When perceiving an object by touch, human beings integrate various sensory information with observing the object's surface in appropriate manners, such as rubbing, pushing and picking, according to the proceeding of the recognition. This fact suggests that sensory integration and active perception play essential roles in the haptic recognition process.
In light of such characteristics of haptics, the author constructed a haptic recognition system which descriminated feel of touch in a similar manner to human's. The system is equipped with several sensor devices, including a vibration sensor, a friction sensor and a thermal sensor, and can push and rub the object's surface with several values of force and speed. It integrates information from these sensors iteratively with selecting an appropriate sensor and a measurement condition according to the proceeding of the recognition. The algorithms of sensory integration and of active perception are realized by Bayes inference and by an iterative experimental design based on an information criterion, respectively.
The experimental result shows that the system can discriminate subtle difference in feel of touch: It can discriminate 16 kinds of paper and cloth almost perfectly. It is also proved that the system selects appropriate sensors according to the proceeding of the recogntion, that is, the active perception algorithm realizes good recognition accuracy by fewer observations than the random observation algorithm. In addition, it is shown experimentally that the characteristics used in the system well correspond to those human beings utilize in haptic recognition. These results suggest that the constructed system is a faithful model of the human haptic mechanism.
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