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Vorlesung: Robot Learning

Quick Facts

Description

(:youtube qtqubguikMk :) In the 1980s, classical robotics already reached a high level of maturity and it was able to produce large factories. For example, cars factories were completely automated. Despite these impressive achievements, unlike personal computers, modern service robots still did not leave the factories and take a seat as robot companions on our side. The reason is that it is still harder for us to program robots than computers. Usually, modern companion robots learn their duties by a mixture of imitation and trial-and-error. This new way of programming robots has a crucial consequence in the field of industry: the programming cost increases, making mass production impossible.

However, in research, this approach had a great influence and over the last ten years all top universities in the world conduct research in this area. The success of these new methods has been demonstrated in a variety of sample scenarios: autonomous helicopters learning from teachers complex maneuver, walking robot learning impressive balancing skills, self-guided cars hurtling at high speed in racetracks, humanoid robots balancing a bar in their hand and anthropomorphic arms cooking pancakes.

Accordingly, this class serves as an introduction to autonomous robot learning. The class focuses on approaches from the fields of robotics, machine learning, model learning, imitation learning, reinforcement learning and motor primitives. Application scenarios and major challenges in modern robotics will be presented as well.

We pay particular attention to interactions with the participants of the lecture, asking multiple question and appreciating enthusiastic students.

We also offer a parallel project, the Robot Learning: Integrated Project. It is designed to enable participants to understand robot learning in its full depth by directly applying methods presented in this class to real or simulated robots. We suggest motivated students to attend it as well, either during or after the Robot Learning Class!

Contents

The course gives a introduction to robotics and machine learning methods. The following topics are expected to be covered throughout the semester.

• Robotics Basics
• Machine Learning Basics
• Model Learning
• Imitation Learning
• Optimal Decision Making
• Optimal Control
• Reinforcement Learning
• Policy Search
• Inverse Reinforcement Learning

Requirements

Mathematics from the first semesters, basic programming abilities, computer science basics.

Literature

The most important books for this class are:

1. B. Siciliano, L. Sciavicco. Robotics: Modelling, Planning and Control, Springer
2. C.M. Bishop. Pattern Recognition and Machine Learning, Springer free online copy
3. R. Sutton, A. Barto. Reinforcement Learning - An Introduction, MIT Press free online copy

Additionally, the following papers are useful for specific topics:

1. •       Bib
     Deisenroth, M. P.; Neumann, G.; Peters, J. (2013). A Survey on Policy Search for Robotics, Foundations and Trends in Robotics, 21, pp.388-403.
2. •       Bib
     Kober, J.; Bagnell, D.; Peters, J. (2013). Reinforcement Learning in Robotics: A Survey, International Journal of Robotics Research (IJRR), 32, 11, pp.1238-1274.
3. •     Bib
     Nguyen Tuong, D.; Peters, J. (2011). Model Learning in Robotics: a Survey, Cognitive Processing, 12, 4.

Teaching Staff

Lectures will be held by Jan Peters and additionally supervised by Daniel Tanneberg and Marco Ewerton.

Jan Peters heads the Intelligent Autonomous Systems Lab at the Department of Computer Science at the TU Darmstadt. Jan has studied computer science, electrical, control, mechanical and aerospace engineering. You can find Jan Peters in the Robert-Piloty building S2 | 02 in room E314. You can also contact him through mail@jan-peters.net.

Daniel Tanneberg is a Ph.D. student at the Intelligent Autonomous Systems (IAS) Group at the Technical University of Darmstadt since October 2015. He is investigating the applicability and properties of (spiking/stochastic) deep neural networks for open-ended robot learning. He is working on the GOAL-Robots project, that aims at developing goal-based open-ended autonomous learning robots; building lifelong learning robots. You can contact him by email at daniel@robot-learning.de .

Marco Ewerton is a Ph.D. student at the IAS since January 2015. He works on the BIMROB project, which investigates how humans and robots can improve their movements by interacting with each other. Before his Ph.D., Marco completed his Master Degree in Electrical Engineering at the TU Darmstadt. You can find him in the Robert-Piloty building S2 | 02 room E226. You can also contact him through ewerton@ias.tu-darmstadt.de.

For further inquiries do not hesitate to contact us immediately!