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DD2413 Social Robotics 7.5 credits

This course provides an overview of the-state-of-the-art algorithms, computational techniques and paradigms used to build robots that interact with people.

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Headings with content from the Course syllabus DD2413 (Autumn 2020–) are denoted with an asterisk ( )

Content and learning outcomes

Course contents

  • Introduction to the field: types of interaction, anthropomorphism and embodying, design principles of social robotics.
  • Building of social robots: generic system design, software components and systems.
  • The robot perception of the user: different modalities and sensor fusion.
  • Verbal and non-verbal communication: dialogue, movement and animation.
  • Social reasoning and decision making.
  • Experiment design how to design and carry out HRI-experiments, common measurements for HRI, annotation of data and behavioural analysis.
  • Social learning.
  • Cooperation between man and robot.
  • Application areas: remote-controlled robots from control to semiautonomous, socially assisting robots for education and healthcare.
  • Social and ethical considerations of use in social environments.

Intended learning outcomes

On completion of the course, the students should be able to

  • apply different concepts within social robotics
  • choose and justify efficient calculation methods for the ability of social robots to perceive, make decisions and move
  • use suitable software design and tools to develop applications for social robotics
  • design, analyse and document experiments in human-robot-interaction (HRI)
  • demonstrate understanding of the social and ethical aspects of the design, the development and the use of social robots.

Course disposition

No information inserted

Literature and preparations

Specific prerequisites

Completed course equivalent DD1320 Applied computer science.

Recommended prerequisites

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Examination and completion

If the course is discontinued, students may request to be examined during the following two academic years.

Grading scale

A, B, C, D, E, FX, F


  • LAB1 - Laboratory work, 1.5 credits, grading scale: P, F
  • LAB2 - Laboratory work, 3.0 credits, grading scale: A, B, C, D, E, FX, F
  • PRO1 - Final project, 3.0 credits, grading scale: A, B, C, D, E, FX, F

Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.

The examiner may apply another examination format when re-examining individual students.

Opportunity to complete the requirements via supplementary examination

No information inserted

Opportunity to raise an approved grade via renewed examination

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Profile picture Iolanda Dos Santos Carvalho Leite

Ethical approach

  • All members of a group are responsible for the group's work.
  • In any assessment, every student shall honestly disclose any help received and sources used.
  • In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.

Further information

Course web

Further information about the course can be found on the Course web at the link below. Information on the Course web will later be moved to this site.

Course web DD2413

Offered by

EECS/Intelligent Systems

Main field of study

Computer Science and Engineering

Education cycle

Second cycle

Add-on studies

No information inserted


Iolanda Leite

Supplementary information

In this course, the EECS code of honor applies, see: