Search result: Catalogue data in Spring Semester 2023
Mechanical Engineering Master  | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
 Core Courses | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Robotics, Systems and Control The courses listed in this category “Core Courses” are recommended. Alternative courses can be chosen in agreement with the tutor. . | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Number | Title | Type | ECTS | Hours | Lecturers | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 151-0314-00L | Information Technologies in the Digital Product | W | 4 credits | 3G | E. Zwicker, R. Montau | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | Digitalization across the Product Lifecycle with Objectives, Concepts and Methods, Smart Connected Products through Industry 4.0 Concepts for Digitalization: Product Structures, Optimization of Engineering Processes with digital models in Sales, Production, Service, Digital Twin versus Digital Thread PLM Fundamentals: Objects, Structures, Processes, Integrations, Visualization Best Practices | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Students learn the fundamentals and concepts of Digitalization along the in the product lifecycle on the foundation of Product Lifecycle Management (PLM) technologies, the usage of databases, the integration of CAx systems and Visualization/AR, the configuration of computer-based collaboration leveraging IT-standards as well as variant and configuration management to enable an efficient utilization of the digital product approach in industry 4.0. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | Possibilities and potential of modern IT applications focussing on PLM and CAx technologies for targeted utilization in the context of product platform - business processes - IT tools. Introduction to the concepts of Product Lifecycle Management (PLM): information modeling, data management, revision, usage and distribution of product data. Structure and functional principles of PLM systems. Integration of new IT technologies in business processes. Possibilities of publication and automatic configuration of product variants via the Internet. Using state-of-the-art information technologies to develop products globally across distributed locations. Interfaces in computer-integrated product development. Selection, configuration, adaptation and introduction of PLM systems. Examples and case studies for industrial usage of modern information technologies. Learning modules: - Introduction to Digitalization (Digital Product, PLM technology) - Database technology (foundation of digitalization) - Object Management - Object Classification - Object identification with Part Numbering Systems - CAx/PLM integration with Visualization/AR - Workflow & Change Management - Interfaces of the Digital Product - Enterprise Application Integration (EAI) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | Didactic concept / learning materials: The course consists of lectures and exercises based on practical examples including the use of modern Web-native PLM applications on Cloud. Provision of lecture handouts and script digitally in Moodle. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Prerequisites: None Recommended: Fokus-Project, interest in Digitalization Lecture appropriate for D-MAVT, D-MTEC, D-ITET and D-INFK Testat/Credit Requirements / Exam: - execution of exercises in teams (recommended) - Oral exam 30 minutes, based on concrete problem cases | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Competencies |
| |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 151-0318-00L | Ecodesign - Environmental-Oriented Product Development | W | 4 credits | 3G | R. Züst | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | Ecodesign has a great potential to improve the environmental performance of a product. Main topics of the lecture: Motivation for Ecodesign; Methodical basics (defining environmental aspects; improvement strageies and measures); Ecodesign implementation (systematic guidance on integrating environmental considerations into product development) in a small project. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Experience shows that a significant part of the environmental impact of a business venture is caused by its own products in the pre and post-production areas. The goal of eco design is to reduce the total effect of a product on the environment in all phases of product life. The systematic derivation of promising improvement measures at the start of the product development process is a key skill that will be taught in the lectures. The participants will discover the economic and ecological potential of ECODESIGN and acquire competence in determining goal-oriented and promising improvements and will be able to apply the knowledge acquired on practical examples. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | Die Vorlesung ist in drei Blöcke unterteilt. Hier sollen die jeweiligen Fragen beantwortet werden: A) Motivation und Einstieg ins Thema: Welche Material- und Energieflüsse werden durch Produkte über alle Lebensphasen, d.h. von der Rohstoffgewinnung, Herstellung, Distribution, Nutzung und Entsorgungen verursacht? Welchen Einfluss hat die Produktentwicklung auf diese Auswirkungen? B) Grundlagen zum ECODESIGN PILOT: Wie können systematisch – über alle Produktlebensphasen hinweg betrachtet – bereits zu Beginn der Produktentwicklung bedeutende Umweltauswirkungen erkannt werden? Wie können zielgerichtet diejenigen Ecodesign-Maßnahmen ermittelt werden, die das größte ökonomische und ökologische Verbesserungspotential beinhalten? C) Anwendung des ECODESIGN PILOT: Welche Produktlebensphasen bewirken den größten Ressourcenverbrauch? Welche Verbesserungsmöglichkeiten bewirken einen möglichst großen ökonomischen und ökologischen Nutzen? Im Rahmen der Vorlesung werden verschiedene Praktische Beispiel bearbeitet. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | Für den Einstieg ins Thema ECODESIGN wurde verschiedene Lehrunterlagen entwickelt, die im Kurs zur Verfügung stehen und teilwesie auch ein "distance learning" ermöglichen: Lehrbuch: Wimmer W., Züst R.: ECODESIGN PILOT, Produkt-Innovations-, Lern- und Optimierungs-Tool für umweltgerechte Produktgestaltung mit deutsch/englischer CD-ROM; Zürich, Verlag Industrielle Organisation, 2001. ISBN 3-85743-707-3 CD: im Lehrbuch inbegriffen (oder Teil "Anwenden" on-line via: www.ecodesign.at) Internet: www.ecodesign.at vermittelt verschiedene weitere Zugänge zum Thema. Zudem werden CD's abgegeben, auf denen weitere Lehrmodule vorhanden sind. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | Hinweise auf Literaturen werden on-line zur Verfügung gestellt. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Testatbedingungen: Abgabe von zwei Übungen | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 151-0530-00L | Nonlinear Dynamics and Chaos II | W | 4 credits | 4G | G. Haller | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | The internal structure of chaos; Hamiltonian dynamical systems; Normally hyperbolic invariant manifolds; Geometric singular perturbation theory; Finite-time dynamical systems | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | The course introduces the student to advanced, comtemporary concepts of nonlinear dynamical systems analysis. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | I. The internal structure of chaos: symbolic dynamics, Bernoulli shift map, sub-shifts of finite type; chaos is numerical iterations. II.Hamiltonian dynamical systems: conservation and recurrence, stability of fixed points, integrable systems, invariant tori, Liouville-Arnold-Jost Theorem, KAM theory. III. Normally hyperbolic invariant manifolds: Crash course on differentiable manifolds, existence, persistence, and smoothness, applications. IV. Geometric singular perturbation theory: slow manifolds and their stability, physical examples. V. Finite-time dynamical system; detecting Invariant manifolds and coherent structures in finite-time flows | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | Handwritten instructor's notes and typed lecture notes will be downloadable from Moodle. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | Books will be recommended in class | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Nonlinear Dynamics I (151-0532-00) or equivalent | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 151-0534-00L | Advanced Dynamics | W | 4 credits | 3V + 1U | P. Tiso | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | Lagrangian dynamics - Principle of virtual work and virtual power - holonomic and non holonomic contraints - 3D rigid body dynamics - equilibrium - linearization - stability - vibrations - frequency response | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | This course provides the students of mechanical engineering with fundamental analytical mechanics for the study of complex mechanical systems .We introduce the powerful techniques of principle of virtual work and virtual power to systematically write the equation of motion of arbitrary systems subjected to holonomic and non-holonomic constraints. The linearisation around equilibrium states is then presented, together with the concept of linearised stability. Linearized models allow the study of small amplitude vibrations for unforced and forced systems. For this, we introduce the concept of vibration modes and frequencies, modal superposition and modal truncation. The case of the vibration of light damped systems is discussed. The kinematics and dynamics of 3D rigid bodies is also extensively treated. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | Lecture notes are produced in class and are downloadable right after each lecture. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | The students will prepare their own notes. A copy of the lecture notes will be available. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Mechanics III or equivalent; Analysis I-II, or equivalent; Linear Algebra I-II, or equivalent. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 151-0566-00L | Recursive Estimation | W | 4 credits | 2V + 1U | R. D'Andrea | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | Estimation of the state of a dynamic system based on a model and observations in a computationally efficient way. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Learn the basic recursive estimation methods and their underlying principles. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | Introduction to state estimation; probability review; Bayes' theorem; Bayesian tracking; extracting estimates from probability distributions; Kalman filter; extended Kalman filter; particle filter; observer-based control and the separation principle. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | Lecture notes available on course website: http://www.idsc.ethz.ch/education/lectures/recursive-estimation.html | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Requirements: Introductory probability theory and matrix-vector algebra. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Competencies |
| |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 151-0630-00L | Nanorobotics | W | 4 credits | 2V + 1U | S. Pané Vidal | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | Nanorobotics is an interdisciplinary field that includes topics from nanotechnology and robotics. The aim of this course is to expose students to the fundamental and essential aspects of this emerging field. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | The aim of this course is to expose students to the fundamental and essential aspects of this emerging field. These topics include basic principles of nanorobotics, building parts for nanorobotic systems, powering and locomotion of nanorobots, manipulation, assembly and sensing using nanorobots, molecular motors, and nanorobotics for nanomedicine. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 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. | W | 4 credits | 9A | C. D. Cadena Lerma, O. Andersson | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Applying Machine Learning methods for solving real-world robotics problems. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | Deep Learning for Perception; (Deep) Reinforcement Learning; Graph-Based Simultaneous Localization and Mapping | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | Slides will be made available to the students. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | Will be announced in the first lecture. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | The students are expected to be familiar with material of the "Recursive Estimation" and the "Introduction to Machine Learning" 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 151-0641-00L | Introduction to Robotics and Mechatronics Number of participants limited to 60. Enrollment is only valid through registration on the MSRL website (www.msrl.ethz.ch). Registrations per e-mail is no longer accepted! | W | 4 credits | 2V + 2U | B. Nelson, Q. Boehler, J. Lussi | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | The aim of this lecture is to expose students to the fundamentals of mechatronic and robotic systems. Over the course of these lectures, topics will include how to interface a computer with the real world, different types of sensors and their use, different types of actuators and their use. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | An ever-increasing number of mechatronic systems are finding their way into our daily lives. Mechatronic systems synergistically combine computer science, electrical engineering, and mechanical engineering. Robotics systems can be viewed as a subset of mechatronics that focuses on sophisticated control of moving devices. The aim of this course is to practically and theoretically expose students to the fundamentals of mechatronic and robotic systems. Over the course of the semester, the lecture topics will include an overview of robotics, an introduction to different types of sensors and their use, the programming of microcontrollers and interfacing these embedded computers with the real world, signal filtering and processing, an introduction to different types of actuators and their use, an overview of computer vision, and forward and inverse kinematics. Throughout the course, students will periodically attend laboratory sessions and implement lessons learned during lectures on real mechatronic systems. By the end of the course, you will be able to independently choose, design and integrate these different building blocks into a working mechatronic system. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | The course consists of weekly lectures and lab sessions. The weekly topics are the following: 0. Course Introduction 1. C Programming 2. Sensors 3. Data Acquisition 4. Signal Processing 5. Digital Filtering 6. Actuators 7. Computer Vision and Kinematics 8. Modeling and Control 9. Review and Outlook The lecture schedule can be found on our course page on the MSRL website (www.msrl.ethz.ch) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | The students are expected to be familiar with C programming. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 151-0660-00L | Model Predictive Control | W | 4 credits | 2V + 1U | M. Zeilinger | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | Model predictive control is a flexible paradigm that defines the control law as an optimization problem, enabling the specification of time-domain objectives, high performance control of complex multivariable systems and the ability to explicitly enforce constraints on system behavior. This course provides an introduction to the theory and practice of MPC and covers advanced topics. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Design and implement Model Predictive Controllers (MPC) for various system classes to provide high performance controllers with desired properties (stability, tracking, robustness,..) for constrained systems. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | - Review of required optimal control theory - Basics on optimization - Receding-horizon control (MPC) for constrained linear systems - Theoretical properties of MPC: Constraint satisfaction and stability - Computation: Explicit and online MPC - Practical issues: Tracking and offset-free control of constrained systems, soft constraints - Robust MPC: Robust constraint satisfaction - Simulation-based project providing practical experience with MPC | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | Script / lecture notes will be provided. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | One semester course on automatic control, Matlab, linear algebra. Courses on signals and systems and system modeling are recommended. Important concepts to start the course: State-space modeling, basic concepts of stability, linear quadratic regulation / unconstrained optimal control. Expected student activities: Participation in lectures, exercises and course project; homework (~2hrs/week). | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 151-0854-00L | Autonomous Mobile Robots | W | 5 credits | 4G | R. Siegwart, L. Ott | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | 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, environment perception, and probabilistic environment modeling, localization, mapping and navigation. Theory will be deepened by exercises with small mobile robots and discussed across application examples. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | 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, environment perception, and probabilistic environment modeling, localization, mapping and navigation. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | 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 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 151-9904-00L | Applied Compositional Thinking for Engineers I | W | 4 credits | 3G | A. Censi, J. Lorand | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | This course is an introduction to Applied Category Theory and related techniques specifically targeted at persons with an applied background. We focus on the benefits of Applied Category Theory for thinking explicitly about abstraction and compositionality. The course will favor a computational/constructive approach, with concrete exercises in the Python language. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | In many domains of engineering and applied sciences it would be beneficial to think explicitly about abstraction and compositionality, to improve both the understanding of the problem and the design of the solution. However, the problem is that the type of math which could be useful to applications is not traditionally taught. Applied Category Theory is a new field of mathematics which could help a lot, but it is quite unreachable by non-mathematicians. Recently, many good options appeared for learning applied category theory; but none satisfy the two properties of 1) being approachable; and 2) highlighting how applied category theory can be used to formalize and solve concrete applied problems. This course will fill this gap. This course's goal is not to teach category theory for the sake of it. Rather, we will teach the "compositionality way of thinking"; category theory will be just the means towards it. This implies that the presentation of materials sometimes diverges from the usual way to teach category theory; and some common concepts might be de-emphasized in favor of more obscure concepts that are more useful for applications. The course will favor a computational/constructive approach: each concept is accompanied by concrete exercises in the programming language Python. Throughout the course, we will discuss many examples related to autonomous robotics, because it is at the intersection of many branches of engineering: we can talk about hardware (sensing, actuation, communication) and software (perception, planning, learning, control) and their composition. ### Intended learning outcomes ### The student is able to recognize algebraic structure for a familiar engineering domain. The student is able to translate such algebraic structure in a concrete implementation using a programming language for the purpose of solving a computational problem. The student can understand when there is a functorial structure between instances of a problem and solutions of the problem, and use such structure to write programs that use these compositionality structures to achieve either more elegance or efficiency (or both). The student is able to recognize structures in concrete scenarios at different levels of abstractions. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | * Review of basic algebraic structures: - Sets and relations, relations - Semigroups, monoids, groups - Homomorphisms - Actions - Graphs * Posets and lattices * (Semi)Categories * Categories of algebraic structures * Categories useful in applications * Categories of processes and procedures * Isomorphisms * Universal properties * Functors * Embeddings * Monotone co-design theory * Monoidal categories, traced monoidal categories | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | Slides and notes will be provided. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | Course book: A. Censi, J. Lorand, G. Zardini, "Applied Compositional Thinking for Engineers" Available online at https://applied-compositional-thinking.engineering/ Note: book includes materials for both ACT4E I and ACT4E II. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Algebra: at the level of a bachelor’s degree in engineering/computer science. Basics of Python programming. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Competencies |
| |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 151-1115-00L | Aircraft Aerodynamics and Flight Mechanics | W | 4 credits | 3G | M. Immer | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | Equations of motion. Aircraft flight performance, flight envelope. Aircraft static stability and control, longitudinal and lateral stability. Dynamic longitudinal and lateral stability. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | - Knowledge of methods to solve flight mechanic problems - To be able to apply basic methods for flight performance calculation and stability investigations | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | Equations of motion. Aircraft flight performance, flight envelope. Aircraft static stability and control, longitudinal and lateral stability. Dynamic longitudinal and lateral stability. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | Mc Cormick, B.W.: Aerodynamics, Aeronautics and Flight Mechanics (John Wiley and Sons), 1979 / 1995 Anderson, J: Fundamentals of Aerodynamics (McGraw-Hill Comp Inc), 2010 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Recommended: Lecture "Introduction to Aircraft and Car Aerodynamics" | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 227-0216-00L | Computational Control Previously (up until FS22) named "Control Systems II" | W | 6 credits | 2V + 2U | S. Bolognani | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | The focus of the course is on the design of advanced controllers for cyber-physical systems, that is, systems in which the controller is an embedded computer that can sense and actuate a physical plant. Advanced computational control strategies like Model Predictive Control, Reinforcement Learning, and Data-Driven control will be covered. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | The objective of the course is to prepare students to the design of advanced digital control systems: this includes comparing alternative control strategies, deciding what class of controllers to employ for a specific problem, tune the controller in order to meet the desired specifications, and produce a conceptual design of how the controller can be implemented and deployed. Simplifying assumptions on the underlying plant that were made in the course Control Systems are relaxed, and advanced computational control concepts and techniques are presented. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | The course will cover both the challenges of a digital control system and the many possibilities offered by powerful computation in control. Different aspects and challenges of embedded control of cyber-physical systems will be discussed. We will then review the limitations of classical control strategies like PID control and LQR control, and motivate the need for controllers that employ significant real-time computation. In particular, we will look into Model Predictive Control, Reinforcement Learning, Data-Driven control, and possibly other advanced computational control techniques. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | Lecture notes will be available on the Moodle page of the course. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | References to the literature will be provided during the course. No textbook is necessary, but students are encouraged to read the suggested readings. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Prerequisites: Control Systems or equivalent. A background in optimization is very helpful. Students that don’t have it will be provided with some additional reading material. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Competencies |
| |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 252-0220-00L | Introduction to Machine Learning Preference is given to students in programmes in which the course is being offered. All other students will be waitlisted. Please do not contact Prof. Krause for any questions in this regard. If necessary, please contact studiensekretariat@inf.ethz.ch | W | 8 credits | 4V + 2U + 1A | A. Krause, F. Yang | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | The course introduces the foundations of learning and making predictions based on data. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | The course will introduce the foundations of learning and making predictions from data. We will study basic concepts such as trading goodness of fit and model complexitiy. We will discuss important machine learning algorithms used in practice, and provide hands-on experience in a course project. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | - Linear regression (overfitting, cross-validation/bootstrap, model selection, regularization, [stochastic] gradient descent) - Linear classification: Logistic regression (feature selection, sparsity, multi-class) - Kernels and the kernel trick (Properties of kernels; applications to linear and logistic regression); k-nearest neighbor - Neural networks (backpropagation, regularization, convolutional neural networks) - Unsupervised learning (k-means, PCA, neural network autoencoders) - The statistical perspective (regularization as prior; loss as likelihood; learning as MAP inference) - Statistical decision theory (decision making based on statistical models and utility functions) - Discriminative vs. generative modeling (benefits and challenges in modeling joint vy. conditional distributions) - Bayes' classifiers (Naive Bayes, Gaussian Bayes; MLE) - Bayesian approaches to unsupervised learning (Gaussian mixtures, EM) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Designed to provide a basis for following courses: - Advanced Machine Learning - Deep Learning - Probabilistic Artificial Intelligence - Seminar "Advanced Topics in Machine Learning" | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Competencies |
| |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 252-0526-00L | Statistical Learning Theory | W | 8 credits | 3V + 2U + 2A | J. M. Buhmann | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | The course covers advanced methods of statistical learning: - Variational methods and optimization. - Deterministic annealing. - Clustering for diverse types of data. - Model validation by information theory. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | The course surveys recent methods of statistical learning. The fundamentals of machine learning, as presented in the courses "Introduction to Machine Learning" and "Advanced Machine Learning", are expanded from the perspective of statistical learning. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | - Variational methods and optimization. We consider optimization approaches for problems where the optimizer is a probability distribution. We will discuss concepts like maximum entropy, information bottleneck, and deterministic annealing. - Clustering. This is the problem of sorting data into groups without using training samples. We discuss alternative notions of "similarity" between data points and adequate optimization procedures. - Model selection and validation. This refers to the question of how complex the chosen model should be. In particular, we present an information theoretic approach for model validation. - Statistical physics models. We discuss approaches for approximately optimizing large systems, which originate in statistical physics (free energy minimization applied to spin glasses and other models). We also study sampling methods based on these models. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | A draft of a script will be provided. Lecture slides will be made available. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | Hastie, Tibshirani, Friedman: The Elements of Statistical Learning, Springer, 2001. L. Devroye, L. Gyorfi, and G. Lugosi: A probabilistic theory of pattern recognition. Springer, New York, 1996 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Knowledge of machine learning (introduction to machine learning and/or advanced machine learning) Basic knowledge of statistics. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 252-0579-00L | 3D Vision | W | 5 credits | 3G + 1A | M. Pollefeys, D. B. Baráth | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | The course covers camera models and calibration, feature tracking and matching, camera motion estimation via simultaneous localization and mapping (SLAM) and visual odometry (VO), epipolar and mult-view geometry, structure-from-motion, (multi-view) stereo, augmented reality, and image-based (re-)localization. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | After attending this course, students will: 1. understand the core concepts for recovering 3D shape of objects and scenes from images and video. 2. be able to implement basic systems for vision-based robotics and simple virtual/augmented reality applications. 3. have a good overview over the current state-of-the art in 3D vision. 4. be able to critically analyze and asses current research in this area. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | The goal of this course is to teach the core techniques required for robotic and augmented reality applications: How to determine the motion of a camera and how to estimate the absolute position and orientation of a camera in the real world. This course will introduce the basic concepts of 3D Vision in the form of short lectures, followed by student presentations discussing the current state-of-the-art. The main focus of this course are student projects on 3D Vision topics, with an emphasis on robotic vision and virtual and augmented reality applications. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 263-5806-00L | Digital Humans Previously Computational Models of Motion and Virtual Humans | W | 8 credits | 3V + 2U + 2A | S. Coros, S. Tang | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | This course covers the core technologies required to model and simulate motions for digital humans and robotic characters. Topics include kinematic modeling, physics-based simulation, trajectory optimization, reinforcement learning, feedback control for motor skills, motion capture, data-driven motion synthesis, and ML-based generative models. They will be richly illustrated with examples. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Students will learn how to estimate human pose, shape, and motion from videos and create basic human avatars from various visual inputs. Students will also learn how to represent and algorithmically generate motions for digital characters and their real-life robotic counterparts. The lectures are accompanied by four programming assignments (written in python or C++) and a capstone project. The deadline to cancel/deregister from the course is May 1st. Deregistration after the deadline will lead to fail. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | - Basic concept of 3D representations - Human body/hand models - Human motion capture; - Non-rigid surface tracking and reconstruction - Neural rendering - Optimal control and trajectory optimization - Physics-based modeling for multibody systems - Forward and inverse kinematics - Rigging and keyframing - Reinforcement learning for locomotion | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Experience with python and C++ programming, numerical linear algebra, multivariate calculus and probability theory. Some background in deep learning, computer vision, physics-based modeling, kinematics, and dynamics is preferred. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Competencies |
| |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 376-1217-00L | Rehabilitation Engineering I: Motor Functions | W | 4 credits | 2V + 1U | R. Riener, C. E. Awai | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | “Rehabilitation” is the (re)integration of an individual with a disability into society. Rehabilitation engineering is “the application of science and technology to ameliorate the handicaps of individuals with disability”. Such handicaps can be classified into motor, sensor, and cognitive disabilities. In general, one can distinguish orthotic and prosthetic methods to overcome these disabilities. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | The goal of this course is to present classical and new technical principles as well as specific examples applied to compensate or enhance motor deficits. In the 1 h exercise the students will learn how to solve representative problems with computational methods applied to exoprosthetics, wheelchair dynamics, rehabilitation robotics and neuroprosthetics. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | Modern methods rely more and more on the application of multi-modal and interactive techniques. Multi-modal means that visual, acoustical, tactile, and kinaesthetic sensor channels are exploited to display information to the patient. Interaction means that the exchange of information and energy occurs bi-directionally between the rehabilitation device and the human being. Thus, the device cooperates with the patient rather than imposing an inflexible strategy (e.g., movement) upon the patient. These principles are recurrent in modern technological tools to support rehabilitation, including prosthesis, orthoses, powered exoskeletons, powered wheelchairs, therapy robots and virtual reality systems. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | Books: Burdet, Etienne, David W. Franklin, and Theodore E. Milner. Human robotics: neuromechanics and motor control. MIT press, 2013. Krakauer, John W., and S. Thomas Carmichael. Broken movement: the neurobiology of motor recovery after stroke. MIT Press, 2017. Teodorescu, Horia-Nicolai L., and Lakhmi C. Jain, eds. Intelligent systems and technologies in rehabilitation engineering. CRC press, 2000. Winters, Jack M., and Patrick E. Crago, eds. Biomechanics and neural control of posture and movement. Springer Science & Business Media, 2012. Selected Journal Articles: Abbas, James J., and Robert Riener. "Using mathematical models and advanced control systems techniques to enhance neuroprosthesis function." Neuromodulation: Technology at the Neural Interface 4.4 (2001): 187-195. Basalp, Ekin, Peter Wolf, and Laura Marchal-Crespo. "Haptic training: which types facilitate (re) learning of which motor task and for whom Answers by a review." IEEE Transactions on Haptics (2021). Calabrò, Rocco Salvatore, et al. "Robotic gait rehabilitation and substitution devices in neurological disorders: where are we now?." Neurological Sciences 37.4 (2016): 503-514. Cooper, R. (1993) Stability of a wheelchair controlled by a human. IEEE Transactions on Rehabilitation Engineering 1, pp. 193-206. Gassert, Roger, and Volker Dietz. "Rehabilitation robots for the treatment of sensorimotor deficits: a neurophysiological perspective." Journal of neuroengineering and rehabilitation 15.1 (2018): 1-15. Laver, Kate E., et al. "Virtual reality for stroke rehabilitation." Cochrane database of systematic reviews 11 (2017). Marquez-Chin, Cesar, and Milos R. Popovic. "Functional electrical stimulation therapy for restoration of motor function after spinal cord injury and stroke: a review." Biomedical engineering online 19 (2020): 1-25. Miller, Larry E., Angela K. Zimmermann, and William G. Herbert. "Clinical effectiveness and safety of powered exoskeleton-assisted walking in patients with spinal cord injury: systematic review with meta-analysis." Medical devices (Auckland, NZ) 9 (2016): 455. Raspopovic, Stanisa. "Advancing limb neural prostheses." Science 370.6514 (2020): 290-291. Riener, R. (2013) Rehabilitation Robotics. Foundations and Trends in Robotics, Vol. 3, nos. 1-2, pp. 1-137. Riener, R., Lünenburger, L., Maier, I. C., Colombo, G., & Dietz, V. (2010). Locomotor training in subjects with sensori-motor deficits: An overview of the robotic gait orthosis Lokomat. Journal of Healthcare Engineering, 1(2), 197-216. Riener, R., Nef, T., Colombo, G. (2005) Robot-aided neurorehabilitation for the upper extremities. Medical & Biological Engineering & Computing 43(1), pp. 2-10. Sigrist, Roland, et al. "Augmented visual, auditory, haptic, and multimodal feedback in motor learning: a review." Psychonomic bulletin & review 20.1 (2013): 21-53. Xiloyannis, Michele, et al. "Soft Robotic Suits: State of the Art, Core Technologies, and Open Challenges." IEEE Transactions on Robotics (2021). | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Target Group: Students of higher semesters and PhD students of - D-MAVT, D-ITET, D-INFK - Biomedical Engineering - Medical Faculty, University of Zurich Students of other departments, faculties, courses are also welcome | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 376-1308-00L | Development Strategies for Medical Implants | W | 3 credits | 2V + 1U | J. Mayer-Spetzler, N. Mathavan | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | Introduction to development strategies for implantable devices considering the interdependencies of biocompatibility, clinical, regulatory, and economical requirements; discussion of state of the art and actual trends in orthopedics, sports medicine, cardiovascular surgery, and regenerative medicine (tissue engineering). | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Primary considerations in implant development. Concept of structural and surface biocompatibility and its relevance for implant design and surgical technique. Understanding conflicting factors, e.g., clinical need, economics, and regulatory requirements. Tissue engineering concepts, their strengths, and weaknesses as current and future clinical solutions. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | Understanding of clinical and economic needs as guidelines for the development of medical implants; implant and implantation-related tissue reactions, biocompatible materials, and material processing technologies; implant testing and regulatory procedures; discussion of state-of-the-art and actual trends in implant development in sports medicine, spinal and cardio-vascular surgery; introduction to tissue engineering. Commented movies from surgeries will further illustrate selected topics. Seminar: Group seminars on selected controversial topics in implant development. Participation is mandatory. Planned excursions (limited availability, not mandatory, to be confirmed): Participation (as a visitor) in a life surgery (travel at own expense) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | Script (electronically available): - presented slides - selected scientific papers for further reading | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | Reference to key papers will be provided during the lectures. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Only Master's students; achieved Bachelor's degree is a pre-condition Admission to the lecture is based on a letter of motivation to the lecturer J. Mayer. The number of participants in the course is limited to 30 students in total. Students will be exposed to surgical movies which may cause emotional reactions. The viewing of the surgical movies is voluntary and is the student's responsibility. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Competencies |
| |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 227-0690-12L | Advanced Topics in Control | W | 4 credits | 2V + 2U | F. Dörfler, M. Hudoba de Badyn | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | Advanced Topics in Control (ATIC) covers advanced research topics in control theory. It is offered each Spring semester with the topic rotating from year to year. Repetition for credit is possible, with consent of the instructor. During the spring of 2020, the course will cover a range of topics in distributed systems control. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | By the end of this course you will have developed a sound and versatile toolkit to tackle a range of problems in network systems and distributed systems control. In particular, we will develop the methodological foundations of algebraic graph theory, consensus algorithms, and multi-agent systems. Building on top of these foundations we cover a range of problems in epidemic spreading over networks, swarm robotics, sensor networks, opinion dynamics, distributed optimization, and electrical network theory. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | Distributed control systems include large-scale physical systems, engineered multi-agent systems, as well as their interconnection in cyber-physical systems. Representative examples are electric power grids, swarm robotics, sensor networks, and epidemic spreading over networks. The challenges associated with these systems arise due to their coupled, distributed, and large-scale nature, and due to limited sensing, communication, computing, and control capabilities. This course covers algebraic graph theory, consensus algorithms, stability of network systems, distributed optimization, and applications in various domains. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | A complete set of lecture notes and slides will be provided. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | The course will be largely based on the following set of lecture notes co-authored by one of the instructors: http://motion.me.ucsb.edu/book-lns/ | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Sufficient mathematical maturity, in particular in linear algebra and dynamical systems. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Page
1
of
1
