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Perception and Learning for Robotics
Last Updated: 2026-02-05 16:37:53
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.
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
Resources
Lecture Notes
Slides will be made available to the students.
Literature
Will be announced in the first lecture.
General Information
- Language
- English
- Levels
- MSC
- Frequency
- Yearly recurring
Examination
- Type
- graded semester performance
Registration & Places
- Signup End
- 11.02.2024
Course Components
| Type | Title | Time & Place | Hours |
|---|---|---|---|
| lecture |
Perception and Learning for Robotics
• Monday 26.02.2024 from 14:15 to 18:00, HG F 26.3
• Wednesday 28.02.2024 from 14:15 to 18:00, the venue (tbd)
• Friday 01.03.2024 from 14:15 to 18:00, the venue (tbd)
|
|
12 h semesterly |
| independent project | Perception and Learning for Robotics | No time listed | 90 h semesterly |
Offered In
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Robotics, Systems and Control (The courses listed in this category “Core Courses” are recommended. Alternative courses can be chosen in agreement with the tutor. .)
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