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

• 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.

[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

In this research seminar we discuss state of the art research topics in robotics, machine learning and autonomous systems. Presenters are invited guest speakers, researcher, and graduate and under graduate students.

The seminar takes place on Fridays where in

• WS2018/19 we meet in our Seminarraum Informatik 2/3 (Cook/Karp) at 1 pm s.t.
• SS2019 we meet in our  Seminarraum Mathematik 2 ( Banach ) at 11 am s.t.

Here is the event calendar with upcoming talks. Click on this link to add this calendar to your account. You may register for this course via moodle.

The lecture Reinforcement Learning belongs to the Module Robot Learning (RO4100).

In the winter semester, Prof. Dr. Elmar Rueckert is teaching the course Probabilistic Machine Learning – PML (RO5101 T).

In the summer semester, Prof. Dr. Elmar Rueckert is teaching the course Reinforcement Learning – RL (RO4100 T).

Important Remarks

• Students will receive a single grade for the Module Robot Learning (RO4100) based on the average grade of PML and RL (rounded down in favor of the students).
• This course is organized through online lectures and exercises. Details to the organizations will be discussed in our
  
  FIRST MEETING: 17.04.2020 12:15-13:45 
  
  using the WEBEX tool. Please follow the instructions of the ITSC here to setup your computer. Click on the links to create a google calendar event, joint the WEBEX meeting or to access the online slides.

Dates & Times of the Online Webex Meetings

• Lectures are organized on FRIDAYS, 12:15-13:45, WEBEX Link
• Exercises are organized on THURSDAYS, 09:15-10:00, WEBEX Link

Course description

1. Introduction to Robotics and Reinforcement Learning (Refresher on Robotics, kinematics, model learning and learning feedback control strategies).
2. Foundations of Decision Making (Reward Hypothesis, Markov Property, Markov Reward Process, Value Iteration, Markov Decision Process, Policy Iteration, Bellman Equation, Link to Optimal Control).
3. Principles of Reinforcement Learning (Exploration and Exploitation strategies, On & Off-policy learning, model-free and model-based policy learning, Algorithmic principles: Q-Learning, SARSA, (Multi-step) TD-Learning, Eligibility Traces).
4. Deep Reinforcement Learning (Introduction to Deep Networks, Stochastic Gradient Descent, Function Approximation, Fitted Q-Iteration, (Double) Deep Q-Learning, Policy-Gradient approaches, Recent research results in Stochastic Deep Neural Networks).

The learning objectives / qualifications are

• Students get a comprehensive understanding of basic decision making theories, assumptions and methods.
• Students learn to analyze the challenges in a reinforcement learning application and to identify promising learning approaches.
• Students will understand the difference between deterministic and probabilistic policies and can define underlying assumptions and requirements for learning them.
• Students understand and can apply advanced policy gradient methods to real world problems.
• Students know how to analyze the learning results and improve the policy learner parameters.
• Students understand how the basic concepts are used in current state of the art research in robot reinforcement learning and in deep neural networks.

Follow this link to register for the course: https://moodle.uni-luebeck.de

Requirements

Basic knowledge in Machine Learning and Neural Networks is required. It is highly recommended to attend any of (but not restricted to) the following courses Probabilistic Machine Learning (RO 5101 T), Artificial Intelligence II (CS 5204 T), Machine Learning (CS 5450), Medical Deep Learning (CS 4374) prior to attending this course. The students will also experiment with state-of-the-art Reinforcement Learning (RL) methods on benchmark RL simulator (OpenAI Gym, Pybullet), which requires strong Python programming skills and knowledge on Pytorch is preferred. All assignment related materials have been tested on a windows machine (Win10 platform).