Learning to Predict Perceptual Distributions of Haptic Adjectives

Richardson, Benjamin A.; Kuchenbecker, Katherine J.

doi:10.3389/fnbot.2019.00116

Cited by 12 publications

(8 citation statements)

References 29 publications

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“…Therefore, Ref. [22] complements this aspect of the study by confirming the antonym pairs of hard/soft, rough/smooth, and cold/warm, and more tactile information than the binary label is analyzed.…”

Section: Related Workmentioning

confidence: 56%

“…In real scenes, humans who have a rich tactile perception system will obtain a delicate sensory feedback by touching an object and will have a specific cognition of the strength level of the object's attribute. For networks, only using binary tactile labels to describe objects will simplify object attributes to binary space and the network has a very rough understanding of the level of the strength of an object's attribute [22].…”

mentioning

confidence: 99%

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Identifying the Strength Level of Objects’ Tactile Attributes Using a Multi-Scale Convolutional Neural Network

Zhang

Shan

et al. 2022

Sensors

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In order to solve the problem in which most currently existing research focuses on the binary tactile attributes of objects and ignores identifying the strength level of tactile attributes, this paper establishes a tactile data set of the strength level of objects’ elasticity and hardness attributes to make up for the lack of relevant data, and proposes a multi-scale convolutional neural network to identify the strength level of object attributes. The network recognizes the different attributes and identifies differences in the strength level of the same object attributes by fusing the original features, i.e., the single-channel features and multi-channel features of the data. A variety of evaluation methods were used for comparison with multiple models in terms of strength levels of elasticity and hardness. The results show that our network has a more significant effect in accuracy. In the prediction results of the positive examples in the predicted value, the true value has a higher proportion of positive examples, that is, the precision is better. The prediction effect for the positive examples in the true value is better, that is, the recall is better. Finally, the recognition rate for all classes is higher in terms of f1_score. For the overall sample, the prediction of the multi-scale convolutional neural network has a higher recognition rate and the network’s ability to recognize each strength level is more stable.

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Section: Related Workmentioning

confidence: 56%

mentioning

confidence: 99%

Identifying the Strength Level of Objects’ Tactile Attributes Using a Multi-Scale Convolutional Neural Network

Zhang

Shan

et al. 2022

Sensors

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show abstract

“…In addition, in [11,12], a number of different researchers were selected to give binary ratings to adjectives of different objects, but the antisense relationships between adjectives were not taken into account. Therefore, [13] complements this aspect of the study by confirming the antonym pairs of hard/soft, rough/smooth and cold/warm, And more tactile information than binary label is analyzed.…”

Section: Related Workmentioning

confidence: 63%

“…Touching an object will get a delicate sensory feedback and have a specific cognition of the strength of the object's attributes. However, for robot tactile perception, only using binary tactile labels to describe objects will simplify the object attributes to binary space [13], which makes the robot have a very rough cognition of the strength of the object attributes.…”

mentioning

confidence: 99%

Recognition of Tactile Attribute Strength and Category Using Convolutional Neural Network

Zhang¹,

Yu²,

Shan³

et al. 2021

Preprint

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Objectives: In order to solve the problem that most of the existing research focuses on the binary tactile attributes of objects,which ignores the tactile attribute strength and category recognition,an attribute strength and category recognition method based on convolutional neural network matrix-label is proposed. Methods:Firstly,in the data preparation stage,we preprocess the raw data and determine the matrix labels to build the haptic dataset.Secondly,in the feature extraction stage,we fuse the haptic data of two fingers and use the convolutional neural network to extract the attribute strength features.Finally,in the attribute strength and category recognition stage,all channel haptic data is fused to predict the attribute strength and category.Results:We compared with the multi-label convolutional neural network method in terms of elastic strength,hardness strength and category,and compared the attribute strength recognition capabilities of the two methods using novel objects outside the haptic dataset.The results show that the accuracy of the last 20 iterations of the matrix-label method has an average elastic strength of 96.73%,hardness strength of 97.34%,and category of 96.67%.The performance is better.When the Euclidean distance between the prediction of the novel object and the real label is less than 1,the accuracy of the elastic strength is best to reach 100%,and the hardness strength is best to reach 100%.The performance is better. Conclusions:The effectiveness of the method has been verified.Comparing with the convolutional neural network method,our method can effectively recognize the attribute strength and category of objects.

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“…[ 42 ] These researches related to sensing and classification of surface materials were useful to develop various haptic applications delivering the physical information to humans because the surface characteristics should be well recognized in advance. [ 43–45 ] However, these tactile material classifications do not represent the overall tactile sensation of human. Because the processing of tactile feeling by the human brain has not yet been revealed clearly and tactile responses vary among humans, it is challenging to imitate the tactile sensation by an artificial system.…”

Section: Introductionmentioning

confidence: 99%

Tactile Avatar: Tactile Sensing System Mimicking Human Tactile Cognition

et al. 2021

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As a surrogate for human tactile cognition, an artificial tactile perception and cognition system are proposed to produce smooth/soft and rough tactile sensations by its user's tactile feeling; and named this system as “tactile avatar”. A piezoelectric tactile sensor is developed to record dynamically various physical information such as pressure, temperature, hardness, sliding velocity, and surface topography. For artificial tactile cognition, the tactile feeling of humans to various tactile materials ranging from smooth/soft to rough are assessed and found variation among participants. Because tactile responses vary among humans, a deep learning structure is designed to allow personalization through training based on individualized histograms of human tactile cognition and recording physical tactile information. The decision error in each avatar system is less than 2% when 42 materials are used to measure the tactile data with 100 trials for each material under 1.2N of contact force with 4cm s−1 of sliding velocity. As a tactile avatar, the machine categorizes newly experienced materials based on the tactile knowledge obtained from training data. The tactile sensation showed a high correlation with the specific user's tendency. This approach can be applied to electronic devices with tactile emotional exchange capabilities, as well as advanced digital experiences.

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Learning to Predict Perceptual Distributions of Haptic Adjectives

Cited by 12 publications

References 29 publications

Identifying the Strength Level of Objects’ Tactile Attributes Using a Multi-Scale Convolutional Neural Network

Identifying the Strength Level of Objects’ Tactile Attributes Using a Multi-Scale Convolutional Neural Network

Recognition of Tactile Attribute Strength and Category Using Convolutional Neural Network

Tactile Avatar: Tactile Sensing System Mimicking Human Tactile Cognition

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