Active Tactile Exploration Based on Cost-Aware Information Gain Maximization

Ottenhaus, Simon; Kaul, Lukas; Vahrenkamp, Nikolaus; Asfour, Tamim

doi:10.1142/s0219843618500159

Cited by 19 publications

(6 citation statements)

References 35 publications

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“…Specifically, IG assigns the most distinguishable feature with the highest information value. Currently, IG has been developed and applied to decision trees, simultaneous localization and mapping (SLAM), and feature selection techniques ( Kent, 1983 ; Lee and Lee, 2006 ) and in object shape re-construction by improving the exploration efficiency ( Ottenhaus et al, 2018 ).…”

Section: Proposed Methodologymentioning

confidence: 99%

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

et al. 2021

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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.

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Section: Proposed Methodologymentioning

confidence: 99%

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

et al. 2021

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“…For instance, Driess et al [35], [45] used a compliant controller to facilitate data collection along object surfaces, and simultaneously used Gaussian Process Regression to estimate the object shape. Similar methods have also been adopted by, Rosales et al (2018) [46] and Ottenhaus et al ( 2018) [47]. While the works mentioned above indicate that simple geometric shape estimation can be accomplished through sliding sensor motion, the effectiveness of such an approach on complex object surfaces remains to be shown.…”

Section: Active Tactile Spatial Explorationmentioning

confidence: 95%

Active Multi-Object Exploration and Recognition via Tactile Whiskers

Xiao,

Xu,

et al. 2021

Preprint

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Robotic exploration under uncertain environments is challenging when optical information is not available. In this paper, we propose an autonomous solution of exploring an unknown task space based on tactile sensing alone. We first designed a whisker sensor based on MEMS barometer devices. This sensor can acquire contact information by interacting with the environment non-intrusively.This sensor is accompanied by a planning technique to generate exploration trajectories by using mere tactile perception. This technique relies on a hybrid policy for tactile exploration, which includes a proactive informative path planner for object searching, and a reactive Hopf oscillator for contour tracing. Results indicate that the hybrid exploration policy can increase the efficiency of object discovery.Last, scene understanding was facilitated by segmenting objects and classification. A classifier was developed to recognize the object categories based on the geometric features collected by the whisker sensor. Such an approach demonstrates the whisker sensor, together with the tactile intelligence, can provide sufficiently discriminative features to distinguish objects.

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“…In addition to the approaches mentioned in the shape detection of geometric objects, the approach based on tactile perception is also a trend at present. In the research [ 116 ], the problem of shape reconstruction from sparse tactile data is studied. The Information Gain Estimation Function combines different goals as a criterion to quantify the cost-aware information gain during exploration.…”

Section: Unknown Objectsmentioning

confidence: 99%

Feature Sensing and Robotic Grasping of Objects with Uncertain Information: A Review

Wang

Zhang

Zang

et al. 2020

Sensors

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As there come to be more applications of intelligent robots, their task object is becoming more varied. However, it is still a challenge for a robot to handle unfamiliar objects. We review the recent work on the feature sensing and robotic grasping of objects with uncertain information. In particular, we focus on how the robot perceives the features of an object, so as to reduce the uncertainty of objects, and how the robot completes object grasping through the learning-based approach when the traditional approach fails. The uncertain information is classified into geometric information and physical information. Based on the type of uncertain information, the object is further classified into three categories, which are geometric-uncertain objects, physical-uncertain objects, and unknown objects. Furthermore, the approaches to the feature sensing and robotic grasping of these objects are presented based on the varied characteristics of each type of object. Finally, we summarize the reviewed approaches for uncertain objects and provide some interesting issues to be more investigated in the future. It is found that the object’s features, such as material and compactness, are difficult to be sensed, and the object grasping approach based on learning networks plays a more important role when the unknown degree of the task object increases.

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Active Tactile Exploration Based on Cost-Aware Information Gain Maximization

Cited by 19 publications

References 35 publications

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

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

Active Multi-Object Exploration and Recognition via Tactile Whiskers

Feature Sensing and Robotic Grasping of Objects with Uncertain Information: A Review

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