151-0634-00L  Perception and Learning for Robotics

SemesterFrühjahrssemester 2021
DozierendeC. D. Cadena Lerma, J. J. Chung
Periodizitäteinmalige Veranstaltung
KommentarNumber 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.


151-0634-00 APerception and Learning for Robotics
The lectures take place during the first two weeks of the Semester. The venue will be announced later.
120s Std.
22.02.14:00-16:00ON LI NE »
24.02.14:00-16:00ON LI NE »
26.02.14:00-16:00ON LI NE »
01.03.14:00-16:00ON LI NE »
03.03.14:00-16:00ON LI NE »
05.03.14:00-16:00ON LI NE »
C. D. Cadena Lerma, J. J. Chung


KurzbeschreibungThis 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.
LernzielApplying Machine Learning methods for solving real-world robotics problems.
InhaltDeep Learning for Perception; (Deep) Reinforcement Learning; Graph-Based Simultaneous Localization and Mapping
SkriptSlides will be made available to the students.
LiteraturWill be announced in the first lecture.
Voraussetzungen / BesonderesThe 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.


Information zur Leistungskontrolle (gültig bis die Lerneinheit neu gelesen wird)
Leistungskontrolle als Semesterkurs
ECTS Kreditpunkte4 KP
PrüfendeC. D. Cadena Lerma, J. J. Chung
Formbenotete Semesterleistung
RepetitionRepetition nur nach erneuter Belegung der Lerneinheit möglich.
Zusatzinformation zum PrüfungsmodusThe grade is based on the realization of a project, presentation and demo (50%), the project report (40%) and quizzes/exercises during the lecture block (10%).


Keine öffentlichen Lernmaterialien verfügbar.
Es werden nur die öffentlichen Lernmaterialien aufgeführt.


Keine Informationen zu Gruppen vorhanden.


PlätzePlätze beschränkt. Spezielles Auswahlverfahren.
WartelisteBis 22.02.2021
BelegungsendeBelegung nur bis 14.02.2021 möglich

Angeboten in

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