Introduction to Biologically-inspired Robotics and Learning (Summer School)
Academic Study Board of the Faculty of Engineering
Teaching language: English
EKA: T930009102
Censorship: Second examiner: Internal
Grading: 7-point grading scale
Offered in: Odense
Offered in: Summer school (spring)
Level: Bachelor
Course ID: T930009101
ECTS value: 5
Date of Approval: 01-12-2022
Duration: Intensive course
Version: Approved - active
Course ID
Course Title
ECTS value
5
Internal Course Code
Responsible study board
Administrative Unit
Date of Approval
Course Responsible
| Name | Department | |
|---|---|---|
| Kamilla Juel Sørensen | kjs@tek.sdu.dk | TEK Uddannelseskoordinering og -support |
| Preben Hagh Strunge Holm | prebenh@mmmi.sdu.dk | SDU Robotics |
Teachers
Programme Secretary
Offered in
Level
Offered in
Duration
Mandatory prerequisites
Two years of studies at university level (equivalent to 120 ECTS) within a relevant field of study, before the summer school starts.
Recommended prerequisites
Students should have some knowledge of Matlab and at least one of the following programming languages – C/C++ or Python. Basic knowledge of reading from sensors and control of motors used in mechatronics and robotics is a plus.
Overall learning objectives
Upon completion of the course, the student will be able to extract relevant principles of sensing,
control and learning from biological systems and apply them to technology.
Learning objectives - Knowledge
Having completed the course the successful student will have knowledge about:
- Concepts in embodied artificial intelligence, neurorobotics and biorobotics
- How biological neurons and their engineering models work
- How biological learning in neurons and its engineering models work
Learning objectives - Skills
Having completed the course the successful student will be able to:
- Construct simple neural brains for robots to solve a task
- Apply biological learning algorithms to allow robots to learn
Learning objectives - Competences
Having completed the course the successful student will be able to:
- Posing a robotic problem as a neural learning problem
- Building appropriate neural brains to solve robotic problems
Content
- Biologically-inspired approaches to robotics - embodied artificial intelligence, neurorobotics and biorobotics
- Biological neurons and their simplified engineering models
- Simple artificial neural brains for sensing and control
- Learning mechanisms in biological neurons and their simplified engineering models
- Learning in artificial neural brains
URL for Skemaplan
Teaching Method
Lectures, computer simulation exercises, group project work.
Time of classes: two weeks in August.
First week – lectures and simulation exercises, second week - group project with mobile robot
Number of lessons
hours per week
Teaching language
Examination regulations
Exam regulations
Name
Exam regulations
Examination is held
At the end of the course
Tests
Exam
EKA
T930009102
Name
Exam
Description
The examination is based on an overall assessment of:
- Attendance (minimum 80%)
- Oral exam assessing general understanding of the course content and the group project.
Form of examination
Oral examination
Censorship
Second examiner: Internal
Grading
7-point grading scale
Identification
Student Identification Card - Date of birth
Language
English
ECTS value
5
Additional exam information
The form of examination in the re-examination is the same as in the ordinary examination except the requirement of 80% attendance which is removed.
Additional information
Enrolment is limited to 30 students. If more applicants than places, applicants who meet the mandatory requirements are prioritised according to the below selection criteria:
- Undergraduate and graduate students from partner universities (exchange); international undergraduate and graduate guest students (fee-paying); undergraduate and graduate students from other Danish universities.
- Ph.D students from partner universities and other international Ph.D. students; other applicants.
Students are prioritised on a first come, first served basis, i.e. according to the time we receive your complete application.
In case a course is filled up, we try to offer you an alternative course from your list of priorities. All final decisions about admission will be sent out continually.