Tutoring master students in a topic of "Design of motion cueing algorithm for an immersive wheelchair simulator: Active preference learning on parameters tuning"

Motion Cueing Algorithm is studied with the aim of providing high fidelity motions for users of the simulator. In this work, MCA tries to regenerate wheelchair motion cues by transforming motions of the real or simulated wheelchair into the simulator motion in such a way which "minimizes" the sensation error for the simulator users. However, lacking the tool to quantitatively measure the sensation error raises the problem of evaluating the quality of the designed control system as well as parameter tuning in the design phase. This is the point where the active preference learning technique is used which provides a tool to exploit the qualitative measurement obtained from the feeling of simulator users to tune parameters of MCA.