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. 2021 Jul 29:8:686490.
doi: 10.3389/frobt.2021.686490. eCollection 2021.

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

Yu Xia  1 , Alireza Mohammadi  1 , Ying Tan  1 , Bernard Chen  1 , Peter Choong  2   3 , Denny Oetomo  1   3
Affiliations

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

Yu Xia et al. Front Robot AI. .

Abstract

Haptic perception is one of the key modalities in obtaining physical information of objects and 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 haptic object identification to reduce the number of grasps required to correctly identify an object out of a given object set. Thus, in a case where multiple grasps are required to characterise an object, the proposed algorithm seeks to determine where the next grasp should be on the object to obtain the most amount of distinguishing information. As such, the paper proposes the construction of the object description that preserves the association of the spatial information and the haptic information on the object. A clustering technique is employed both to construct the description of the object in a data set 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.

Keywords: clustering; haptic based object identification; identification efficiency; information gain; object description.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
The characteristics of an object are regarded to be fully captured by grasping the object at n different poses. The n poses are spread, if possible uniformly, across the surface of the object.
FIGURE 2
FIGURE 2
An illustration of a 2D projection of the clusters formed by the M-dimensional normalised measurements (represented by each point on the plot) for all n poses in an objects for all ℓ objects in the dataset. The points are categorised into K grasp clusters.
FIGURE 3
FIGURE 3
The description of an arbitrary object in the object set.
FIGURE 4
FIGURE 4
Consists of 23 objects in a variety of shapes, sizes and stiffness.
FIGURE 5
FIGURE 5
Experimental platform. (A) The physical experimental setup. (B) The schematic of the experimental setup (side view).
FIGURE 6
FIGURE 6
The number of grasps required to correctly identify each of 23 objects using the proposed approach and the baseline approach: Scenario 1 (without uncertainty).
FIGURE 7
FIGURE 7
The accuracy of identifying each of 23 objects through the proposed approach: Scenario 2 (with uncertainty).
FIGURE 8
FIGURE 8
The number of grasps required to correctly identify each of 23 objects using the proposed approach: Scenario 2 (with uncertainty).
See this image and copyright information in PMC

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