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Information Processing for Robotics
Last Updated: 2026-02-05 15:23:48
Abstract
This lecture will present most recent approaches to artificial intelligence and its applications to robotics.
Objective
The goal of this lecture is to present basic information processing tools and its applications to robotics and intelligent systems. This includes the most common approaches in artificial intelligence and applications like mobile robot motion control and localization or applied computer vision.
Content
I. Information Processing Tools (4 weeks) a) Principal Component Analysis (PCA) / Independent Component Analysis (ICA) b) Support Vector Machines (SVM) / AdaBoost c) Artificial Neural Network (ANN) d) Reinforcement Learning (RL) e) Probabilistic Reasoning f) Genetic Algorithms II. Application to Localisation and Mapping (3 weeks) a) Probabilistic Localisation b) Probabilistic Mapping and SLAM c) SVM for 3D object identifications III. Application to Vision (2 week) a) PCA for image identification b) AdaBoost for image identification IV. Application to Robotic Control (2 week) a) ANN for adaptative control b) RL for adaptative control V. Application to Motion Planning (3 weeks) a) Motion optimisation using Genetic Algorithms b) Learning behaviour using Probabilistic Reasonning c) Motion Planning using MDP/POMDP d) Motion Planning in Graphs (A*,D*,...)
Resources
Lecture Notes
Handouts of the slides; scientific papers; reference books;
Literature
A list of relevant literature will be presented in the lecture.
General Information
- Language
- English
- Levels
- BSC , MSC
- Frequency
- Yearly recurring
Examination
- Type
- session examination
- Mode
- oral 30 minutes
Course Components
| Type | Title | Time & Place | Hours |
|---|---|---|---|
| lecture with exercise | Information Processing for Robotics |
|
3 h weekly |