Schwerpunkt: AI
https://lvb.informatik.uni-bremen.de/ibap/03-ibap-ml.pdf
Die Übungen starten in der 2. Semesterwoche.

https://lvb.informatik.uni-bremen.de/ibap/03-ibap-mrca.pdf
Robotics is a complex field that emerged at the intersection of multiple disciplines such as physics, mathematics and computer science. New advances in hardware and software design and progress in artificial intelligence enable robotics research to pursue higher goals and achieve increased autonomy in various environments. For instance, robots can operate in disaster zones for search and rescue operations, can be employed in rehabilitation and healthcare, space and underwater exploration, etc. Given the complexity of such scenarios, it is essential to develop robust robotic systems with a high degree of autonomy, able to assist humans in difficult and tedious tasks.

This course aims to provide the fundamentals of modern robot control approaches that enable robotic agents to operate in the environment autonomously. The course introduces a basic understanding of autonomous robots, along with tools and methods to control various types of mobile robotic platforms and manipulators. Firstly, the course presents the types of sensors and actuators employed in autonomous robotic platforms. Secondly, it offers a formal understanding of the robot geometry, its kinematic and dynamic models. Finally, the course provides methods and approaches to control the robotic system from a deliberative and reactive point of view. Students will put this knowledge into practice during tutorials and exercise sheets using Python implementation and robot simulations.

Contents

• Introduction to Robotics and AI: long term robot autonomy, artificial intelligence, deliberative vs. reactive control, robotic applications.
• Sensing and Actuation Modalities: types of sensors and actuators, sensor fusion, actuator control.
• Robot Geometry and Transformations: robot transformations in the 3D space, exponential and logarithmic maps, forward and inverse geometric models.
• Kinematics: definition of twists and wrenches for rigid bodies, geometric Jacobian formulation, forward and inverse kinematics.
• Dynamics: an introduction to Lagrangian and Newtonian mechanics, robot dynamics formulation, recursive Newton-Euler algorithm.
• Localization: direct and probabilistic methods for robot localization, odometry, global localization, particle filter.
• Path Planning: path vs. trajectory generation, graph-based methods for path planning (e.g. Djikstra, A\*).
• Kinodynamic Planning: transcribing a dynamic planning problem into trajectory optimization, direct and indirect methods, costs and constraints.
• Reinforcement Learning-based Control: mathematical foundations, discrete vs continuous methods, reinforcement learning for closed-loop robot control.
• Dynamic Control: PD gravity compensation control, computed torque control, admittance vs impedance control.
• Optimal Control: energy-shaping control, LQR and time-varying LQR control.

Learning Outcomes

At the end of the course, the student is expected to be able to:

• Define robot autonomy and list its key aspects.
• Describe the sensor and actuator modalities used in robotics, and explain their relevance for robot control.
• Implement and understand the low-level actuator control methods.
• Compute the 3D world coordinate transformations for rigid bodies.
• Apply the robot forward and inverse geometric model.
• Describe a robotic system based on its kinematic and dynamic properties.
• Use probabilistic methods for robot localization.
• Generate an optimal path for a mobile robot or manipulator using graph search methods.
• Plan a path taking into account the robot kinodynamic properties.
• Use reinforcement learning methods to control simple robotic systems.
• Apply dynamical and optimal control methods on robotic systems such that they are robust against disturbances.
• Assess the strengths and limitations of different control methods presented in the course.
• Identify open challenges in robotics research and current trends in state-of-the-art.
• Communicate confidently using the terminology in the field of robotics.
• Cooperate and work in teams in order to solve tasks.

Examination

During the semester, students are required to complete 6 worksheets in groups of 4.
To pass the course, students must achieve a minimum of 50% on both the worksheets and the written exam.
The final grade is 40% based on worksheets and 60% on the written exam.

References

• Mechanics of Robotic Manipulation, Mathew T. Masen, MIT press, 2001.
• Algebra and Geometry, Alan F. Beardon, Cambridge University Press, 2005.
• Modelling and Control of Robot Manipulators, Lorenzo Sciavicco, Bruno Siciliano, Springer, 2000.
• Probabilistic Robotics (Intelligent Robotics and Autonomous Agents), Sebastian Thrun, Wolfram Burgard, and Dieter Fox, MIT Press, 2005.
• Introduction to Autonomous Mobile Robots, Siegwart R., Nourbakhsh I., Scaramuzza D., MIT press, 2011.
• Automated Planning: Theory and Practice, Malik Ghallab, Dana Nau, Paolo Traverso, Elsevier, 2004.
• Behaviour-based robotics, R. C. Arkin, MIT press, 1998.
• Modern Robotics: Mechanics, Planning, and Control, Kevin M. Lynch and Frank C. Park, Cambridge University Press, 2017.

3 SWS Seminar
Raum wird nach Anmeldung in Stud.IP bekannt gegeben.
Homepage des KKSB und Uni-Lageplan

Die Veranstaltung findet im TAB in Raum 0.30 statt.
https://lvb.informatik.uni-bremen.de/ibv/03-ibvp-akr.pdf

This course deals with the idea of bringing knowledge into applications to support users in daily life. It therefore covers topics on how knowledge can be represented to be machine-understandable, how knowledge can be acquired from different sources (including Web scraping) and how such different knowledge chunks can be linked. It will further discuss how to reason about knowledge and how different agents like websites, AR applications or robots can use knowledge to support users in their daily life. All exercises will be available in platform-independent jupyter notebooks based on python and have low software requirements.

Eine Doppelanerkennung von 'Data Science' und 'Data Science and Visualization' ist nicht möglich.
https://lvb.informatik.uni-bremen.de/ibv/03-ibva-dsv.pdf

The class will provide an introduction to data science and information visualization. For this, the programming language Python will be used (and taught). For creating data visualizations, you will be able to choose among a series of tools (e.g., Plotly, Observable, etc.)

We will explore different concepts from the fields of human-computer interaction, data visualization, and computer-supported collaborative work. You will learn about:
      • Basic statistics
      • Visualization techniques
      • Interaction design
Exploratory data analysis and evaluation, as an integral part of data science, will also be taught. The course will involve applying the learned techniques to real-world datasets to develop a custom project.

This class is taught in person and in English. Use material like the coursebook to learn more about the topics as we progress in the course.

https://lvb.informatik.uni-bremen.de/igs/03-ibfw-hto.pdf
A large number of problems arising in practical scenarios like communication, transportation, planning, logistics etc. can be formulated as discrete linear optimization problems. This course briefly introduces the theory of such problems. We develop a toolkit to model real-world problems as (discrete) linear programs. We also explore several ways to find integer solutions such as cutting planes, branch & bound, and column generation.

Throughout the course, we learn these skills by modeling and solving, for example, scheduling, packing, matching, routing, and network-design problems. We focus on translating practical examples into mixed-integer linear programs. We learn how to use solvers (such as CPLEX and Gurobi) and tailor the solution process to certain properties of the problem.

This course consists of two phases:

• One week Mon-Fri (full day) of lectures and practical labs: October 9-13, 2023, in MZH.
• A subsequent project period: One problem has to be modeled, implemented, and solved individually or in a group of at most three students. The topic will be provided by the lecturers and will be discussed on the last day of the block course. The project including the implementation has to be presented in the beginning of the winter semester.

There are no prerequisites except some basic programming skills to participate.

Please confirm your participation by email to Felix fhommels@uni-bremen.de by September 15.

Profil: SQ, KIKR.
Schwerpunkt: IMA-SQ, IMVT-AI, IMVT-VMC
weitere Studiengänge: M-M-Alg-Num, M-T
https://lvb.informatik.uni-bremen.de/imat/03-imat-apx.pdf

A large number of problems arising in practical scenarios like communication, transportation, planning, logistics etc. can be modeled as combinatorial optimization problems. In most cases, these problems are computationally intractable and one often resorts to heuristics that provide sufficiently good solutions in reasonable amount of runtime. However, in most cases, such heuristics do not provide a worst case guarantee on the performance in comparison to the optimum solution. In this course, we shall study algorithms for combinatorial optimization problems which can provide strong mathematical guarantees on performance. The course aims at developing a toolkit for solving such problems. The lectures will consist of designing polynomial-time algorithms and proving rigorous bounds on their worst case performances.
We review many classical results in the field of approximation algorithms, highlighting different techniques commonly used for the design of such algorithms. Among others, we will treat the following topics:
• Greedy algorithms and Local Search
• Rounding Data and Dynamic Programming
• Deterministic Rounding of Linear Programs (LPs)
• Random Sampling and Randomized Rounding of LPs
• Primal-Dual Methods
• Hardness of Approximation
• Problem Solving under Uncertainty

Profil: KIKR
Schwerpunkt: IMAP-AI, IMA-VMC
https://lvb.informatik.uni-bremen.de/imap/03-imap-aml.pdf

Profil: SQ.
Schwerpunkt: IMVP-DMI, IMVP-SQ
https://lvb.informatik.uni-bremen.de/imap/03-imap-dis.pdf
Die Veranstaltung ist inhaltlich identisch mit "Entwurf von Informationssystemen" (keine Doppelanerkennung möglich).

Profil: KIKR.
Schwerpunkt: IMA-AI, IMVP-VMC
https://lvb.informatik.uni-bremen.de/imap/03-imap-rl.pdf

Profil: DMI
Schwerpunkt: IMA-DMI, IMA-VMC
https://lvb.informatik.uni-bremen.de/imaa/03-imaa-cthci.pdf

Profil: DMI
Schwerpunkt: IMK-DMI, IMA-VMC
https://lvb.informatik.uni-bremen.de/imaa/03-imaa-ec.pdf

Profil: DMI
Schwerpunkt: IMA-DMI, IMVA-DMI
https://lvb.informatik.uni-bremen.de/imva/03-imva-mad.pdf
Die Veranstaltung richtet sich an Studenten der Informatik und Digitalen Medien. In Gruppenarbeit sollen die Studierenden semesterbegleitend ein App-Projekt umsetzen. In der Vorlesung werden alle relevanten Informationen der modernen Softwareentwicklung, mit Fokus auf die mobile App-Entwickung, vermittelt. Dazu gehören Themen wie mobiles Testing, Scrum, UX Design, Evaluation & Nutzertests, Design Patterns und Cross-Plattform-Entwicklung. Das Ziel dabei ist die Vermittlung von praxisrelevantem Wissen aus dem Alltag eines erfolgreichen Unternehmens.

The lecture addresses Interactive Visualization of Huge Scientific Datasets. More information can also be found on the Homepage: https://www.uni-bremen.de/ag-high-performance-visualization

Die Vorlesung beschäftigt sich mit den mathematischen Grundlagen der wissenschaftlichen Visualisierung und behandelt Methoden für das parallele Post-Processing großer wissenschaftlicher Datensätze. Anwendungsbeispiele werden anhand der Open-Source-Software ParaView erläutert.
Homepage zur Veranstaltung: https://www.uni-bremen.de/ag-high-performance-visualization

Schwerpunkt: IMVP-AI, IMVP-DMI
https://lvb.informatik.uni-bremen.de/imvp/03-imvp-ecl.pdf

Schwerpunkt: IMVP-SQ
https://lvb.informatik.uni-bremen.de/imvp/03-imvp-lspd.pdf

https://lvb.informatik.uni-bremen.de/imvp/03-imvp-mpar.pdf
Profil: SQ, KIKR, DMI.
Schwerpunkt: SQ, AI, DMI, VMC
Some prior expertise in C will be helpful. The lecture will be held in German or English, depending on demand.
https://cgvr.cs.uni-bremen.de/teaching/

https://lvb.informatik.uni-bremen.de/imvp/03-imvp-tcrs.pdf
Schwerpunkt: IMVP-AI

Schwerpunkt: IMVA-AI, IMVA-DMI
https://lvb.informatik.uni-bremen.de/imva/03-imva-acss.pdf
The aim of this course is to create an understanding of the major aspects of sports applications. The course is split into two parts: the first half has a classic lecture/tutorial style, whereas the second half will focus on the creation of individual sports applications.

The lectures will explain the necessary fundamentals, such as sensor technology, user feedback, and the conduction of empirical studies, along with a number of inspiring examples.

In the project part, own prototypes for sports applications are developed in small groups. The exact application as well as the technical implementation approach can be chosen freely. The final graded outcome of the course will be a small sports application about which a presentation has to be held and a documentation in a scientific paper style has to be written.

The course will be held in English.

Schwerpunkt: AI, DMI

Profil: DMI.
Schwerpunkt: IMVA-DMI, IMVA-VMC
https://lvb.informatik.uni-bremen.de/imva/03-imva-ei.pdf

4 SWS, Schwerpunkt: IMVA-DMI
https://lvb.informatik.uni-bremen.de/imva/03-imva-gac.pdf

In this seminar, we want to investigate current tools and models (e.g., Dall-E2, ChatGPT) for creating content. We will discuss the idea and design and the implications for artists, developers, and researchers. Therefore, we will read current research papers and develop applications that use these models. We want to design and create new interfaces and ideas to support users with these tools. The course is open to Digital Media and Computer Science students.

https://lvb.informatik.uni-bremen.de/imva/03-imva-tmp.pdf
Schwerpunkt: IMVA-DMI
https://bernardrobben.net/wp-content/uploads/2023/03/TheMachinicProject.pdf

https://lvb.informatik.uni-bremen.de/ims/03-ims-ais.pdf
Profil: KIKR

Profil: KIKR, DMI.
https://lvb.informatik.uni-bremen.de/ims/03-ims-apmsk.pdf
Die Veranstaltung findet in Englischer Sprache statt.

Genaue Block-Zeiten werden noch bekanntgegeben.
Der Einführungstermin findet am 03.04.2024 in Raum 4380 statt.

https://lvb.informatik.uni-bremen.de/ims/03-ims-cnt.pdf

Auftakttermin am 03.04.2024 von 10-12h in Raum 0.01.
Weitere Termine werden dann abgesprochen.

60 Lehrveranstaltungen in Deutsch und Englisch für Bachelor- und Masterstudentinnen aller Fächer. Als General Studies sowie teilweise als Fachstudium im Sommersemester 2024 sowie im Wintersemester 2024/25 anerkannt. Alle Einzelangaben, Zeiten und Anmeldungen jederzeit nur über die Website https://www.informatica-feminale.de.
60 courses in German and English for women Bachelor and Master students from all fields of study. Courses are part of General Studies, some are accepted in Informatics; in the summer semester 2024 as well as in winter semester 2024/25. Further information, schedules and registration only on the website https://www.informatica-feminale.de.

Veranstaltung für Doktoranten, jeden 1. Montag im Monat von 14-16h in Raum 5300.

60 Lehrveranstaltungen in Deutsch und Englisch für Bachelor- und Masterstudentinnen aller Fächer. Als General Studies sowie teilweise als Fachstudium im Sommersemester 2024 sowie im Wintersemester 2024/25 anerkannt. Alle Einzelangaben, Zeiten und Anmeldungen jederzeit nur über die Website https://www.informatica-feminale.de.
60 courses in German and English for women Bachelor and Master students from all fields of study. Courses are part of General Studies, some are accepted in Informatics; in the summer semester 2024 as well as in winter semester 2024/25. Further information, schedules and registration only on the website https://www.informatica-feminale.de.