Learning to Predict Perceptual Distributions of Haptic Adjectives.

ABSTRACT: When humans touch an object with their fingertips, they can immediately describe its tactile properties using haptic adjectives, such as hardness and roughness; however, human perception is subjective and noisy, with significant variation across individuals and interactions. Recent research has worked to provide robots with similar haptic intelligence but was focused on identifying binary haptic adjectives, ignoring both attribute intensity and perceptual variability. Combining ordinal haptic adjective labels gathered from human subjects for a set of 60 objects with features automatically extracted from raw multi-modal tactile data collected by a robot repeatedly touching the same objects, we designed a machine-learning method that incorporates partial knowledge of the distribution of obje

SUBMITTER: Richardson BA 

PROVIDER: S-EPMC7016190 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature