Roland Siegwart: Katalogdaten im Frühjahrssemester 2018 |
| Name | Herr Prof. Dr. Roland Siegwart |
| Lehrgebiet | Autonome Systeme |
| Adresse | Inst. f. Robotik u. Intell. Syst. ETH Zürich, LEE J 205 Leonhardstrasse 21 8092 Zürich SWITZERLAND |
| Telefon | +41 44 632 23 58 |
| Fax | +41 44 632 11 81 |
| rolandsi@ethz.ch | |
| Departement | Maschinenbau und Verfahrenstechnik |
| Beziehung | Ordentlicher Professor |
| Nummer | Titel | ECTS | Umfang | Dozierende | |
|---|---|---|---|---|---|
| 151-0073-11L | Indoor Mobility Robot Voraussetzung: Besuch der Lerneinheit 151-0073-10L Indoor Mobility Robot im HS17. | 14 KP | 15A | R. Siegwart | |
| Kurzbeschreibung | Im Team ein Produkt von A-Z entwickeln und realisieren! Anwenden und Vertiefen des bestehenden Wissens, Arbeiten in Teams, Selbständigkeit, Problemstrukturierung, Lösungsfindung in unscharfen Problemstellungen, Systembeschreibung und -simulation, Präsentation und Dokumentation, Realisationsfähigkeit, Werkstatt- und Industriekontakte, Anwendung modernster Ingenieur-Werkzeuge (Matlab, Simulink usw). | ||||
| Lernziel | Die vielfältigen Lernziele dieses Fokus-Projektes sind: - Synthetisieren und Vertiefen des theoretischen Wissens aus den Grundlagenfächern des 1.-4. Semesters - Teamorganisation, Arbeiten in Teams, Steigerung der sozialen Kompetenz - Selbständigkeit, Initiative, selbständiges Lernen neuer Themeninhalte - Problemstrukturierung, Lösungsfindung in unscharfen Problemstellungen, Suchen von Informationen - Systembeschreibung und -simulation - Präsentationstechnik, Dokumentationserstellung - Entscheidungsfähigkeit, Realisationsfähigkeit - Werkstatt- und Industriekontakte - Erweiterung und Vertiefung von Sachwissen - Beherrschung modernster Ingenieur-Werkzeuge (Matlab, Simulink, CAD, CAE, PDM) | ||||
| 151-0073-21L | Robotic Elephant Trunk Voraussetzung: Besuch der Lerneinheit 151-0073-20L Robotic Elephant Trunk im HS17. | 14 KP | 15A | M. Hutter, R. Siegwart | |
| Kurzbeschreibung | Im Team ein Produkt von A-Z entwickeln und realisieren! Anwenden und Vertiefen des bestehenden Wissens, Arbeiten in Teams, Selbständigkeit, Problemstrukturierung, Lösungsfindung in unscharfen Problemstellungen, Systembeschreibung und -simulation, Präsentation und Dokumentation, Realisationsfähigkeit, Werkstatt- und Industriekontakte, Anwendung modernster Ingenieur-Werkzeuge (Matlab, Simulink usw). | ||||
| Lernziel | Die vielfältigen Lernziele dieses Fokus-Projektes sind: - Synthetisieren und Vertiefen des theoretischen Wissens aus den Grundlagenfächern des 1.-4. Semesters - Teamorganisation, Arbeiten in Teams, Steigerung der sozialen Kompetenz - Selbständigkeit, Initiative, selbständiges Lernen neuer Themeninhalte - Problemstrukturierung, Lösungsfindung in unscharfen Problemstellungen, Suchen von Informationen - Systembeschreibung und -simulation - Präsentationstechnik, Dokumentationserstellung - Entscheidungsfähigkeit, Realisationsfähigkeit - Werkstatt- und Industriekontakte - Erweiterung und Vertiefung von Sachwissen - Beherrschung modernster Ingenieur-Werkzeuge (Matlab, Simulink, CAD, CAE, PDM) | ||||
| 151-0623-00L | ETH Zurich Distinguished Seminar in Robotics, Systems and Controls  Students for other Master's programmes in Department Mechanical and Process Engineering cannot use the credit in the category Core Courses | 1 KP | 1S | B. Nelson, M. Chli, R. Gassert, M. Hutter, W. Karlen, R. Riener, R. Siegwart | |
| Kurzbeschreibung | This course consists of a series of seven lectures given by researchers who have distinguished themselves in the area of Robotics, Systems, and Controls. | ||||
| Lernziel | Obtain an overview of various topics in Robotics, Systems, and Controls from leaders in the field. Please see http://www.msrl.ethz.ch/education/distinguished-seminar-in-robotics--systems---controls--151-0623-0.html for a list of upcoming lectures. | ||||
| Inhalt | This course consists of a series of seven lectures given by researchers who have distinguished themselves in the area of Robotics, Systems, and Controls. MSc students in Robotics, Systems, and Controls are required to attend every lecture. Attendance will be monitored. If for some reason a student cannot attend one of the lectures, the student must select another ETH or University of Zurich seminar related to the field and submit a one page description of the seminar topic. Please see http://www.msrl.ethz.ch/education/distinguished-seminar-in-robotics--systems---controls--151-0623-0.html for a suggestion of other lectures. | ||||
| Voraussetzungen / Besonderes | Students are required to attend all seven lectures to obtain credit. If a student must miss a lecture then attendance at a related special lecture will be accepted that is reported in a one page summary of the attended lecture. No exceptions to this rule are allowed. | ||||
| 151-0634-00L | Perception and Learning for Robotics Number of participants limited to: 30 To apply for the course please create a CV in pdf of max. 2 pages, including your machine learning and/or robotics experience. Please send the pdf to cesarc@ethz.ch for approval. | 4 KP | 1A | C. D. Cadena Lerma, I. Gilitschenski, R. Siegwart | |
| Kurzbeschreibung | This course covers tools from statistics and machine learning enabling the participants to deploy these algorithms as building blocks for perception pipelines on robotic tasks. All mathematical methods provided within the course will be discussed in context of and motivated by example applications mostly from robotics. The main focus of this course are student projects on robotics. | ||||
| Lernziel | Applying Machine Learning methods for solving real-world robotics problems. | ||||
| Inhalt | Deep Learning for Perception; (Deep) Reinforcement Learning; Graph-Based Simultaneous Localization and Mapping | ||||
| Skript | Slides will be made available to the students. | ||||
| Literatur | Will be announced in the first lecture. | ||||
| Voraussetzungen / Besonderes | The students are expected to be familiar with material of the "Recursive Estimation" and the "Learning and Intelligent Systems" lectures. Particularly understanding of basic machine learning concepts, stochastic gradient descent for neural networks, reinforcement learning basics, and knowledge of Bayesian Filtering are required. Furtheremore, good knowledge of programming in C++ and Python is required. | ||||
| 151-0854-00L | Autonomous Mobile Robots | 5 KP | 4G | R. Siegwart, M. Chli, J. Nieto | |
| Kurzbeschreibung | The objective of this course is to provide the basics required to develop autonomous mobile robots and systems. Main emphasis is put on mobile robot locomotion and kinematics, envionmen perception, and probabilistic environment modeling, localizatoin, mapping and navigation. Theory will be deepened by exercises with small mobile robots and discussed accross application examples. | ||||
| Lernziel | The objective of this course is to provide the basics required to develop autonomous mobile robots and systems. Main emphasis is put on mobile robot locomotion and kinematics, envionmen perception, and probabilistic environment modeling, localizatoin, mapping and navigation. | ||||
| Skript | This lecture is enhanced by around 30 small videos introducing the core topics, and multiple-choice questions for continuous self-evaluation. It is developed along the TORQUE (Tiny, Open-with-Restrictions courses focused on QUality and Effectiveness) concept, which is ETH's response to the popular MOOC (Massive Open Online Course) concept. | ||||
| Literatur | This lecture is based on the Textbook: Introduction to Autonomous Mobile Robots Roland Siegwart, Illah Nourbakhsh, Davide Scaramuzza, The MIT Press, Second Edition 2011, ISBN: 978-0262015356 | ||||
| 401-5860-00L | Seminar in Robotics for CSE | 4 KP | 2S | R. Siegwart | |
| Kurzbeschreibung | This course provides an opportunity to familiarize yourself with the advanced topics of robotics and mechatronics research. The seminar consists of a literature study, including a report and a presentation. | ||||
| Lernziel | The students are familiar with the challenges of the fascinating and interdisciplinary field of Robotics and Mechatronics. They are introduced in the basics of independent non-experimental scientific research and are able to summarize and to present the results efficiently. | ||||
| Inhalt | This 4 ECTS course requires each student to discuss a study plan with the lecturer and select minimum 10 relevant scientific publications to read through. At the end of semester, the results should be presented in an oral presentation and summarized in a report. | ||||