Students will acquire a solid theoretical foundation about human-robot interaction (HRI) and will bring the theory into practice working with a humanoid robot. Within the HRI field we will focus on the human-centric and artificial intelligence point of view.
The aims of the course are:
- To understand the core principles of HRI.
- To be able to explain the key features for designing interactive robots (e.g. companions or social robots) by means of understanding human perception.
- To gain knowledge about state-of-the-art perception and learning algorithms for interacting under uncertainty.
- To be capable of deploying algorithms in a humanoid robot and study its behaviour.
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The future expects that robots will be among us, in our houses, working with us, teaching our children, taking care of us in the hospital. How we can develop systems and algorithms that can face this challenge. This interdisciplinary course combines artificial intelligence, robotics, computer vision, cognitive psychology, and other related areas to allow robots to perform natural and efficient interactions in complex settings like human-robot interaction (HRI).
The course is formatted in 6 lectures (3 theoretical and 3 applied), 2 practical assignments and a final project. We will cover from basic perception and action in robotics, to more sophisticated robot learning (using probabilistic approaches and neural networks), and ending with high level aspects behaviours, such as attention, social robotics and measures to evaluate HRI.
The practical assignments and the project will provide hands-on experience in designing, implementing and experimenting with human-robot interaction. Students will work with a humanoid robot first in simulation and then the real platform (e.g. NAO robot). Besides, the final project will be performed in teams and designed by the students.
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Strong programming knowledge (Python or C++). It is recommended that the student has already coursed Introduction to Robotics or any other similar course.
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- Two practical assignments 30% (minimum grade 5.0)
- Final project 60% (minimum grade 5.0)
- Paper discussion 10%
In order to pass the course both practical assignments and the project have to be completed.
The paper discussion involves the reading and discussion of relevant works of the literature.
There is retake of the two practical assignments. (if students don't submit on time or fail, they have a second deadline with a grade of max 8 points).
There is no retake of the practical project.
Grading for the different parts will be made public in Brightspace. Only the final grade will be published in Osiris at the end of the course.
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Students who have passed the course Robotlab Practical (SOW-MKI59) are excluded from this course.
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