Tactile Object Recognition with Semi-Supervised Learning

Luo, Shan; Liu, Xiaozhou; Althoefer, Kaspar; Liu, Hongbin

doi:10.1007/978-3-319-22876-1_2

Cited by 19 publications

(13 citation statements)

References 31 publications

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“…This is also particularly useful for tactile object recognition, since the touches could introduce unexpected object rotation and translation. SIFT has been explored in [35], [101]- [103] for tactile recognition and a good performance can be achieved. As both visual images and tactile readings are present in numerical matrices, many other vision descriptors [104], [105] also have the potential to be applied in tactile sensing, e.g., Shape Context [106], SURF [107] and 3D descriptor SHOT [108].…”

Section: A Local Shape Recognitionmentioning

confidence: 99%

Robotic tactile perception of object properties: A review

et al. 2017

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Abstract-Touch sensing can help robots understand their surrounding environment, and in particular the objects they interact with. To this end, roboticists have, in the last few decades, developed several tactile sensing solutions, extensively reported in the literature. Research into interpreting the conveyed tactile information has also started to attract increasing attention in recent years. However, a comprehensive study on this topic is yet to be reported. In an effort to collect and summarize the major scientific achievements in the area, this survey extensively reviews current trends in robot tactile perception of object properties. Available tactile sensing technologies are briefly presented before an extensive review on tactile recognition of object properties. The object properties that are targeted by this review are shape, surface material and object pose. The role of touch sensing in combination with other sensing sources is also discussed. In this review, open issues are identified and future directions for applying tactile sensing in different tasks are suggested.

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Section: A Local Shape Recognitionmentioning

confidence: 99%

Robotic tactile perception of object properties: A review

et al. 2017

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“…The Bag-of-Words (BoW) approach (Jurie and Triggs, 2005), commonly used in computer vision methods, is then used to learn and classify the objects based on these features. Luo et al (2015), also present an approach which utilises tactile images and a BoW approach to object recognition. An optimised BoW approach and dictionary were produced by considering different outputs of the classic BoW with different dictionary sizes for different views of objects.…”

Section: Background and Related Researchmentioning

confidence: 99%

Material recognition using tactile sensing

Kerr

McGinnity

Coleman

2018

Expert Systems with Applications

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Identification of the material from which an object is made is of significant value for effective robotic grasping and manipulation. Characteristics of the material can be retrieved using different sensory modalities: vision based, tactile based or sound based. Compressibility, surface texture and thermal properties can each be retrieved from physical contact with an object using tactile sensors. This paper presents a method for collecting data using a biomimetic fingertip in contact with various materials and then using these data to classify the materials both individually and into groups of their type. Following acquisition of data, principal component analysis (PCA) is used to extract features. These features are used to train seven different classifiers and hybrid structures of these classifiers for comparison. For all materials, the artificial systems were evaluated against each other, compared with human performance and were all found to outperform human participants' average performance. These results highlighted the sensitive nature of the BioTAC sensors and pave the way for research that requires a sensitive and accurate approach such as vital signs monitoring using robotic systems.

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“…Independent of the approach, the majority of these works are based on the use of artificial intelligence algorithms to classify the data. One proposition consists of using computer vision algorithms and machine learning techniques by treating the pressure data as images [ 38 , 39 ]. Moreover, related works made use of neural networks and deep learning [ 40 , 41 ].…”

Section: Related Workmentioning

confidence: 99%

Enhancing Perception with Tactile Object Recognition in Adaptive Grippers for Human–Robot Interaction

Gandarias

Gómez-de-Gabriel

García-Cerezo

2018

Sensors

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The use of tactile perception can help first response robotic teams in disaster scenarios, where visibility conditions are often reduced due to the presence of dust, mud, or smoke, distinguishing human limbs from other objects with similar shapes. Here, the integration of the tactile sensor in adaptive grippers is evaluated, measuring the performance of an object recognition task based on deep convolutional neural networks (DCNNs) using a flexible sensor mounted in adaptive grippers. A total of 15 classes with 50 tactile images each were trained, including human body parts and common environment objects, in semi-rigid and flexible adaptive grippers based on the fin ray effect. The classifier was compared against the rigid configuration and a support vector machine classifier (SVM). Finally, a two-level output network has been proposed to provide both object-type recognition and human/non-human classification. Sensors in adaptive grippers have a higher number of non-null tactels (up to 37% more), with a lower mean of pressure values (up to 72% less) than when using a rigid sensor, with a softer grip, which is needed in physical human–robot interaction (pHRI). A semi-rigid implementation with 95.13% object recognition rate was chosen, even though the human/non-human classification had better results (98.78%) with a rigid sensor.

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Tactile Object Recognition with Semi-Supervised Learning

Cited by 19 publications

References 31 publications

Robotic tactile perception of object properties: A review

Robotic tactile perception of object properties: A review

Material recognition using tactile sensing

Enhancing Perception with Tactile Object Recognition in Adaptive Grippers for Human–Robot Interaction

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