Patent ID: 11972052
Assignee: UNIVERSITY OF SOUTHERN CALIFORNIA
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 6:
7. A system comprising:
a motorized touch device configured to apply haptic force and tactile feedback on a hand, allowing users to feel virtual objects and touch sensations;
at least one programmable processor; and
a non-transitory machine-readable medium storing instructions which, when executed by the at least one programmable processor, cause the at least one programmable processor to perform operations comprising:
training, based on training data associated with textures of materials, a texture model generator to map a set of latent variables to haptic texture models; and
performing, via the texture model generator, a texture search by an algorithm configured to search a desired virtual texture through evolving latent variables in a desired region in a latent space, such that the virtual texture can be provided to a user with the motorized touch device,

wherein the texture search comprises: evolving of the latent variables associated with the texture model generator using a Covariance Matrix Adaptation Evolution Strategy (CMA-ES), and
wherein the texture search for a given real texture comprises:
a. sampling a first set of latent variables from a normal distribution having a same dimensionality as the latent space of the texture model generator;
b. inputting the first set of latent variables to the texture model generator to generate one or more texture models;
c. rendering the one or more texture models via a haptic user interface comprising the touch device to allow users to touch the one or more texture models;
d. receiving user selection of a texture model from the one or more texture models, the user selected texture model being closest to a given real texture;
e. updating, based on the user selected texture model, a mean of the normal distribution;
f. adapting a covariance of the normal distribution to increase a speed of the texture search;
g. sampling a second set of latent variables using an updated normal distribution and performing steps a-f until the user is satisfied that the selected texture model is sufficiently close to the given real texture; and
h. tuning a friction coefficient of a Coulomb friction model for a virtual texture based on a user's preference to match a perceived resistance of the given real texture.