Author: Elmar Rueckert

image_pdfimage_print

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. 

We use the C++ libfranka library in our own ROS project for learning complex manipulation skills. 

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. 

190.003 CPS Research Seminar I (2SH SE, SS)

Elmar Rueckert teaching_currentseminars

Univ.-Prof. Dr. Elmar Rueckert is organizing this research seminar. Topics include research in AI, machine and deep learning, robotics, cyber-physical-systems and process informatics. 

Language:
English only

Are you an undergraduate,  graduate, or doctoral student and want to learn more about AI?

This course will give you the opportunity to listen to research presentations of latest achievements. The target audience are non-experts. Thus no prior knowledge in AI is required.

To get the ECTS credits, you will select a research paper, read it and present it within the research seminar (10-15 min presentation). Instead of selecting a paper of our list, you can also suggest a paper. This suggestion has to be discussed with Univ.-Prof. Dr. Elmar Rueckert first.

After the presentation, the paper is discussed for 10-15 min.

Further, external presenters that  are leading researchers in AI will be invited. External speakers will present their research in 30-45 min, followed by a 15 min discussion.

Location & Time

List of Talks and Dates

The specified time windows do not include discussions. 

Some Research Paper Candidates

 

Humanoid Robotics Exercise (RO5300)

Elmar Rueckert teaching_pastlectures

Univ.-Prof. Dr. Elmar Rueckert was teaching this course at the University of Luebeck in 2018, 2019 and 2020.

Teaching Assistant:
Nils Rottmann, M.Sc.

Language:
English and German

Course Details

On this page you can find short videos which explain the exercise 02 given in the lecture Humanoid Robotics.

Datenstrukturen und Algorithmen (708.031)

Elmar Rueckert teaching_pastlectures

Univ.-Prof. Dr. Elmar Rueckert was teaching this course at the Technical University Graz in the winter semester in 2012/13 and in 2013/14.

Language:
German only

Link to the university's course page

Link to the course in the TUG online system.

Course Details

  • Elementare Datenstrukturen (Felder, Stapel, Schlange).
  • Asymptotische Laufzeitanalyse von Programmen (O-Notation).
  • Sortierverfahren (Einfügen, Auswahl, Quicksort, Mergesort, Heapsort, Fachverteilung, i-größte Zahl, Randomisierung, untere Laufzeitschranken).
  • Gestreute Speicherung (Hashing; Überläuferlisten, offene Adressierung, Hashfunktionen).
  • Suchmethoden (sequentiell, binär, interpolativ, quadratische Binärsuche).
  • Baumstrukturen (Binärbäume, (a-b)-Bäume, amortisierte Umstrukturierungskosten, optimale Suchbäume).
  • Dynamische Datenverwaltung (Wörterbuchproblem, Warteschlangenproblem, Union-Find Problem).
  • Algorithmische Techniken (Inkrementelles Einfügen, Elimination, Divide & Conquer, dynamisches Programmieren, Randomisierung).

Mit bis zu 390 Teilnehmern*innen pro Vorlesung.

Literature

  • Cormen, Leiserson, Rivest: Introduction to Algorithms, MIT Press, London, 1990.

Probabilistic Learning for Robotics (RO5601)

Elmar Rueckert teaching_pastlectures

Univ.-Prof. Dr. Elmar Rueckert was teaching this course at the University of Luebeck in the winter semester in 2018.

Language:
English only

Course Details

[WS2018/19] In the winter semester, I will teach a course on Probabilistic Learning for Robotics which covers advanced topics including graphical models, factor graphs, probabilistic inference for decision making and planning, and computational models for inference in neuroscience. The lecture will take place in the Seminarraum Informatik 5 (Von Neumann) 2.132 from 12.00 – 14.00 on selected Thursdays.

In accompanying exercises and hands on tutorials the students will experiment with state of the art machine learning methods and robotic simulation tools. In particular, Mathworks’ MATLAB,  the robot middleware ROS and the simulation tool V-Rep will be used. The exercises and tutorials will also take place in the seminar room  2.132 on selected Fridays (see the course materials and dates below).

Prerequisites (recommended) 

  • Humanoid Robotics (RO5300)
  • Robotics (CS2500)

Follow this link to register for the course: https://moodle.uni-luebeck.de/course/view.php?id=3793.

Location & Time: Room: Seminarraum Informatik 5 (Von Neumann) 2.132 12.15 – 14.00

Course materials and dates

  1. Probabilistic Learning for Robotics Intro (L1: October, 18th)
  2. Introductions to Topics I-III: Bayesian Inference, Gaussian Processes & Kalman/P. Filters (L2: October, 25th)
  3. Introductions to Topics IV-VI: Bayesian Optimization, Spiking Networks for Planning, Probabilistic Movement Primitives (L3: November, 1st)