Human-Robot Interaction

Course Description

Human-robot interaction is a research field dedicated to understanding, designing and evaluating robot systems for use by or with human. In the beginning we shall look into basic principles and early history of human-robot interaction, as well as certain psychological and ethical aspects of the field. Then we shall focus on multimodal interaction and human tracking by using a camera and microphone array. Thereafter, we shall analyze the use of Bayesian Theory of Mind for estimating human intentions. We shall analyze the challenges of task sharing and physical human-robot interaction. In the end we shall look into the problem of haptic robot teleoperation.

Learning Outcomes

  1. define and describe the principles of multimodal interaction
  2. develop algorithms for speaker tracking
  3. apply machine learning methods for people tracking
  4. define and describe the principles of phyisical interaction
  5. develop haptic teleoperation algorithms

Forms of Teaching

Lectures

Lectures will be interactive where students will follow the lecturer step-by-step in solving simple examples.

Seminars and workshops

Students will present the results of their project assignment.

Independent assignments

Students will in groups solve more complex examples from the materials covered by lectures.

Grading Method

Continuous Assessment Exam
Type Threshold Percent of Grade Threshold Percent of Grade
Seminar/Project 50 % 30 % 50 % 30 %
Mid Term Exam: Written 50 % 35 % 50 %
Final Exam: Written 50 % 35 %

Week by Week Schedule

  1. Social robotics, Anthropomorphism and design, the uncanny valley, human-robot interaction architectures
  2. Sensors and perception for human-robot interactions, Robot audition and other sensing interfaces
  3. Sensors and perception for human-robot interactions, Robot audition and other sensing interfaces
  4. Human motion detection, tracking and prediction
  5. Human motion detection, tracking and prediction
  6. Human intention prediction, Bayesian networks, hidden Markov models
  7. Human intention prediction, Bayesian networks, hidden Markov models
  8. Midterm exam
  9. Decision making from noisy data, Markov decision processes, partially observable Markov decision processes
  10. Decision making from noisy data, Markov decision processes, partially observable Markov decision processes
  11. Task sharing between a human and a robot
  12. Synchronisation of tasks execution
  13. Physical interaction for rehabilitation and assistive applications
  14. Telerobotics and telepresence, teleoperation with feedback
  15. Final exam

Study Programmes

University graduate
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[FER3-HR] Computer Science - profile
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[FER3-HR] Control Systems and Robotics - profile
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[FER3-HR] Electronic and Computer Engineering - profile
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[FER3-HR] Electronics - profile
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[FER3-HR] Information and Communication Engineering - profile
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[FER3-HR] Network Science - profile
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[FER3-HR] Software Engineering and Information Systems - profile
Elective Courses (3. semester)

Literature

Christoph Bartneck (2020.), Human-Robot Interaction: An Introduction, Cambridge University Press
Paolo Barattini, Federico Vicentini, Gurvinder Singh Virk, Tamas Haidegger (2019.), Human-Robot Interaction: Safety, Standardization, and Benchmarking, Routledge

For students

General

ID 222576
  Winter semester
5 ECTS
L1 English Level
L3 e-Learning
30 Lectures
0 Seminar
0 Exercises
8 Laboratory exercises
0 Project laboratory

Grading System

87,5 Excellent
75 Very Good
62,5 Good
50 Sufficient