This course teaches how to use data for increased realism in haptic modeling and devices. The learning outcomes include understanding haptography, haptic recording devices, texture recording procedures, sound modeling, synthesizing sound outputs, and preference-driven tuning. Students will learn about haptic models, friction, texture generative models, and data-driven social touch. The teaching method involves a combination of theoretical concepts and practical demonstrations. The course is intended for individuals interested in haptic technology, data-driven modeling, and enhancing realism in virtual environments.
Overview
Syllabus
Introduction.
HAPTOGRAPHY.
HAPTIC RECORDING DEVICE.
HAPTIC TEXTURE RECORDING PROCEDURE.
RECORDED DATA.
SOUND MODELING.
SYNTHESIZING A NEW SOUND OUTPUT.
OLD WAY: HAND TUNING MODELS.
NEW WAY: PREFERENCE-DRIVEN TUNING.
HAPTIC MODELS: FRICTION AND TEXTURE.
TEXTURE GENERATIVE MODEL.
PREFERENCE-DRIVEN MODELING FRAMEWORK.
TUNING TEXTURE MODELS.
REALISM OF MODELS.
ENCOUNTERED-TYPE HAPTIC DEVICE.
COMPARING TO TRADITIONAL RENDERING METHODS.
RESULTS: REALISM.
DATA-DRIVEN SOCIAL TOUCH.
EMOTION ACCURACY.
REAL-TIME TRANSMISSION OF TOUCH.
STUDYING EFFECT OF SPEED ON EMOTION.
Taught by
Stanford Online
