This course is an archived course. It remains open to registrations although it is not facilitated by the course teachers: its contents are no longer updated and may therefore no longer be up to date (course contents were created in 2015). If you register, you can freely consult the read-only resources but all collaborative spaces are closed (forums, wiki and other collaborative exercises): you cannot interact with the teaching team or with other learners. Furthermore, no attestation of achievement will be delivered for this course.
About This Course
Mobile Robots are increasingly working in close interaction with human beings in environments as diverse as homes, hospitals, public spaces, public transportation systems and disaster areas. The situation is similar when it comes to Autonomous Vehicles, which are equipped with robot-like capabilities (sensing, decision and control).
Such robots must balance constraints such as safety, efficiency and autonomy, while addressing the novel problems of acceptability and human-robot interaction. Given the high stakes involved, developing these technologies is clearly a major challenge for both the industry and the human society.
OBJECTIVES, CHALLENGES, STATE OF THE ART
BAYES & KALMAN FILTERS
EXTENDED KALMAN FILTERS
PERCEPTION & SITUATION AWARENESS & DECISION MAKING
BEHAVIOR MODELING AND LEARNING (with examples and exercises in Python)