Beta Mixture Model for the Uncertainties in Robotic Haptic Object Identification

Robotic haptic object identification is the process to identify objects out of a given object set using a robotic hand equipped with tactile and finger-joint displacement sensors. When taking measurements by grasping the object, the uncertainties in the pose of the object relative to the hand will adversely affect the identification accuracy. Each tactile sensor measures contact in its locality, thus, a change in object contact locations relative to the robotic grasping hand significantly affects the tactile measurements. In object identification, statistical properties of the uncertainties in the collected measurements are generally obtained a priori, allowing the probabilities of an object to be estimated for improved accuracy. The problem of object pose uncertainty typical in robotic grasping results in multiple peaks in the probability distribution of the resulting tactile measurements. The peaks are associated with whether or not the (locality of) tactile sensor on the robotic hand is in contact with the object due to the variations in object pose. As such, in this article, a Beta mixture model allowing multiple peaks in the distribution is proposed to represent this object pose uncertainty (relative to the robotic hand) in place of the conventional Gaussian model used in the literature. The method was experimentally validated and demonstrated to be effective in capturing the uncertainties and improving the accuracy of the haptic object identification.