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M.Sc. Thesis, Stefan Maintinger – Map-based and map-less mobile navigation via deep reinforcement learning in dynamic environments

Supervisor: Linus Nwankwo, M.Sc.;
Univ.-Prof. Dr Elmar Rückert
Start date: 5th September 2022

 

Theoretical difficulty: mid
Practical difficulty: mid

Abstract

For over 20 years today, the simultaneous localisation and mapping (SLAM) approach has been widely used to achieve autonomous navigation objectives. The SLAM problem is the problem of building a map of the environment while simultaneously estimating the robot’s position relative to the map given noisy sensor observations and a series of control data.  Recently, the 

mapless-based approach with deep reinforcement learning has been proposed. For this approach, the agent (robot) learns the navigation policy given only sensor data and a series of control data without a prior map of the task environment. In the context of this thesis, we will evaluate the performance of both approaches in a crowded dynamic environment using our differential drive open-source open-shuttle mobile robot.

Tentative Work Plan

To achieve our objective, the following concrete tasks will be focused on:

  • Literature research and a general understanding of the field
    • mobile robotics and industrial use cases
    • Overview of map-based autonomous navigation (SLAM & Path planning)
    • Overview of mapless-based autonomous navigation approach with deep reinforcement learning
    •  
  • Setup and familiarize with the simulation environment
    • Build the robot model (URDF) for the simulation (optional if you wish to use the existing one)
    • Setup the ROS framework for the simulation (Gazebo, Rviz)
    • Recommended programming tools: C++, Python, Matlab
    •  
  • Intermediate presentation:
    • Presenting the results of the literature study
    • Possibility to ask questions about the theoretical background
    • Detailed planning of the next steps
    •  
  • Define key performance/quality metrics for evaluation:
    • Time to reach the desired goal
    • Average/mean speed
    • Path smoothness
    • Obstacle avoidance/distance to obstacles
    • Computational requirement
    • success rate
    • e.t.c
    •  
  • Assessment and execution:
    • Compare the results from both map-based and map-less approaches on the above-defined evaluation metrics.
    •  
  • Validation:
    • Validate both approaches in a real-world scenario using our open-source open-shuttle mobile robot.
    •  
  • Furthermore, the following optional goals are planned:
    • Develop a hybrid approach combining both the map-based and the map-less methods.
    •  
  • M.Sc. thesis writing
  • Research paper writing (optional)

Related Work

[1] 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“, In: Applied Sciences (MDPI), Special Issue on Intelligent Robotics, 2022.

[2] Han Hu; Kaicheng Zhang; Aaron Hao Tan; Michael Ruan; Christopher Agia; Goldie Nejat “Sim-to-Real Pipeline for Deep Reinforcement Learning for Autonomous Robot Navigation in Cluttered Rough Terrain”,  IEEE Robotics and Automation Letters ( Volume: 6, Issue: 4, October 2021).

[3] Md. A. K. Niloy; Anika Shama; Ripon K. Chakrabortty; Michael J. Ryan; Faisal R. Badal; Z. Tasneem; Md H. Ahamed; S. I. Mo, “Critical Design and Control Issues of Indoor Autonomous Mobile Robots: A Review”, IEEE Access ( Volume: 9), February 2021.

[4]  Ning Wang, Yabiao Wang, Yuming Zhao, Yong Wang and Zhigang Li , “Sim-to-Real: Mapless Navigation for USVs Using Deep Reinforcement Learning”, Journal of Marine Science and Engineering, 2022, 10, 895. https://doi.org/10.3390/jmse10070895

Master Thesis

The final master thesis document can be downloaded here. 




Meeting Notes May 2023

Meeting 11/05

Research

  • submitted CR-VAE paper to ECAI
  • Research leads:
    1. Dimensionality Collapse of Visual Representations in Reinforcement Learning
    2. Improve SwAV architecture by creating better latent space clusters with the use of Sparse Autoencoders

PhD Registration

  • Signed Application
  • Will hand it over to the Admissions office

M.Sc. Students/Interns

  • Possible PhD position for Iye Szin
  • Early June first draft presentation

ML Course

  • Assignment 5

Miscellaneous

  • Kleinwassertal

 

Meeting 25/05

Research

Literature Review

  1. Dimensionality Collapse of Visual Representations in Reinforcement Learning
  2. Improve SwAV architecture by creating better latent space clusters with the use of Sparse Autoencoders

 ECAI review papers

  • 6 papers assigned = 16hours(2 days)/paper = 96 hours(12 days)
  • More feasible to review 2 papers.
  • deadline 16 June

M.Sc. Students/Interns

ML Course

  • Graded up to assignment 4
  • Assignment 6 out

Miscellaneous

 




3D perception and SLAM using geometric and semantic information for mine inspection with quadruped robot

Supervisor: Linus Nwankwo, M.Sc.;
Univ.-Prof. Dr Elmar Rückert
Start date: As soon as possible

 

Theoretical difficulty: mid
Practical difficulty: high

Abstract

Unlike the traditional mine inspection approach, which is inefficient in terms of time, terrain, and coverage, this project/thesis aims to investigate novel 3D perception and SLAM using geometric and semantic information for real-time mine inspection.

We propose to develop a SLAM approach that takes into account the terrain of the mining site and the sensor characteristics to ensure complete coverage of the environment while minimizing traversal time.

Tentative Work Plan

To achieve our objective, the following concrete tasks will be focused on:

  • Study the concept of 3D perception and SLAM for mine inspection, as well as algorithm development, system integration and real-world demonstration using Unitree Go1 quadrupedal robot.

  • Setup and familiarize with the simulation environment:
    • Build the robot model (URDF) for the simulation (optional if you wish to use the existing one)
    • Setup the ROS framework for the simulation (Gazebo, Rviz)
    • Recommended programming tools: C++, Python, Matlab
  • Develop a novel SLAM system for the quadrupedal robot to navigate, map and interact with challenging real-world environments:
    • 2D/3D mapping in complex indoor/outdoor environments

    • Localization using either Monte Carlo or extended Kalman filter

    • Complete coverage path-planning

  • Intermediate presentation:
    • Presenting the results of the literature study
    • Possibility to ask questions about the theoretical background
    • Detailed planning of the next steps
  • Implementation:

    • Simulate the achieved results in a virtual environment (Gazebo, Rviz, etc.)

    • Real-time testing on Unitree Go1 quadrupedal robot.

  • Evaluate the performance in various challenging real-world environments, including outdoor terrains, urban environments, and indoor environments with complex structures.
  • M.Sc. thesis or research paper writing (optional)

Related Work

[1]  Wolfram Burgard, Cyrill Stachniss, Kai Arras, and Maren Bennewitz , ‘SLAM: Simultaneous
Localization and Mapping’,  http://ais.informatik.uni-freiburg.de/teaching/ss12/robotics/slides/12-slam.pdf

[2]  V.Barrile, G. Candela, A. Fotia, ‘Point cloud segmentation using image processing techniques for structural analysis’, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-2/W11, 2019 

[3]  Łukasz Sobczak , Katarzyna Filus , Adam Domanski and Joanna Domanska, ‘LiDAR Point Cloud Generation for SLAM Algorithm Evaluation’, Sensors 2021, 21, 3313. https://doi.org/10.3390/ s21103313.




M.Sc. Project – Mekonoude Etienne Kpanou: Mobile robot teleoperation based on human finger direction and vision

Supervisors: Univ. -Prof. Dr. Elmar Rückert  and Nwankwo Linus M.Sc.

Theoretical difficulty: mid
Practical difficulty: mid

Naturally, humans have the ability to give directions (go front, back, right, left etc) by merely pointing fingers towards the direction in question. This can be done effortlessly without saying a word. However, mimicking or training a mobile robot to understand such gestures is still today an open problem to solve.
In the context of this thesis, we propose finger-pose based mobile robot navigation to maximize natural human-robot interaction. This could be achieved by observing the human fingers’ Cartesian  pose from an

RGB-D camera and translating it to the robot’s linear and angular velocity commands. For this, we will leverage computer vision algorithms and the ROS framework to achieve the objectives.
The prerequisite for this project are basic understanding of Python or C++ programming, OpenCV and ROS.

Tentative work plan

In the course of this thesis, the following concrete tasks will be focused on:

  • study the concept of visual navigation of mobile robots
  • develop a hand detection and tracking algorithm in Python or C++
  • apply the developed algorithm to navigate a simulated mobile robot
  • real-time experimentation
  • thesis writing

References

  1. Shuang Li, Jiaxi Jiang, Philipp Ruppel, Hongzhuo Liang, Xiaojian Ma,
    Norman Hendrich, Fuchun Sun, Jianwei Zhang,  “A Mobile Robot Hand-Arm Teleoperation System by Vision and IMU“,  IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS),  October 25-29, 2020, Las Vegas, NV, USA.
  2.  



Micro – ROS Servo

CPS presents a guide to setup the communication between Micro – ROS and ROS2 and control multiple servos attached to a PWM/Servo Driver board. Therefor the installation of ROS2, the setup of a Micro – ROS workspace, the establishment of the communication with a custom message and the implementation of third party libraries as well as the usage of two different RTOS system will be described step for step.

By combining the power of Micro ROS Foxy, ESP32 and a PCA9685 board, this project provides a way of controlling multiple servos. The setup has been tested on a Linux Ubuntu 20.04.6 LTS environment, allowing seamless communication and accurate servo positioning. The linked guide give some information about Micro – ROS and walks through the installation of ROS2, the setup of a Micro-ROS workspace, and the establishment of the communication between Micro-ROS and ROS2 using a custom message format called “ServoMessage”. Additionally, the guide covers the implementation of third-party libraries and the usage of two different RTOS systems. The code includes examples of how to use the Micro ROS Foxy framework to send and receive ServoMessages over the ROS2 network to control the attached servos. 

Components

Electronics:

  • Micro USB Cable
  • ESP32 Developement Board
  • 2 x 4,7 kΩ resistors 
  • PCA9685 PWM/Servo Board
  • Servos
  • 5V Power Supply

Links




Daniel Wagermaier, B.Sc.

Master Thesis Student at the Montanuniversität Leoben

Short bio: Daniel Wagermaier writes his master thesis at the chair of Cyber-Physical Systems (CPS). The title of the thesis is: ‘Improving fundamental metallurgical modelling using data-driven approaches.  

Research Interests

  • Machine and Deep Learning
  • Metallurgical Processes

Thesis

Contact

Daniel Wagermaier, B.Sc
Master Thesis Student at the Chair of Cyber-Physical-Systems
Montanuniversität Leoben
Franz-Josef-Straße 18, 
8700 Leoben, Austria 

Email: daniel.wagermaier@stud.unileoben.ac.at




Meeting Notes April 2023

Meeting 20/04

Research

  • reviewing paper for IROS
  • working on CR-VAE paper
  • experiment for the SL competition

PhD Registration

  • waiting for Toussaint’s response
  • maybe contact other professors?
    • rudolf

M.Sc. Students/Interns

  • Iye Szin
    • SL competition; deadline May 1

ML Course

  • grades for assignment 2 out

Miscellaneous

  • Summer Schools
    • ProbAI accepted (registration until 26/04)
    • ETH & RLSS waiting list
  • May-June Leaves
    • 19 May
    • 30 May – 6 June
  • Move May 1 to May 10 vacation

 

Meeting 27/04

Research

  • working on CR-VAE paper
  • image encoder for the SL competition

PhD Registration

  • Mentor: Rudolf Lioutikov
  • Application need signature from Rudolf

M.Sc. Students/Interns

  • Iye Szin
    • SL competition; deadline May 1

ML Course

  • Assignment 4: Regression

Miscellaneous

  • Summer Schools
    • Accepted:
      • M2LSS registered
      • ProbAI declined it
    • Rejected
      • ETH
      • RLSS
    • Applied:
      • LxMLS
      • Ellis Recommendation letter
  • internship application



Prof. Elmar Rueckert (Chair)

Chair of Cyber-Physical-Systems

Portrait Prof. Dr. Rueckert Elmar, January 2018

Short bio: Since March 2021 is Univ.-Prof. Dr. Elmar Rueckert the chair of the Cyber-Physical-Systems Institute at the Montanuniversität Leoben in Austria. He received his PhD in computer science at the Graz University of Technology in 2014 and worked for four years as senior researcher and research group leader at the Technical University of Darmstadt. Thereafter, he worked for three years as assistant professor at the University of Lübeck. His research interests include stochastic machine and deep learning, robotics and reinforcement learning and human motor control. In 2019, he was awarded with the ‘German Young Researcher Award’. 

Research Interests

  • Computational Modeling & Process Informatics: Cyber-Physical-Systems, Process Modeling in Metal Forming, Movement Decoding and Understanding, Brain- Computer-Interfaces, Electroencephalography, Spiking Neural Networks, Optimal Feedback Control, Muscle Synergies, Probabilistic Time-Series Models.
  • Machine & Deep Learning: Deep Networks, Graphical Models, Probabilistic Inference, Variational Inference, Gaussian Processes, Transfer Learning, Message Passing, Clustering, Bayesian Optimization, Lazy Learning, Genetic Programming, LSTMs.
  • Robotics: Stochastic Optimal Control, Movement Primitives, Reinforcement Learning, Imitation Learning, Morphological Computation, Quadruped Locomotion, Humanoid Postural Control, Grasping, Tactile Learning, Dynamic Control.
  • Human Motor Control & Science: Prosthesis Research & Rehabilitation, Motor Adaptation, Motor Skill Learning, Postural Control, Telepresence, Embodiment, Congruence in Teleoperation, Interactive Learning, Shared Control, Human Feedback.

Contact & Quick Links

Univ.-Prof. Dipl.-Ing. Dr.techn. Elmar Rueckert
Leiter des Lehrstuhls für Cyber-Physical-Systems
Montanuniversität Leoben
Roseggerstrasse 11 
8700 Leoben, Austria 

Phone:  +43 3842 402 – 1901 (Sekretariat CPS)
Email:   rueckert@ai-lab.science
Web:  https://cps.unileoben.ac.at
Chat: WEBEX

CV of Prof. Elmar Rueckert
DBLP
Frontiers Network
GitHub
Google Citations
LinkedIn
ORCID
Research Gate

VCard

Publcations

Journal Articles

Krukenfellner, Philip; Rueckert, Elmar; Flachberger, Helmut

Predicting condition states, based on displacement data, generated by acceleration sensors on industrial linear vibrating screens through neural networks Journal Article

In: IEEE Sensors Journal, pp. 1–13, 2024, ISBN: 1558-1748.

Links | BibTeX

Trimmel, Simone; Spörl, Philipp; Haluza, Daniela; Lashin, Nagi; Meisel, Thomas C.; Pitha, Ulrike; Prohaska, Thomas; Puschenreiter, Markus; Rückert, Elmar; Spangl, Bernhard; Wiedenhofer, Dominik; Irrgeher, Johanna

Green and blue infrastructure as model system for emissions of technology-critical elements Journal Article

In: Science of The Total Environment, vol. 934, 2024, ISBN: 0048-9697, (https://doi.org/10.1016/j.scitotenv.2024.173364).

Links | BibTeX

Kunavar, Tjasa; Jamšek, Marko; Avila-Mireles, Edwin Johnatan; Rueckert, Elmar; Peternel, Luka; Babič., Jan

The Effects of Different Motor Teaching Strategies on Learning a Complex Motor Task Journal Article

In: Sensors (MDPI), vol. 24, no. 4, pp. 1–17, 2024.

Links | BibTeX

Nwankwo, Linus; Fritze, Clemens; Bartsch, Konrad; Rueckert, Elmar

ROMR: A ROS-based Open-source Mobile Robot Journal Article

In: HardwareX, vol. 15, pp. 1–29, 2023.

Abstract | Links | BibTeX

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

Rottmann, Nils; Studt, Nico; Ernst, Floris; Rueckert, Elmar

ROS-Mobile: An Android™ application for the Robot Operating System Journal Article

In: Arxiv, 2022.

Links | BibTeX

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

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

Tanneberg, Daniel; Ploeger, Kai; Rueckert, Elmar; Peters, Jan

SKID RAW: Skill Discovery from Raw Trajectories Journal Article

In: IEEE Robotics and Automation Letters (RA-L), pp. 1–8, 2021, ISSN: 2377-3766, (© 20XX IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.).

Links | BibTeX

Jamsek, Marko; Kunavar, Tjasa; Bobek, Urban; Rueckert, Elmar; Babic, Jan

Predictive exoskeleton control for arm-motion augmentation based on probabilistic movement primitives combined with a flow controller Journal Article

In: IEEE Robotics and Automation Letters (RA-L), pp. 1–8, 2021, ISSN: 2377-3766, (© 20XX IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.).

Links | BibTeX

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

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

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

A novel Chlorophyll Fluorescence based approach for Mowing Area Classification Journal Article

In: IEEE Sensors Journal, 2020.

Links | BibTeX

Tanneberg, Daniel; Rueckert, Elmar; Peters, Jan

Evolutionary training and abstraction yields algorithmic generalization of neural computers Journal Article

In: Nature Machine Intelligence, pp. 1–11, 2020.

Links | BibTeX

Cartoni, E.; Mannella, F.; Santucci, V. G.; Triesch, J.; Rueckert, E.; Baldassarre, G.

REAL-2019: Robot open-Ended Autonomous Learning competition Journal Article

In: Proceedings of Machine Learning Research, vol. 123, pp. 142-152, 2020, (NeurIPS 2019 Competition and Demonstration Track).

Links | BibTeX

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

Tanneberg, Daniel; Peters, Jan; Rueckert, Elmar

Intrinsic Motivation and Mental Replay enable Efficient Online Adaptation in Stochastic Recurrent Networks Journal Article

In: Neural Networks - Elsevier, vol. 109, pp. 67-80, 2019, ISBN: 0893-6080, (Impact Factor of 7.197 (2017)).

Links | BibTeX

Sosic, Adrian; Zoubir, Abdelhak M.; Rueckert, Elmar; Peters, Jan; Koeppl, Heinz

Inverse Reinforcement Learning via Nonparametric Spatio-Temporal Subgoal Modeling Journal Article

In: Journal of Machine Learning Research (JMLR), vol. 19, no. 69, pp. 1-45, 2018.

Links | BibTeX

Paraschos, Alexandros; Rueckert, Elmar; Peters, Jan; Neumann, Gerhard

Probabilistic Movement Primitives under Unknown System Dynamics Journal Article

In: Advanced Robotics (ARJ), vol. 32, no. 6, pp. 297-310, 2018.

Links | BibTeX

Rueckert, Elmar; Camernik, Jernej; Peters, Jan; Babic, Jan

Probabilistic Movement Models Show that Postural Control Precedes and Predicts Volitional Motor Control Journal Article

In: Nature Publishing Group: Scientific Reports, vol. 6, no. 28455, 2016.

Links | BibTeX

Rueckert, Elmar; Kappel, David; Tanneberg, Daniel; Pecevski, Dejan; Peters, Jan

Recurrent Spiking Networks Solve Planning Tasks Journal Article

In: Nature Publishing Group: Scientific Reports, vol. 6, no. 21142, 2016.

Links | BibTeX

Rueckert, Elmar; d'Avella, Andrea

Learned parametrized dynamic movement primitives with shared synergies for controlling robotic and musculoskeletal systems Journal Article

In: Frontiers in Computational Neuroscience, vol. 7, no. 138, 2013.

Links | BibTeX

Rueckert, Elmar; Neumann, Gerhard; Toussaint, Marc; Maass, Wolfgang

Learned graphical models for probabilistic planning provide a new class of movement primitives Journal Article

In: Frontiers in Computational Neuroscience, vol. 6, no. 97, 2013.

Links | BibTeX

Rueckert, Elmar; Neumann, Gerhard

Stochastic Optimal Control Methods for Investigating the Power of Morphological Computation Journal Article

In: Artificial Life, vol. 19, no. 1, 2012.

Links | BibTeX

Conferences

Trimmel, Simone; Sp"orl, Philipp; Haluza, Daniela; Meisel, Thomas C; Pitha, Ulrike; Prohaska, Thomas; Puschenreiter, Markus; R"uckert, Elmar; Spangl, Bernhard; Wiedenhofer, Dominik; Irrgeher, Johanna

Determination of Technology-Critical Elements in Urban Plants and Water using Inductively Coupled Plasma Tandem Mass Spectrometry Conference

SETAC Europe 35th Annual Meeting, 2025, (Extended Abstract).

BibTeX

Dave, Vedant; Rueckert, Elmar

Denoised Predictive Imagination: An Information-theoretic approach for learning World Models Conference

European Workshop on Reinforcement Learning (EWRL), 2024.

Abstract | Links | BibTeX

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

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

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

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

Proceedings Articles

Neubauer, Melanie; Özdenizci, Ozan; Piater, Justus; Rueckert, Elmar

Sparsifying instance segmentation models for efficient vision-based industrial recycling Proceedings Article

In: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2025.

Links | BibTeX

Vanjani, Pankhuri; Mattes, Paul; Li, Maximilian Xiling; Dave, Vedant; Lioutikov, Rudolf

DisDP: Robust Imitation Learning via Disentangled Diffusion Policies Proceedings Article Forthcoming

In: Reinforcement Learning Conference (RLC), Reinforcement Learning Journal, Forthcoming.

BibTeX

Dave, Vedant; Rueckert, Elmar

Skill Disentanglement in Reproducing Kernel Hilbert Space Proceedings Article

In: Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), pp. 16153-16162, 2025.

Abstract | Links | BibTeX

Koinig, Gerald; Neubauer, Melanie; Martinelli, Walter; Radmann, Yves; Kuhn, Nikolai; Fink, Thomas; Rueckert, Elmar; Tischberger-Aldrian, Alexia

CNN-based copper reduction in shredded scrap for enhanced electric arc furnace steelmaking Proceedings Article

In: International Conference on Optical Characterization of Materials (OCM 2025), pp. 319-328, 2025, ISBN: 9783731514084.

Links | BibTeX

Nwankwo, Linus; Ellensohn, Bjoern; Dave, Vedant; Hofer, Peter; Forstner, Jan; Villneuve, Marlene; Galler, Robert; Rueckert, Elmar

EnvoDat: A Large-Scale Multisensory Dataset for Robotic Spatial Awareness and Semantic Reasoning in Heterogeneous Environments Proceedings Article

In: IEEE International Conference on Robotics and Automation (ICRA 2025)., 2025.

Links | BibTeX

Oezdenizci, Ozan; Rueckert, Elmar; Legenstein, Robert

Privacy-Aware Lifelong Learning Proceedings Article

In: International Conference on Learning Representations (ICLR), 2025.

Links | BibTeX

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

Feith, Nikolaus; Rueckert, Elmar

Integrating Human Expertise in Continuous Spaces: A Novel Interactive Bayesian Optimization Framework with Preference Expected Improvement Proceedings Article

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

Links | BibTeX

Feith, Nikolaus; Rueckert, Elmar

Advancing Interactive Robot Learning: A User Interface Leveraging Mixed Reality and Dual Quaternions Proceedings Article

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

Links | BibTeX

Neubauer, Melanie; Rueckert, Elmar

Semi-Autonomous Fast Object Segmentation and Tracking Tool for Industrial Applications Proceedings Article

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

Links | BibTeX

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), pp. 8013-8020, IEEE, 2024, ISBN: 979-8-3503-8457-4, (* equal contribution).

Abstract | Links | BibTeX

Nwankwo, Linus; Rueckert, Elmar

The Conversation is the Command: Interacting with Real-World Autonomous Robots Through Natural Language Proceedings Article

In: HRI '24: Companion of the 2024 ACM/IEEE International Conference on Human-Robot Interaction., pp. 808–812, ACM/IEEE Association for Computing Machinery, New York, NY, USA, 2024, ISBN: 9798400703232, (Published as late breaking results. Supplementary video: https://cloud.cps.unileoben.ac.at/index.php/s/fRE9XMosWDtJ339 ).

Abstract | Links | BibTeX

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

Yadav, Harsh; Xue, Honghu; Rudall, Yan; Bakr, Mohamed; Hein, Benedikt; Rueckert, Elmar; Nguyen, Ngoc Thinh

Deep Reinforcement Learning for Mapless Navigation of Autonomous Mobile Robot Proceedings Article

In: International Conference on System Theory, Control and Computing (ICSTCC), 2023, (October 11-13, 2023, Timisoara, Romania.).

Links | BibTeX

Nwankwo, Linus; Rueckert, Elmar

Understanding why SLAM algorithms fail in modern indoor environments Proceedings Article

In: International Conference on Robotics in Alpe-Adria-Danube Region (RAAD). , pp. 186 - 194, Cham: Springer Nature Switzerland., 2023.

Abstract | Links | BibTeX

Keshavarz, Sahar; Vita, Petr; Rueckert, Elmar; Ortner, Ronald; Thonhauser, Gerhard

A Reinforcement Learning Approach for Real-Time Autonomous Decision-Making in Well Construction Proceedings Article

In: Society of Petroleum Engineers - SPE Symposium: Leveraging Artificial Intelligence to Shape the Future of the Energy Industry, AIS 2023, Society of Petroleum Engineers., 2023, ISBN: 9781613999882.

Links | BibTeX

Xue, Honghu; Song, Rui; Petzold, Julian; Hein, Benedikt; Hamann, Heiko; Rueckert, Elmar

End-To-End Deep Reinforcement Learning for First-Person Pedestrian Visual Navigation in Urban Environments Proceedings Article

In: International Conference on Humanoid Robots (Humanoids 2022), 2022.

Abstract | Links | BibTeX

Dave, Vedant; Rueckert, Elmar

Predicting full-arm grasping motions from anticipated tactile responses Proceedings Article

In: International Conference on Humanoid Robots (Humanoids), pp. 464-471, IEEE, 2022, ISBN: 979-8-3503-0979-9.

Abstract | Links | BibTeX

Leonel, Rozo*; Vedant, Dave*

Orientation Probabilistic Movement Primitives on Riemannian Manifolds Proceedings Article

In: Conference on Robot Learning (CoRL), pp. 11, 2022, (* equal contribution).

Abstract | Links | BibTeX

Denz, R.; Demirci, R.; Cansev, E.; Bliek, A.; Beckerle, P.; Rueckert, E.; Rottmann, N.

A high-accuracy, low-budget Sensor Glove for Trajectory Model Learning Proceedings Article

In: International Conference on Advanced Robotics , pp. 7, 2021.

Links | BibTeX

Rottmann, N.; Denz, R.; Bruder, R.; Rueckert, E.

Probabilistic Approach for Complete Coverage Path Planning with low-cost Systems Proceedings Article

In: European Conference on Mobile Robots (ECMR 2021), 2021.

Links | BibTeX

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 Proceedings Article

In: 2020.

Abstract | Links | BibTeX

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

Exploiting Chlorophyll Fluorescense for Building Robust low-Cost Mowing Area Detectors Proceedings Article

In: IEEE SENSORS , pp. 1–4, 2020.

Links | BibTeX

Rottmann, N.; Kunavar, T.; Babič, J.; Peters, J.; Rueckert, E.

Learning Hierarchical Acquisition Functions for Bayesian Optimization Proceedings Article

In: International Conference on Intelligent Robots and Systems (IROS’ 2020), 2020.

Links | BibTeX

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

Parameter Optimization for Loop Closure Detection in Closed Environments Proceedings Article

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

Links | BibTeX

Tolga-Can Çallar, Elmar Rueckert; Böttger, Sven

Efficient Body Registration Using Single-View Range Imaging and Generic Shape Templates Proceedings Article

In: 54th Annual Conference of the German Society for Biomedical Engineering (BMT 2020), 2020.

Links | BibTeX

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

Stark, Svenja; Peters, Jan; Rueckert, Elmar

Experience Reuse with Probabilistic Movement Primitives Proceedings Article

In: Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems (IROS), 2019., 2019.

Links | BibTeX

Boettger, S.; Callar, T. C.; Schweikard, A.; Rueckert, E.

Medical robotics simulation framework for application-specific optimal kinematics Proceedings Article

In: Current Directions in Biomedical Engineering 2019, pp. 1–5, 2019.

Links | BibTeX

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

Loop Closure Detection in Closed Environments Proceedings Article

In: European Conference on Mobile Robots (ECMR 2019), 2019, ISBN: 978-1-7281-3605-9.

Links | BibTeX

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

Cataglyphis ant navigation strategies solve the global localization problem in robots with binary sensors Proceedings Article

In: Proceedings of International Conference on Bio-inspired Systems and Signal Processing (BIOSIGNALS), Prague, Czech Republic , 2019, ( February 22-24, 2019).

Links | BibTeX

Rueckert, Elmar; Jauer, Philipp; Derksen, Alexander; Schweikard, Achim

Dynamic Control Strategies for Cable-Driven Master Slave Robots Proceedings Article

In: Keck, Tobias (Ed.): Proceedings on Minimally Invasive Surgery, Luebeck, Germany, 2019, (January 24-25, 2019).

Links | BibTeX

Gondaliya, Kaushikkumar D.; Peters, Jan; Rueckert, Elmar

Learning to Categorize Bug Reports with LSTM Networks Proceedings Article

In: Proceedings of the International Conference on Advances in System Testing and Validation Lifecycle (VALID)., pp. 6, XPS (Xpert Publishing Services), Nice, France, 2018, ISBN: 978-1-61208-671-2, ( October 14-18, 2018).

Links | BibTeX

Rueckert, Elmar; Nakatenus, Moritz; Tosatto, Samuele; Peters, Jan

Learning Inverse Dynamics Models in O(n) time with LSTM networks Proceedings Article

In: Proceedings of the International Conference on Humanoid Robots (HUMANOIDS), 2017.

Links | BibTeX

Tanneberg, Daniel; Peters, Jan; Rueckert, Elmar

Efficient Online Adaptation with Stochastic Recurrent Neural Networks Proceedings Article

In: Proceedings of the International Conference on Humanoid Robots (HUMANOIDS), 2017.

Links | BibTeX

Stark, Svenja; Peters, Jan; Rueckert, Elmar

A Comparison of Distance Measures for Learning Nonparametric Motor Skill Libraries Proceedings Article

In: Proceedings of the International Conference on Humanoid Robots (HUMANOIDS), 2017.

Links | BibTeX

Thiem, Simon; Stark, Svenja; Tanneberg, Daniel; Peters, Jan; Rueckert, Elmar

Simulation of the underactuated Sake Robotics Gripper in V-REP Proceedings Article

In: Workshop at the International Conference on Humanoid Robots (HUMANOIDS), 2017.

Links | BibTeX

Tanneberg, Daniel; Peters, Jan; Rueckert, Elmar

Online Learning with Stochastic Recurrent Neural Networks using Intrinsic Motivation Signals Proceedings Article

In: Proceedings of the Conference on Robot Learning (CoRL), 2017.

Links | BibTeX

Tanneberg, Daniel; Paraschos, Alexandros; Peters, Jan; Rueckert, Elmar

Deep Spiking Networks for Model-based Planning in Humanoids Proceedings Article

In: Proceedings of the International Conference on Humanoid Robots (HUMANOIDS), 2016.

Links | BibTeX

Azad, Morteza; Ortenzi, Valerio; Lin, Hsiu-Chin; Rueckert, Elmar; Mistry, Michael

Model Estimation and Control of Complaint Contact Normal Force Proceedings Article

In: Proceedings of the International Conference on Humanoid Robots (HUMANOIDS), 2016.

Links | BibTeX

Kohlschuetter, Jan; Peters, Jan; Rueckert, Elmar

Learning Probabilistic Features from EMG Data for Predicting Knee Abnormalities Proceedings Article

In: Proceedings of the XIV Mediterranean Conference on Medical and Biological Engineering and Computing (MEDICON), 2016.

Links | BibTeX

Modugno, Valerio; Neumann, Gerhard; Rueckert, Elmar; Oriolo, Giuseppe; Peters, Jan; Ivaldi, Serena

Learning soft task priorities for control of redundant robots Proceedings Article

In: Proceedings of the International Conference on Robotics and Automation (ICRA), 2016.

Links | BibTeX

Sharma, David; Tanneberg, Daniel; Grosse-Wentrup, Moritz; Peters, Jan; Rueckert, Elmar

Adaptive Training Strategies for BCIs Proceedings Article

In: Cybathlon Symposium, 2016.

Links | BibTeX

Weber, Paul; Rueckert, Elmar; Calandra, Roberto; Peters, Jan; Beckerle, Philipp

A Low-cost Sensor Glove with Vibrotactile Feedback and Multiple Finger Joint and Hand Motion Sensing for Human-Robot Interaction Proceedings Article

In: Proceedings of the IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), 2016.

Links | BibTeX

Calandra, Roberto; Ivaldi, Serena; Deisenroth, Marc; Rueckert, Elmar; Peters, Jan

Learning Inverse Dynamics Models with Contacts Proceedings Article

In: Proceedings of the International Conference on Robotics and Automation (ICRA), 2015.

Links | BibTeX

Rueckert, Elmar; Mundo, Jan; Paraschos, Alexandros; Peters, Jan; Neumann, Gerhard

Extracting Low-Dimensional Control Variables for Movement Primitives Proceedings Article

In: Proceedings of the International Conference on Robotics and Automation (ICRA), 2015.

Links | BibTeX

Paraschos, Alexandros; Rueckert, Elmar; Peters, Jan; Neumann, Gerhard

Model-Free Probabilistic Movement Primitives for Physical Interaction Proceedings Article

In: Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems (IROS), 2015.

Links | BibTeX

Rueckert, Elmar; Lioutikov, Rudolf; Calandra, Roberto; Schmidt, Marius; Beckerle, Philipp; Peters, Jan

Low-cost Sensor Glove with Force Feedback for Learning from Demonstrations using Probabilistic Trajectory Representations Proceedings Article

In: ICRA 2015 Workshop on Tactile and force sensing for autonomous compliant intelligent robots, 2015.

Links | BibTeX

Rueckert, Elmar; Mindt, Max; Peters, Jan; Neumann, Gerhard

Robust Policy Updates for Stochastic Optimal Control Proceedings Article

In: Proceedings of the International Conference on Humanoid Robots (HUMANOIDS), 2014.

Links | BibTeX

Rueckert, Elmar; d'Avella, Andrea

Learned Muscle Synergies as Prior in Dynamical Systems for Controlling Bio-mechanical and Robotic Systems Proceedings Article

In: Abstracts of Neural Control of Movement Conference (NCM), Conference Talk, pp. 27–28, 2013.

Links | BibTeX

Rueckert, Elmar; Neumann, Gerhard

A study of Morphological Computation by using Probabilistic Inference for Motor Planning Proceedings Article

In: Proceedings of the 2nd International Conference on Morphological Computation (ICMC), pp. 51–53, 2011.

Links | BibTeX

Masters Theses

Rueckert, Elmar

Simultaneous localisation and mapping for mobile robots with recent sensor technologies Masters Thesis

Technical University Graz, 2010.

Links | BibTeX

PhD Theses

Rueckert, Elmar

Biologically inspired motor skill learning in robotics through probabilistic inference PhD Thesis

Technical University Graz, 2014.

Links | BibTeX

Workshops

Nwankwo, Linus; Rueckert, Elmar

Multimodal Human-Autonomous Agents Interaction Using Pre-Trained Language and Visual Foundation Models Workshop

2024, ( In Workshop of the 2024 ACM/IEEE International Conference on HumanRobot Interaction (HRI ’24 Workshop), March 11–14, 2024, Boulder, CO, USA. ACM, New York, NY, USA).

Abstract | Links | BibTeX

Yadav, Harsh; Xue, Honghu; Rudall, Yan; Bakr, Mohamed; Hein, Benedikt; Rueckert, Elmar; Nguyen, Thinh

Deep Reinforcement Learning for Autonomous Navigation in Intralogistics Workshop

2023, (European Control Conference (ECC) Workshop, Extended Abstract.).

Abstract | Links | BibTeX

Dave, Vedant; Rueckert, Elmar

Can we infer the full-arm manipulation skills from tactile targets? Workshop

Advances in Close Proximity Human-Robot Collaboration Workshop, International Conference on Humanoid Robots (Humanoids), 2022.

Abstract | Links | BibTeX

Track Record

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1 Lehrlingsstelle (4 Jahre), 2303API

1 Lehrlingsstelle für den Lehrberuf “Informationstechnologie mit Schwerpunkt Betriebstechnik” (Lehrzeit 4 Jahre) am Department Product Engineering – Lehrstuhl für Cyber Physical Systems ab der Montanuniversität Leoben ab 01.09.2023 zu besetzen (die Lehrlingsentschädigung gemäß Kollektivvertrag beträgt im 1. Lehrjahr 863,20 € (14x jährlich)).

Besondere Erfordernisse:

• Abschluss der allgemeinen Schulpflicht
• Interesse an Technik
• Gute Englischkenntnisse in Wort und Schrift

Aufgabengebiet:
• Auswählen, Einrichten, Synchronisieren und in Betrieb nehmen von (auch mobilen) Benutzerendgeräten und Peripheriegeräten sowie Konfigurieren von Endgeräten.
• Auswählen und in Betrieb nehmen von neuen Netzkomponenten.
• Konzipieren und Planen von Datenspeichersystemen sowie Implementieren und Testen von Datenspeichersystemen inklusive Backup-Lösungen.
• Konfigurieren von Serversystemen und deren Basisdiensten sowie Testen der Konfiguration.

Erwünschte Zusatzqualifikationen:
• Programmier-Grundkenntnisse in einer aktuellen Programmiersprache (C, C++, Python o.ä.)

Referenznummer: 2303API

Ende der Bewerbefrist: 04.05.2023

Die Montanuniversität Leoben strebt eine Erhöhung des Frauenanteiles an und fordert deshalb qualifizierte Frauen ausdrücklich zur Bewerbung auf. Frauen werden bei gleicher Qualifikation wie der bestgeeignete Mitbewerber vorrangig aufgenommen.

Für Ihre Bewerbung verwenden Sie bitte unser Online Bewerbungsformular auf der Homepage: https://www.unileoben.ac.at/jobs




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