https://studiegids.vu.nl/en/courses/2024-2025/XM_0074The overall objective of this course is that students will be able to apply basic design skills to create interaction designs for a social robot and develop key skills of the social robot using AI techniques. Students will gain an understanding about social robotics and related AI techniques to control such a robot (e.g., conversational AI, computer vision aimed at interacting with people, expressing gestures, etc.) and apply this knowledge for designing a social robotics use case with the Nao robot (Applying Knowledge and Understanding) Students will be asked to evaluate their use case prototype on a social robot by piloting their user study design with peers that take part in the course (Making Judgments) Students will need to present their use case ideas and design in the course (during lectures) and (outside lectures) work in groups to design a use case and will need to define and communicate about their individual roles within their group (Communication). Students will be challenged to take the initiative and direct their own learning by designing a specific use case for a social robot, where their design is grounded in existing (multi-disciplinary) literature (Learning Skills).In this course we will take a user-centered approach to the design of social robots and look into AI techniques for developing a social robot that can interact with human users. We will look at the basic cognitive skills we expect a social robot to have, including visual perception (e.g. face recognition), speech recognition and dialogue, emotional expression through body language, and the architecture for integrating these various skills to execute them on the robot.Lectures, and practical work (to be done by students in groups).You will be asked to complete a practical assignment with a group of students. Grades for each of the main deliverables will contribute to a final grade for your group work and will be weight as follows: Robot software (20%) Design document (50%) Final presentation (30%) Bonus point on final grade based on votes for final group presentations (top ranked group 1 point, second-ranked group .5 point, within each of the two sessions). Every group member is expected to keep track of their weekly progress in the form of a logbook. In case we find clear differences in what and how much individual group members have contributed to the final result (deliverables), we may take this into account and differentiate grades for individual group members. We will also use input from the teaching assistants, who will discuss with your group each week to establish such differences. It will not be possible to redo the practical assignment (no resit). There will also be individual micro-assignments presented during class, which will be graded as a pass or fail. You will receive .5 point bonus if you pass four or more.A brief course manual will be made available. The main literature used will consist of existing literature (papers and other materials) on social robotics and related AI techniques.Master Artificial IntelligenceThe capacity for this course is 100 students.Important: Students who cannot attend the practical sessions on campus cannot join this course (because these practical sessions are crucial for the learning goals).Students should have the programming skills (Python) and ability to learn to use a programming framework for social robots that will be made available in the course.