Author: Elmar Rueckert

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190.012 Introduction to Machine Learning (2SH L, SS)

Elmar Rueckert teaching_pastlectures

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.

The lecture 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

 

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:

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. 

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MATLAB Code of Probabilistic Movement Primitives for Motion Analysis

Elmar Rueckert research_software

Matlab Code Link

https://cps.unileoben.ac.at/wp/resources/code/MATLAB_ProbabilisticTrajectoryModel_2016Rueckert.zip

Publication where the Code was used

2016

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

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

MATLAB Code of Spiking Neural Networks for Robot Motion Planning

Elmar Rueckert research_software

Matlab Code Link

https://cps.unileoben.ac.at/wp/resources/code/MATLAB_SpikingNeuralPlanning_2016Rueckert.zip

Publication where the Code was used

2016

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

Recurrent Spiking Networks Solve Planning Tasks

Stochastic Neural Networks for Robot Motion Planning

Elmar Rueckert research_videos

Video

Link to the file

You may use this video for research and teaching purposes. Please cite the Chair of Cyber-Physical-Systems or the corresponding research paper. 

Publications

2016

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

Deep Spiking Networks for Model-based Planning in Humanoids

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

Recurrent Spiking Networks Solve Planning Tasks

Learning Multimodal Solutions with Movement Primitives

Elmar Rueckert research_videos

Video

Link to the file

You may use this video for research and teaching purposes. Please cite the Chair of Cyber-Physical-Systems or the corresponding research paper. 

Publications

2015

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

Extracting Low-Dimensional Control Variables for Movement Primitives

Dynamic Control of a CableBot

Elmar Rueckert projects_research_past

Building a CableBot and Learning the Dynamics Model and the Controller

Controlling cable driven master slave robots is a challenging task. Fast and precise motion planning requires stabilizing struts which are disruptive elements in robot-assisted surgeries. In this work, we study parallel kinematics with an active deceleration mechanism that does not require any hindering struts for stabilization. 

Reinforcement learning is used to learn control gains and model parameters which allow for fast and precise robot motions without overshooting. The developed mechanical design as well as the controller optimization framework through learning can improve the motion and tracking performance of many widely used cable-driven master slave robots in surgical robotics.

Project Consortium

  • Montanuniversität Leoben

Related Work

H Yuan, E Courteille, D Deblaise (2015). Static and dynamic stiffness analyses of cable-driven parallel robots with non-negligible cable mass and elasticity, Mechanism and Machine Theory, 2015 – Elsevier, link.

MA Khosravi, HD Taghirad (2011). Dynamic analysis and control of cable driven robots with elastic cables, Transactions of the Canadian Society for Mechanical Engineering 35.4 (2011): 543-557, link.

Publications

2019

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

Dynamic Control Strategies for Cable-Driven Master Slave Robots

Active transfer learning with neural networks through human-robot interactions (TRAIN)

Elmar Rueckert projects_thirdparty

DFG Project 07/2020-01/2025

In our vision, autonomous robots are interacting with humans at industrial sites, in health care, or at our homes managing the household. From a technical perspective, all these application domains require that robots process large amounts of data of noisy sensor observations during the execution of thousands of different motor and manipulation skills. From the perspective of many users, programming these skills manually or using recent learning approaches, which are mostly operable only by experts, will not be feasible to use intelligent autonomous systems in tasks of everyday life.

In this project, we aim at improving robot skill learning with deep networks considering human feedback and guidance. The human teacher is rating different transfer learning strategies in the artificial neural network to improve the learning of novel skills by optimally exploiting existing encoded knowledge. Neural networks are ideally suited for this task as we can gradually increase the number of transferred parameters and can even transition between the transfer of task specific knowledge to abstract features encoded in deeper layers. To consider this systematically, we evaluate subjective feedback and physiological data from user experiments and elaborate assessment criteria that enable the development of human-oriented transfer learning methods. In two main experiments, we first investigate how users experience transfer learning and then examine the influence of shared autonomy of humans and robots. This will result in a methodical robot skill learning framework that adapts to the users’ needs, e.g., by adjusting the degree of autonomy of the robot to laymen requirements. Even though we evaluate the learning framework focusing on pick and place tasks with anthropomorphic robot arms, our results will be transferable to a broad range of human-robot interaction scenarios including collaborative manipulation tasks in production and assembly, but also for designing advanced controls for rehabilitation and household robots.

Project Consortium

  • Friedrich-Alexander-Universität Erlangen-Nürnberg

  • Montanuniversität Leoben

Links

Details on the research project can be found on the project webpage.

 

Publications

2021

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

SKID RAW: Skill Discovery from Raw Trajectories

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

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

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

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

Learning Hierarchical Acquisition Functions for Bayesian Optimization

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