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Perception and Learning for Robotics
Last Updated: 2026-06-01 11:33:47
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
- 09.02.2025
Course Components
| Type | Title | Time & Place | Hours |
|---|---|---|---|
| lecture |
Perception and Learning for Robotics
• Monday 24.02.2025 from 14:15 to 18:00
• Wednesday 26.02.2025 from 14:15 to 18:00
• Friday 28.02.2025 from 14:15 to 18:00
|
|
12 h semesterly |
| independent project | Perception and Learning for Robotics | No time listed | 90 h semesterly |
Offered In
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Fachspezifische Vertiefung (Es müssen mindestens 20 KP aus den Deep Track Lerneinheiten absolviert werden. Überzählige KP können für Wahlfächer angerechnet werden.)
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Vertiefungsfächer Robotics (Diese LE's können sowohl als Vertiefungsfach als auch als Wahlfach angerechnet werden.)
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