Tactile Sensors for Friction Estimation and Incipient Slip Detection—Toward Dexterous Robotic Manipulation: A Review

Chen, Wei; Khamis, Heba; Birznieks, Ingvars; Lepora, Nathan F.; Redmond, Stephen J.

doi:10.1109/jsen.2018.2868340

Cited by 155 publications

(102 citation statements)

References 102 publications

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“…Once slippage is detected, the robotic hand needs to adjust its contact configuration. Tactile information has been used as the primary sensory modality for slip detection [2]. Most of the previous works formulated slip detection as a classification problem, in which a classifier was built with an SVM [3] or random forest [4].…”

Section: Introductionmentioning

confidence: 99%

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Grasping Force Control of Multi-Fingered Robotic Hands through Tactile Sensing for Object Stabilization

Deng

Jonetzko

Zhang

et al. 2020

Sensors

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Grasping force control is important for multi-fingered robotic hands to stabilize the grasped object. Humans are able to adjust their grasping force and react quickly to instabilities through tactile sensing. However, grasping force control through tactile sensing with robotic hands is still relatively unexplored. In this paper, we make use of tactile sensing for multi-fingered robot hands to adjust the grasping force to stabilize unknown objects without prior knowledge of their shape or physical properties. In particular, an online detection module based on Deep Neural Network (DNN) is designed to detect contact events and object material simultaneously from tactile data. In addition, a force estimation method based on Gaussian Mixture Model (GMM) is proposed to compute the contact information (i.e., contact force and contact location) from tactile data. According to the results of tactile sensing, an object stabilization controller is then employed for a robotic hand to adjust the contact configuration for object stabilization. The spatio-temporal property of tactile data is exploited during tactile sensing. Finally, the effectiveness of the proposed framework is evaluated in a real-world experiment with a five-fingered Shadow Dexterous Hand equipped with BioTac sensors.

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Section: Introductionmentioning

confidence: 99%

“…This work trains an online detection module to identify contact events and material simultaneously. (2) An online detection module is designed based on a deep neural network for slip and material detection. It is not necessary to design features manually for slip or material detection.…”

Section: Introductionmentioning

confidence: 99%

Grasping Force Control of Multi-Fingered Robotic Hands through Tactile Sensing for Object Stabilization

Deng

Jonetzko

Zhang

et al. 2020

Sensors

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“…The markers are located inside the elastomer with different colors to estimate force magnitude and direction based on markers displacement. Later on, this approach became more popular which reflects the significant progress in camera-based tactile sensors [30]- [33].…”

Section: Introductionmentioning

confidence: 99%

A Novel Dynamic-Vision-Based Approach for Tactile Sensing Applications

Naeini

Alali

Al-Husari

et al. 2020

IEEE Trans. Instrum. Meas.

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In this paper, a novel Vision-Based Measurement (VBM) approach is proposed to estimate the contact force and classify materials in a single grasp. This approach is the first event-based tactile sensor which utilizes the recent technology of neuromorphic cameras. This novel approach provides a higher sensitivity, a lower latency, and less computational and power consumption compared to other conventional visionbased techniques. Moreover, Dynamic Vision Sensor (DVS) has a higher dynamic range which increases the sensor sensitivity and performance in poor lighting conditions. Two time-series machine learning methods, namely, Time Delay Neural Network (TDNN) and Gaussian Process (GP) are developed to estimate the contact force in a grasp. A Deep Neural Network (DNN) is proposed to classify the object materials. Forty-eight experiments are conducted for four different materials to validate the proposed methods and compare them against a piezoresistive force sensor measurements. A leave-one-out cross-validation technique is implemented to evaluate and analyze the performance of the proposed machine learning methods. The contact force is successfully estimated with a mean squared error of 0.16 N and 0.17 N for TDNN and GP respectively. Four materials are classified with an average accuracy of 79.17% using unseen experimental data. The results show the applicability of eventbased sensors for grasping applications.

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“…Learning-based approaches for grasping are also abundant with some relying on large amounts of robot trials [20] or synthetic data [21] while others combine learning with analytic grasp metrics [22] or use lower-dimensional sub-spaces to find appropriate hand grasping postures [23]. For a more extensive overview of the grasping and manipulation fields we refer the reader to [24,25].…”

Section: Introductionmentioning

confidence: 99%

Grip Stabilization through Independent Finger Tactile Feedback Control

Veiga

Edin

Peters

2020

Sensors

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Grip force control during robotic in-hand manipulation is usually modeled as a monolithic task, where complex controllers consider the placement of all fingers and the contact states between each finger and the gripped object in order to compute the necessary forces to be applied by each finger. Such approaches normally rely on object and contact models and do not generalize well to novel manipulation tasks. Here, we propose a modular grip stabilization method based on a proposition that explains how humans achieve grasp stability. In this biomimetic approach, independent tactile grip stabilization controllers ensure that slip does not occur locally at the engaged robot fingers. Local slip is predicted from the tactile signals of each fingertip sensor i.e., BioTac and BioTac SP by Syntouch. We show that stable grasps emerge without any form of central communication when such independent controllers are engaged in the control of multi-digit robotic hands. The resulting grasps are resistant to external perturbations while ensuring stable grips on a wide variety of objects.

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Tactile Sensors for Friction Estimation and Incipient Slip Detection—Toward Dexterous Robotic Manipulation: A Review

Cited by 155 publications

References 102 publications

Grasping Force Control of Multi-Fingered Robotic Hands through Tactile Sensing for Object Stabilization

Grasping Force Control of Multi-Fingered Robotic Hands through Tactile Sensing for Object Stabilization

A Novel Dynamic-Vision-Based Approach for Tactile Sensing Applications

Grip Stabilization through Independent Finger Tactile Feedback Control

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