A Deep Learning Framework for Tactile Recognition of Known as Well as Novel Objects

Abderrahmane, Zineb; Ganesh, Gowrishankar; Crosnier, André; Cherubini, Andrea

doi:10.1109/tii.2019.2898264

Cited by 23 publications

(13 citation statements)

References 45 publications

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“…Abderrahmane et al [32] proposed generating tactile data from semantic descriptions of objects. They provided 19 binary haptic adjectives, which included information about the material and shape of the objects.…”

Section: Estimating Haptic Interaction From Visionmentioning

confidence: 99%

“…We are, therefore, limited as regards the amount of information processed about the object. Nevertheless, if the item is previously recognised, we could include in the learning pipeline a vector of characteristics describing it, as Abderrahmane et al [32] showed in their work. Then, our system would receive more information about the object.…”

Section: Limitationsmentioning

confidence: 99%

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Generation of Tactile Data From 3D Vision and Target Robotic Grasps

Zapata-Impata

Gil

Mezouar

et al. 2021

IEEE Trans. Haptics

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Tactile perception is a rich source of information for robotic grasping: it allows a robot to identify a grasped object and assess the stability of a grasp, among other things. However, the tactile sensor must come into contact with the target object in order to produce readings. As a result, tactile data can only be attained if a real contact is made. We propose to overcome this restriction by employing a method that models the behaviour of a tactile sensor using 3D vision and grasp information as a stimulus. Our system regresses the quantified tactile response that would be experienced if this grasp were performed on the object. We experiment with 16 items and 4 tactile data modalities to show that our proposal learns this task with low error.

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Section: Estimating Haptic Interaction From Visionmentioning

confidence: 99%

Section: Limitationsmentioning

confidence: 99%

Generation of Tactile Data From 3D Vision and Target Robotic Grasps

Zapata-Impata

Gil

Mezouar

et al. 2021

IEEE Trans. Haptics

View full text Add to dashboard Cite

show abstract

“…OSL has been implemented in tactile-based recognition network for object classification (Kaboli et al, 2016 ). As compared to vision, OSL has not received much attention in tactile recognition, much less in sensorized soft robotics (Abderrahmane et al, 2020 ). Due to lightweight training dataset, this learning approach also allow us to potentially scale up the training process in the future to include more variety of objects.…”

Section: Related Workmentioning

confidence: 99%

Development and Grasp Stability Estimation of Sensorized Soft Robotic Hand

et al. 2021

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This paper introduces the development of an anthropomorphic soft robotic hand integrated with multiple flexible force sensors in the fingers. By leveraging on the integrated force sensing mechanism, grip state estimation networks have been developed. The robotic hand was tasked to hold the given object on the table for 1.5 s and lift it up within 1 s. The object manipulation experiment of grasping and lifting the given objects were conducted with various pneumatic pressure (50, 80, and 120 kPa). Learning networks were developed to estimate occurrence of object instability and slippage due to acceleration of the robot or insufficient grasp strength. Hence the grip state estimation network can potentially feedback object stability status to the pneumatic control system. This would allow the pneumatic system to use suitable pneumatic pressure to efficiently handle different objects, i.e., lower pneumatic pressure (50 kPa) for lightweight objects which do not require high grasping strength. The learning process of the soft hand is made challenging by curating a diverse selection of daily objects, some of which displays dynamic change in shape upon grasping. To address the cost of collecting extensive training datasets, we adopted one-shot learning (OSL) technique with a long short-term memory (LSTM) recurrent neural network. OSL aims to allow the networks to learn based on limited training data. It also promotes the scalability of the network to accommodate more grasping objects in the future. Three types of LSTM-based networks have been developed and their performance has been evaluated in this study. Among the three LSTM networks, triplet network achieved overall stability estimation accuracy at 89.96%, followed by LSTM network with 88.00% and Siamese LSTM network with 85.16%.

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“…[70]. AI and CV can be used for tactile recognition [71] to resist the transmission of the SARS-COV-2 virus from various surfaces to the human body by recognizing objects already touched and objects not touched before [72].…”

Section: Diagnosis Of Covid-19 Using Aimentioning

confidence: 99%

Review of Recent Technologies for Tackling COVID-19

Alharbi

Rahman

2021

SN COMPUT. SCI.

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The current pandemic caused by the COVID-19 virus requires more effort, experience, and science-sharing to overcome the damage caused by the pathogen. The fast and wide human-to-human transmission of the COVID-19 virus demands a significant role of the newest technologies in the form of local and global computing and information sharing, data privacy, and accurate tests. The advancements of deep neural networks, cloud computing solutions, blockchain technology, and beyond 5G (B5G) communication have contributed to the better management of the COVID-19 impacts on society. This paper reviews recent attempts to tackle the COVID-19 situation using these technological advancements.

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A Deep Learning Framework for Tactile Recognition of Known as Well as Novel Objects

Cited by 23 publications

References 45 publications

Generation of Tactile Data From 3D Vision and Target Robotic Grasps

Generation of Tactile Data From 3D Vision and Target Robotic Grasps

Development and Grasp Stability Estimation of Sensorized Soft Robotic Hand

Review of Recent Technologies for Tackling COVID-19

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