Robot Vision
| 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 Tue Wed 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:
-
- Cybernetics and Robotics 2016 (compulsory elective course)