thesis titled ‘Hardware Review and Control of a Mobile Inverted Pendulum Robot’, he implemented a stabilisation control for the robot and as well investigated the possibility of controlling the robot via CANopen communication network

Research Interests

  • Robotics:  Dynamics modelling, Robust control, Sensor fusion, Simultaneous Localization & Mapping (SLAM), Navigation & Motion planning.
  • Machine Learning:  Probabilistic machine learning and inferences for robotics (Bayesian Optimization & Gaussian Processes).

Research Videos

Contact & Quick Links

M.Sc. Linus Nwankwo
Doctoral Student supervised by Univ.-Prof. Dr. Elmar Rueckert since August 2021.
Montanuniversität Leoben
Franz-Josef-Straße 18, 
8700 Leoben, Austria 

Phone:  +43 3842 402 – 1901 (Sekretariat CPS)
Email:   linus.nwankwo@unileoben.ac.at 
Web Work: CPS-Page
Web Private: https://sites.google.com/view/linus-nwankwo
Chat: WEBEX

CV of M.Sc. Linus Nwankwo
DBLP
Frontiers Network
GitHub
Google Citations
LinkedIn
ORCID
Research Gate

Publcations

Sorry, no publications matched your criteria.

Research Discussion

Nikolaus Feith, M.Sc.

Ph.D. Student at the Montanuniversität Leoben

Short bio: Mr. Nikolaus Feith started at CPS in July 2022. 

M.Sc. Nikolaus Feith studied Mechanical Engineering at the Montanuniversität Leoben. He passed his Master defense in June 2022.

In his doctoral thesis, he investigates  graphical model inference models for manipulation tasks through conditioning on visual inputs.

Research Interests

  • Cyber-Physical-Systems 
  • Robotics
  • Machine Learning

Contact

M.Sc. Nikolaus Feith
Doctoral Student supervised by Univ.-Prof. Dr. Elmar Rueckert since July 2022.
Montanuniversität Leoben
Franz-Josef-Straße 18, 
8700 Leoben, Austria 

Phone:  +43 3842 402 – 1901 (Sekretariat CPS)
Email:   nikolaus.feith@unileoben.ac.at 
Web Work: CPS-Page
Chat: WEBEX

Publications

Sorry, no publications matched your criteria.

Meeting Notes

Honghu Xue, M.Sc.

Ph.D. Student at the University of Luebeck

Portrait of Honghu Xue

Short bio: Mr. Honghu Xue investigates in his doctoral thesis deep reinforcement learning approaches for  planning and control. His methods are applied to mobile robots and the FRANKA EMIKA robot arm. He started his thesis in March 2019.

Honghu Xue received his M.Sc. in Embedded Systems Engineering at Albert-Ludwigs-University of Freiburg with the study focus on Reinforcement Learning, Machine Learning and AI.

Research Interests

  • Deep Reinforcement Learning: Model-Based RL, Sample-efficient RL, Long-time-horizon RL, Efficient Exploration Strategies in MDP, Distributional RL, Policy Search.
  • Deep & Machine Learning: Learning Transition Model in MDP (featuring visual input and modelling the stochasticity of the environment), Super-resolution Image using DL, Time-Sequential Model for Partial Observability.

Research Videos

Contact & Quick Links

M.Sc. Honghu Xue
Doctoral Student supervised by Univ.-Prof. Dr. Elmar Rueckert since March 2019.
Ratzeburger Allee 160,
23562 Lübeck,
Deutschland

Phone:  +49 451 3101 – 5213
Email:   xue@rob.uni-luebeck.de
Web:https://www.rob.uni-luebeck.de/index.php?id=460

CV of M.Sc. Honghu Xue
DBLP
Frontiers Network
Github
Google Citations
LinkedIn
ORCID
Rearch Gate

Meeting Notes

Publcations

2022

Herzog, Rebecca; Berger, Till M; Pauly, Martje Gesine; Xue, Honghu; Rueckert, Elmar; Munchau, Alexander; B"aumer, Tobias; Weissbach, Anne

Cerebellar transcranial current stimulation-an intraindividual comparison of different techniques Journal Article

In: Frontiers in Neuroscience, 2022.

Links | BibTeX

Cerebellar transcranial current stimulation-an intraindividual comparison of different techniques

Xue, Honghu; Hein, Benedikt; Bakr, Mohamed; Schildbach, Georg; Abel, Bengt; Rueckert, Elmar

Using Deep Reinforcement Learning with Automatic Curriculum Learning for Mapless Navigation in Intralogistics Journal Article

In: Applied Sciences (MDPI), Special Issue on Intelligent Robotics, 2022, (Supplement: https://cloud.cps.unileoben.ac.at/index.php/s/Sj68rQewnkf4ppZ).

Abstract | Links | BibTeX

Using Deep Reinforcement Learning with Automatic Curriculum Learning for Mapless Navigation in Intralogistics

2021

Xue, Honghu; Herzog, Rebecca; Berger, Till M.; Bäumer, Tobias; Weissbach, Anne; Rueckert, Elmar

Using Probabilistic Movement Primitives in analyzing human motion differences under Transcranial Current Stimulation Journal Article

In: Frontiers in Robotics and AI , vol. 8, 2021, ISSN: 2296-9144.

Abstract | Links | BibTeX

Using Probabilistic Movement Primitives in analyzing human motion differences under Transcranial Current Stimulation

Cansev, Mehmet Ege; Xue, Honghu; Rottmann, Nils; Bliek, Adna; Miller, Luke E.; Rueckert, Elmar; Beckerle, Philipp

Interactive Human-Robot Skill Transfer: A Review of Learning Methods and User Experience Journal Article

In: Advanced Intelligent Systems, pp. 1–28, 2021.

Links | BibTeX

Interactive Human-Robot Skill Transfer: A Review of Learning Methods and User Experience

2020

Akbulut, M Tuluhan; Oztop, Erhan; Seker, M Yunus; Xue, Honghu; Tekden, Ahmet E; Ugur, Emre

ACNMP: Skill Transfer and Task Extrapolation through Learning from Demonstration and Reinforcement Learning via Representation Sharing Conference

2020.

Abstract | Links | BibTeX

ACNMP: Skill Transfer and Task Extrapolation through Learning from Demonstration and Reinforcement Learning via Representation Sharing

Rottmann, N.; Bruder, R.; Xue, H.; Schweikard, A.; Rueckert, E.

Parameter Optimization for Loop Closure Detection in Closed Environments Inproceedings

In: Workshop Paper at the International Conference on Intelligent Robots and Systems (IROS), pp. 1–8, 2020.

Links | BibTeX

Parameter Optimization for Loop Closure Detection in Closed Environments

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 Inproceedings

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