Using Data for Increased Realism with Haptic Modeling and Devices

Learn about cutting-edge haptic technology and data-driven approaches to creating realistic touch sensations in this Stanford seminar. Explore the process of haptography, including recording textures with specialized devices and synthesizing new haptic outputs. Discover how preference-driven modeling and machine learning techniques are revolutionizing haptic model tuning, surpassing traditional hand-tuned methods. Examine the development of encountered-type haptic devices and their advantages over conventional rendering methods. Delve into the fascinating world of data-driven social touch, investigating emotion accuracy in touch communication and the real-time transmission of tactile sensations. Gain insights into ongoing research on the relationship between touch speed and emotional conveyance in this comprehensive 56-minute presentation on advanced haptic modeling and devices.

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.