Tactile sensing based softness classification using machine learning

Bandyopadhyaya, Irin; Babu, Dennis; Kumar, Ambuj; Roychowdhury, Joydeb

doi:10.1109/iadcc.2014.6779503

Cited by 31 publications

(22 citation statements)

References 12 publications

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“…Perhaps most similar to our application, [27] used tactile sensors to classify interactions between a robot hand and its environment. There are similar works in this area (e.g., [28,29,30,31,32]) that propose the use of ML to relate complex tactile data to object and material classes. In this paper, we aim to use a similar approach in that we propose the novel application of ML and proprioceptive sensing to classify the excavation media in industrial excavation activities.…”

Section: Machine Learning For Classificationmentioning

confidence: 99%

“…The feasibility of classifying "rock" and "gravel" materials using this data set is evaluated using two supervised learning algorithms, k-nearest neighbours (KNN) and artificial neural networks (ANN), and one unsupervised learning algorithm, k-means. There are many other potential classification algorithms such Support Vector Machines, Decision Trees, and Naive Bayes; however, KNN, ANN and k-means were selected because of their simplicity, popularity, and demonstrated suitability in classification applications-particularly in robotics (e.g., [28,22,26,29]). The three algorithms are described with further detail in In contrast, unsupervised learning algorithms do not have an explicit teacher.…”

Section: Binary Classification Of Rock and Gravelmentioning

confidence: 99%

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What lies beneath: Material classification for autonomous excavators using proprioceptive force sensing and machine learning

Fernando

Marshall

2020

Automation in Construction

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The ability of robotic excavators to acquire meaningful knowledge about materials during digging can augment their autonomous functionality, as well as optimize downstream operations in construction and mining. Some material properties, such as rock sizes, can be determined visually, but these methods cannot see what lies beneath. In this work, a classification methodology that utilizes only proprioceptive force data acquired from an autonomous digging system and machine learning algorithms is proposed for excavation material identification. The consistent performance synonymous with autonomous digging systems allows for the use of basic features extracted from the force data for classification. A proof of concept of this novel approach to excavation material classification is demonstrated through a binary classification of rock and gravel materials. Force data were obtained from full-scale autonomous loading trials with a 14-tonne capacity load-haul-dump machine at a mining and construction test facility. Preliminary results achieved a classification accuracy of 90 %.

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Section: Machine Learning For Classificationmentioning

confidence: 99%

Section: Binary Classification Of Rock and Gravelmentioning

confidence: 99%

What lies beneath: Material classification for autonomous excavators using proprioceptive force sensing and machine learning

Fernando

Marshall

2020

Automation in Construction

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“…An artificial finger with embedded PolyVinyliDene Fluoride (PVDF) membrane and strain gauge sensors are used to classify various materials [11]. Another research is presented in [12], whereas two piezoresistive tactile sensors are utilized to classify softness of vegetable using a decision-tree machine learning technique. Other applications of object classifications using different types of force sensors and traditional machine learning algorithms such as Support Vector Machines (SVM) are presented in [13], [14].…”

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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“…Finally, the support vector machine (SVM) is trained and used to recognize the hardness. Other methods based on traditional machine learning include: decision tree based method (Bandyopadhyaya et al, 2014), k-nearest neighbors (KNN) based method (Drimus et al, 2011), and SVM based method (Kaboli et al, 2014), etc. Recently, the deep learning technique has made great progress and has been successfully applied in many fields (Han et al, 2013, 2015; Wu et al, 2015, 2017; Li et al, 2016; Zhang et al, 2017; Hou et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Hardness Recognition of Robotic Forearm Based on Semi-supervised Generative Adversarial Networks

Qian

Zhang

et al. 2019

Front. Neurorobot.

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The hardness recognition is of great significance to tactile sensing and robotic control. The hardness recognition methods based on deep learning have demonstrated a good performance, however, a huge amount of manually labeled samples which require lots of time and labor costs are necessary for the training of deep neural networks. In order to alleviate this problem, a semi-supervised generative adversarial network (GAN) which requires less manually labeled samples is proposed in this paper. First of all, a large number of unlabeled samples are made use of through the unsupervised training of GAN, which is used to provide a good initial state to the following model. Afterwards, the manually labeled samples corresponding to each hardness level are individually used to train the GAN, of which the architecture and initial parameter values are inherited from the unsupervised GAN, and augmented by the generator of trained GAN. Finally, the hardness recognition network (HRN), of which the main architecture and initial parameter values are inherited from the discriminator of unsupervised GAN, is pretrained by a large number of augmented labeled samples and fine-tuned by manually labeled samples. The hardness recognition result can be obtained online by importing the tactile data captured by the robotic forearm into the trained HRN. The experimental results demonstrate that the proposed method can significantly save the manual labeling work while providing an excellent recognition precision for hardness recognition.

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Tactile sensing based softness classification using machine learning

Cited by 31 publications

References 12 publications

What lies beneath: Material classification for autonomous excavators using proprioceptive force sensing and machine learning

What lies beneath: Material classification for autonomous excavators using proprioceptive force sensing and machine learning

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

Hardness Recognition of Robotic Forearm Based on Semi-supervised Generative Adversarial Networks

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