Aims
This course is an introduction to Intelligent Robotic Systems, i.e., machines that move (themselves and/or objects in their environment) and sense what is going on in their (immediate) neighbourhood, in order to achieve a given goal under uncertain environment conditions.
Applying AI techniques to a physical system poses challenges that are not apparent in other contexts. This course aims to teach how one casts an embodied-agent problem to a form that lends itself to an AI solution, for instance by choosing AI techniques, sensor/data representations and motor command schemes that are synergetic with one another.
This course will cover both "classical" AI techniques that are easily parametrized by an expert, and techniques that are learned from data. We will study the applicability of both approaches and discuss how to judiciously choose one or the other based on the nature of the task.
Since robotics is about integrating the best things from several research areas (mechanics, computer science, geometry, artificial intelligence, ...), relationships with other courses often occur, but we avoid overlaps as much as possible. The students are intensively stimulated to think and discuss as a researcher.
During the course, students
identify problems that lend themselves to a robot AI solution and decide whether a “classical” or data-driven solution should be preferred,
cast an embodied-agent problem to a form that lends itself to an AI solution,
generate an intelligent robot behavior (conceptual or in software):
Extract information from sensor streams (e.g., object/people identity/position, body postures, 3D room and object structures),
Control robot actuators,
Learn useful sensorimotor behaviors (e.g., mobility or grasping),
learn to analyse robotics applications from a system-level point of view, since robotics is very much a science of integration.
are stimulated to develop a critical, research-oriented attitude.
Content
This course is organized as guided self study: there is only a limited number of lectures in class (to explain and discuss the fundamental concepts of robot AI). For the rest of the course the students work on problems of their own choice. Collaboration in groups of maximally three students is encouraged.
The course has no organized examination session: it uses continuous evaluation, based on the students' reports, to which feedback is provided by the lecturer and all other students. Reports and the feedback to them are public to all participating students, and become an inherent part of the "course material". In a final individual discussion session with the lecturer, each student is expected to present the material in a relevant academic research paper in a very critical way, and to show creativity in identifying appropriate applications, open problems, or inherent limitations in the studied material.
The concept of the course allows to adapt its contents to the interests and background of the students.
More information can be found on the course's webpage: https://renaud-detry.net/teaching/h02a4a/
