151-0634-00L  Perception and Learning for Robotics

SemesterSpring Semester 2018
LecturersC. D. Cadena Lerma, I. Gilitschenski, R. Siegwart
Periodicitynon-recurring course
Language of instructionEnglish
CommentNumber 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 on the following days in the 2nd week of the Semester:

- Monday 26.02.2018 at 14-18
- Wednesday 28.02.2018 at 14-18
- Friday 02.03.2018 at 14-18

The venue will be announced later.
12s hrs
26.02.14-18HG D 3.1 »
28.02.14-18HG F 26.1 »
02.03.14-18HG E 23 »
C. D. Cadena Lerma, I. Gilitschenski, R. Siegwart

Catalogue data

AbstractThis 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.
ObjectiveApplying Machine Learning methods for solving real-world robotics problems.
ContentDeep Learning for Perception; (Deep) Reinforcement Learning; Graph-Based Simultaneous Localization and Mapping
Lecture notesSlides will be made available to the students.
LiteratureWill be announced in the first lecture.
Prerequisites / NoticeThe 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.

Performance assessment

Performance assessment information (valid until the course unit is held again)
Performance assessment as a semester course
ECTS credits4 credits
ExaminersC. D. Cadena Lerma, I. Gilitschenski, R. Siegwart
Typegraded semester performance
Language of examinationEnglish
RepetitionRepetition only possible after re-enrolling for the course unit.
Additional information on mode of examinationThe grade is based on the realization of a project, presentation and demo (50%), the project report (40%) and quizzes during the lecture block (10%).

Learning materials

No public learning materials available.
Only public learning materials are listed.


No information on groups available.


There are no additional restrictions for the registration.

Offered in

Mechanical Engineering MasterRobotics, Systems and ControlWInformation
Robotics, Systems and Control MasterCore CoursesWInformation