AUTHOR=Xia Yu , Mohammadi Alireza , Tan Ying , Chen Bernard , Choong Peter , Oetomo Denny 

TITLE=On the Efficiency of Haptic Based Object Identification: Determining Where to Grasp to Get the Most Distinguishing Information

JOURNAL=Frontiers in Robotics and AI

VOLUME=Volume 8 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/robotics-and-ai/articles/10.3389/frobt.2021.686490

DOI=10.3389/frobt.2021.686490

ISSN=2296-9144

ABSTRACT=Tactile perception is one of the key approaches in obtaining physical information of objects and has been used in object identification. Most existing literature focused on improving the accuracy of identification algorithms with less attention paid to the efficiency. This work aims to investigate the efficiency of tactile-based object identification to reduce the number of grasps required to correctly identify an object out of a given object set. Thus, it seeks to determine where to grasp the object to obtain the most amount of distinguishing information. The paper proposes the construction of the object description that preserves the association of the spatial information on the object and the tactile information it yields. A clustering technique is employed both to construct the description and for the identification process. An information gain (IG) based method is then employed to determine which pose would yield the most distinguishing information among the remaining possible candidates in the object set to improve the efficiency of the identification process. This proposed algorithm is validated experimentally. A Reflex TakkTile robotic hand with integrated joint displacement and tactile sensors is used to perform both the data collection for the dataset and the object identification procedure. The proposed IG approach was found to require a significantly lower number of grasps to identify the objects compared to a baseline approach where the decision was made by random choice of grasps.