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CZECH TECHNICAL UNIVERSITY IN PRAGUE
STUDY PLANS
2021/2022

Robot Vision

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Code Completion Credits Range Language
B3B33VIR Z,ZK 4 2P+2L Czech
Corequisite:
The course cannot be taken simultaneously with:
Robot Learning (B3B33UROB)
Lecturer:
Karel Zimmermann (guarantor), Patrik Vacek
Tutor:
Karel Zimmermann (guarantor), Teymur Azayev, Martin Pecka, Vojtěch Šalanský, Patrik Vacek
Supervisor:
Department of Cybernetics
Synopsis:

Course learn to apply the machine learning methods and optimization algorithms on known robotics problems such as metrical or semantic segmentation from RGB-D data or reactive motion control. The focus of the subject lies in teaching deep learning methods. Students employ the elementary knowledge of optimization and linear algebra such as robust solutions of overdetermined systems of nonlinear equations or gradient minimization methods. First 7 labs are devoted to solving elementary problems in PyTorch, second half is devoted to the individual solution of the semester work.

Requirements:
Syllabus of lectures:

1 Overview and lecture outline

2 Regression ML/MAP

3 Classification ML/MAP

4 Neural networks, backpropagation

5 Convolution layer + backpropagation

6 Normalization layers (BachNorm, InstanceNorm, ...) + backpropagation

7 Training (SGD, momentum, ...)

8 Architectures of deep neural networks I: detection (yolo), segmentation (DeepLab), classification (ResNet)

9 Architectures of deep neural networks II: pose regression, LIFT

10 Introduction to PyTorch

11 Generative Adversarial Networks, Cascaded Refinement Networks, Style Transfer Networks

12 Reinforcement Learning in Robotics (Imitation Learning, RL, Actor-Critic, applications)

13 Presentation of semestral work

Syllabus of tutorials:

During labs, students will work on individual semestral works.

Study Objective:

Course learn to apply the machine learning methods and optimization algorithms on known robotics problems such as metrical or semantic mapping from RGB-D data or reactive motion control.

Study materials:

Thrun S., Burgard W., Fox D. Probabilistic robotics, MIT Press, 2006

Šonka M., Hlavác V., Boyle R.: Image processing, analysis, and machine vision, Cengage Learning, Toronto, 2015.

Note:
Further information:
https://cw.fel.cvut.cz/wiki/courses/B3B33VIR
Time-table for winter semester 2021/2022:
06:00–08:0008:00–10:0010:00–12:0012:00–14:0014:00–16:0016:00–18:0018:00–20:0020:00–22:0022:00–24:00
Mon
roomKN:E-301
Zimmermann K.
Vacek P.

14:30–16:00
(lecture parallel1)
Karlovo nám.
Šrámkova posluchárna K9
roomKN:E-230
Zimmermann K.
Vacek P.

16:15–17:45
(lecture parallel1
parallel nr.101)

Karlovo nám.
Laboratoř PC
Tue
roomKN:E-230
Zimmermann K.
Vacek P.

09:15–10:45
(lecture parallel1
parallel nr.103)

Karlovo nám.
Laboratoř PC
Wed
roomKN:E-230
Zimmermann K.
Vacek P.

16:15–17:45
(lecture parallel1
parallel nr.102)

Karlovo nám.
Laboratoř PC
roomKN:E-230
Zimmermann K.
Vacek P.

18:00–19:30
(lecture parallel1
parallel nr.104)

Karlovo nám.
Laboratoř PC
Thu
Fri
Time-table for summer semester 2021/2022:
Time-table is not available yet
The course is a part of the following study plans:
Data valid to 2022-08-08
For updated information see http://bilakniha.cvut.cz/en/predmet4675106.html