Data-driven analysis of kinaesthetic and tactile information for shape classification

Oliveira, Thiago Eustaquio Alves de; Fonseca, Vinícius Prado da; Huluta, Emanuil; Rosa, Paulo Fernando Ferreira; Petriu, Emil M.

doi:10.1109/civemsa.2015.7158615

Cited by 8 publications

(5 citation statements)

References 8 publications

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“…These interactions can be static, usually generating tactile images [ 3 , 4 ], or dynamic, generating signals that vary over time [5] , [6] , [7] , [8] . Our dataset focuses on dynamic tactile data for texture characterization but utilizes a set of commonly used tactile textures instead of synthetic gratings [9] or macroscopic profiles [10] . This dataset is also valuable because it aims to avoid a halt in the research of tactile perception of textures, considering that previous research, such as [ 11 , 12 ], have not maintained the availability of their data.…”

Section: Value Of the Datamentioning

confidence: 99%

Section: Value Of the Datamentioning

confidence: 99%

See 1 more Smart Citation

A multimodal tactile dataset for dynamic texture classification

Monteiro Rocha Lima,

Danyamraju,

Alves de Oliveira

et al. 2023

Data in Brief

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Section: Value Of the Datamentioning

confidence: 99%

Section: Value Of the Datamentioning

confidence: 99%

A multimodal tactile dataset for dynamic texture classification

Monteiro Rocha Lima,

Danyamraju,

Alves de Oliveira

et al. 2023

Data in Brief

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“…CCD camera solutions involve generally a high cost and weight, while the loss of light due to microbending or chirping causes distortion is the measured data [23]. To alleviate the drawbacks associated to any one single technology, a possible solution is to capitalize on the use of multiple sensor technologies for recuperating tactile information [8,24,25,26]. A biomimetic fingertip containing three accelerometers and seven force sensors in two layers of polyurethane [27], is employed to discriminate six fabrics and an aluminum plate by comparing the differences in their surface texture in [28].…”

Section: Literature Reviewmentioning

confidence: 99%

“…The robot is able to correctly identify 10 different objects on 99 out of 100 presentations. A data-driven analysis to the problem of sensor selection in the contour following for shape discrimination task is presented in [26]. Data collected from the motors, an inertial measurement unit, and a magnetometer attached to a 4-DOF robotic finger, during the exploration of seven synthetic shapes are analyzed using principal component analysis and a multilayer perceptron neural network is trained to classify the shapes.…”

Section: Literature Reviewmentioning

confidence: 99%

Multimodal Bio-Inspired Tactile Sensing Module for Surface Characterization

Oliveira

Creţu

Petriu

2017

Sensors

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Robots are expected to recognize the properties of objects in order to handle them safely and efficiently in a variety of applications, such as health and elder care, manufacturing, or high-risk environments. This paper explores the issue of surface characterization by monitoring the signals acquired by a novel bio-inspired tactile probe in contact with ridged surfaces. The tactile module comprises a nine Degree of Freedom Microelectromechanical Magnetic, Angular Rate, and Gravity system (9-DOF MEMS MARG) and a deep MEMS pressure sensor embedded in a compliant structure that mimics the function and the organization of mechanoreceptors in human skin as well as the hardness of the human skin. When the modules tip slides over a surface, the MARG unit vibrates and the deep pressure sensor captures the overall normal force exerted. The module is evaluated in two experiments. The first experiment compares the frequency content of the data collected in two setups: one when the module is mounted over a linear motion carriage that slides four grating patterns at constant velocities; the second when the module is carried by a robotic finger in contact with the same grating patterns while performing a sliding motion, similar to the exploratory motion employed by humans to detect object roughness. As expected, in the linear setup, the magnitude spectrum of the sensors’ output shows that the module can detect the applied stimuli with frequencies ranging from 3.66 Hz to 11.54 Hz with an overall maximum error of ±0.1 Hz. The second experiment shows how localized features extracted from the data collected by the robotic finger setup over seven synthetic shapes can be used to classify them. The classification method consists on applying multiscale principal components analysis prior to the classification with a multilayer neural network. Achieved accuracies from 85.1% to 98.9% for the various sensor types demonstrate the usefulness of traditional MEMS as tactile sensors embedded into flexible substrates.

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“…The authors of Lederman and Klatzky (1987) pointed out six different exploratory movements to identify the properties of an object. Authors of de Oliveira et al (2015b) proposed a data-driven analysis for shape discrimination tasks using a robotic finger that performs the sliding movement. Lima et al (2020) developed an experiment with a sliding tactile-enabled robotic fingertip to explore textures dynamically.…”

Section: Introductionmentioning

confidence: 99%

Active learning strategies for robotic tactile texture recognition tasks

Das,

Prado da Fonseca,

Soares

2024

Front. Robot. AI

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Accurate texture classification empowers robots to improve their perception and comprehension of the environment, enabling informed decision-making and appropriate responses to diverse materials and surfaces. Still, there are challenges for texture classification regarding the vast amount of time series data generated from robots’ sensors. For instance, robots are anticipated to leverage human feedback during interactions with the environment, particularly in cases of misclassification or uncertainty. With the diversity of objects and textures in daily activities, Active Learning (AL) can be employed to minimize the number of samples the robot needs to request from humans, streamlining the learning process. In the present work, we use AL to select the most informative samples for annotation, thus reducing the human labeling effort required to achieve high performance for classifying textures. We also use a sliding window strategy for extracting features from the sensor’s time series used in our experiments. Our multi-class dataset (e.g., 12 textures) challenges traditional AL strategies since standard techniques cannot control the number of instances per class selected to be labeled. Therefore, we propose a novel class-balancing instance selection algorithm that we integrate with standard AL strategies. Moreover, we evaluate the effect of sliding windows of two-time intervals (3 and 6 s) on our AL Strategies. Finally, we analyze in our experiments the performance of AL strategies, with and without the balancing algorithm, regarding f1-score, and positive effects are observed in terms of performance when using our proposed data pipeline. Our results show that the training data can be reduced to 70% using an AL strategy regardless of the machine learning model and reach, and in many cases, surpass a baseline performance. Finally, exploring the textures with a 6-s window achieves the best performance, and using either Extra Trees produces an average f1-score of 90.21% in the texture classification data set.

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Data-driven analysis of kinaesthetic and tactile information for shape classification

Cited by 8 publications

References 8 publications

A multimodal tactile dataset for dynamic texture classification

A multimodal tactile dataset for dynamic texture classification

Multimodal Bio-Inspired Tactile Sensing Module for Surface Characterization

Active learning strategies for robotic tactile texture recognition tasks

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