Tactile Features: Recognising Touch Sensations with a Novel and Inexpensive Tactile Sensor

Corradi, Tadeo; Hall, Peter M.; Iravani, Pejman

doi:10.1007/978-3-319-10401-0_15

Cited by 3 publications

(5 citation statements)

References 26 publications

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“…The new sensor [1] used in this paper is based on the same principle as the TacTip. However, it has neither papillae nor internal gel.…”

Section: Sensor and Tactile Data Representationmentioning

confidence: 99%

“…However, it has neither papillae nor internal gel. Instead it has a plain black smooth opaque silicone rubber hemispherical membrane of radius 40mm and thickness 1mm, mounted at the end of a rigid opaque encasing for the digital camera, 3D printed in ABS 1 . The camera has a resolution of 640 by 480 pixels, and incorporates a set of 8 white LEDs.…”

Section: Sensor and Tactile Data Representationmentioning

confidence: 99%

“…Due to the circular geometry of the sensor image, a rotationally invariant representation was required. In previous work, a number of encoding methods were compared and it was suggested that Zernike Moments together with PCA achieved the best performance [1]. Zernike Moments have been shown to be useful when scale, rotation and translation invariances are sought [22], and have been successfully used for basic shape recognition [23].…”

Section: Sensor and Tactile Data Representationmentioning

confidence: 99%

“…The system learns and recognises objects from singletouch events using a newly developed sensor [1]. Tactile sensations are encoded using Zernike Moments and objects are modeled by a sum of Gaussian distributions.…”

Section: Introductionmentioning

confidence: 99%

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Bayesian tactile object recognition: Learning and recognising objects using a new inexpensive tactile sensor

Corradi

Hall

Iravani

2015

2015 IEEE International Conference on Robotics and Automation (ICRA)

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We present a Bayesian approach to tactile object recognition that improves on state-of-the-art in using singletouch events in two ways. First by improving recognition accuracy from about 90% to about 95%, using about half the number of touches. Second by reducing the number of touches needed for training from about 200 to about 60. In addition, we use a new tactile sensor that is less than one tenth of the cost of widely available sensors. The paper describes the sensor, the likelihood function used with the Naive Bayes classifier, and experiments on a set of ten real objects. We also provide preliminary results to test our approach for its ability to generalise to previously unencountered objects.

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“…The new sensor [1] used in this paper is based on the same principle as the TacTip. However, it has neither papillae nor internal gel.…”

Section: Sensor and Tactile Data Representationmentioning

confidence: 99%

Section: Sensor and Tactile Data Representationmentioning

confidence: 99%

Section: Sensor and Tactile Data Representationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Bayesian tactile object recognition: Learning and recognising objects using a new inexpensive tactile sensor

Corradi

Hall

Iravani

2015

2015 IEEE International Conference on Robotics and Automation (ICRA)

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“…Corradi, et al (23,24) developed a new cheap tactile sensor in order to encoding tactile image. The new sensor was capable of distinguishing between object shapes with nearest neighbor.…”

Section: Introductionmentioning

confidence: 99%

Object Shape and Force Estimation using Deep Learning and Optical Tactile Sensor

Sarakon

Sakai

Shimonomura

et al. 2018

Proceedings of the 6th IIAE International Conference on Intelligent Systems and Image Processing 2018

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Touch sensing plays a necessarily essential role in robot perception. It helps robot understanding its surrounding environment and in particular the object that it interacts with. For this reasons, robots are equipped with tactile sensor. Moreover, tactile object recognition exhibits a challenge in practical scenarios. In this paper, we proposed object shape classification and force estimation based on deep learning and optical tactile sensor with one touching. This study consists of three methods. First, tactile image is selected by optical flow technique. Image augmentation is used to increase a number of image. Second, features of each image are extracted by modified VGG-16 layer. Last, object shape and force estimation classifier are multi-layer perceptron (MLP), which is a supervised learning technique. The experimental result shows that an accuracy rate is 98.9% for classifying six object shapes and 98.68% for estimating eleven force levels. The results showed that our method outperformed the previous methods that use tactile image and one touching.

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Object recognition combining vision and touch

2017

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This paper explores ways of combining vision and touch for the purpose of object recognition. In particular, it focuses on scenarios when there are few tactile training samples (as these are usually costly to obtain) and when vision is artificially impaired. Whilst machine vision is a widely studied field, and machine touch has received some attention recently, the fusion of both modalities remains a relatively unexplored area. It has been suggested that, in the human brain, there exist shared multi-sensorial representations of objects. This provides robustness when one or more senses are absent or unreliable. Modern robotics systems can benefit from multi-sensorial input, in particular in contexts where one or more of the sensors perform poorly. In this paper, a recently proposed tactile recognition model was extended by integrating a simple vision system in three different ways: vector concatenation (vision feature vector and tactile feature vector), object label posterior averaging and object label posterior product. A comparison is drawn in terms of overall accuracy of recognition and in terms of how quickly (number of training samples) learning occurs. The conclusions reached are: (1) the most accurate system is “posterior product”, (2) multi-modal recognition has higher accuracy to either modality alone if all visual and tactile training data are pooled together, and (3) in the case of visual impairment, multi-modal recognition “learns faster”, i.e. requires fewer training samples to achieve the same accuracy as either other modality.

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Tactile Features: Recognising Touch Sensations with a Novel and Inexpensive Tactile Sensor

Cited by 3 publications

References 26 publications

Bayesian tactile object recognition: Learning and recognising objects using a new inexpensive tactile sensor

Bayesian tactile object recognition: Learning and recognising objects using a new inexpensive tactile sensor

Object Shape and Force Estimation using Deep Learning and Optical Tactile Sensor

Object recognition combining vision and touch

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