Deep Visuo-Tactile Learning: Estimation of Tactile Properties from Images

Takahashi, Kuniyuki; Tan, Jethro

doi:10.1109/icra.2019.8794285

Cited by 52 publications

(29 citation statements)

References 29 publications

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“…In ref. [113], estimation of tactile properties based on visual perception has been modelled, where the data are obtained based on using a webcam and uSkin tactile sensor located at the Sawyer robot's end-effector. The purpose of this modality is to increase the perception of the robot to be more aware of the contacting environment of the object while grasping.…”

Section: Multi-sensor Controlmentioning

confidence: 99%

Comprehensive Review on Reaching and Grasping of Objects in Robotics

Mohammed¹,

Chua²,

Kwek³

2021

Robotica

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SUMMARY Interaction between a robot and its environment requires perception about the environment, which helps the robot in making a clear decision about the object type and its location. After that, the end effector will be brought to the object’s location for grasping. There are many research studies on the reaching and grasping of objects using different techniques and mechanisms for increasing accuracy and robustness during grasping and reaching tasks. Thus, this paper presents an extensive review of research directions and topics of different approaches such as sensing, learning and gripping, which have been implemented within the current five years.

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Section: Multi-sensor Controlmentioning

confidence: 99%

Comprehensive Review on Reaching and Grasping of Objects in Robotics

Mohammed¹,

Chua²,

Kwek³

2021

Robotica

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show abstract

“…Three major challenges in creating a clear elastomer are the long curing time of about six to seven hours [48], quality consistency [49], and the formation of air bubbles within a gel that needs vacuum pump for degassing [48]- [51]. Aside from these challenges in creating clear elastomer in the lab, according to [52], the GelSight might have an impressive spatial resolution (30-100 microns), but the elastomer can be easily damaged during grasping thus requires frequent [42], [45], (h) GelSlim (2018): A compact design with slant mirror inside and skin fabric gel covering. LED: only white.…”

Section: A Retrographic Sensormentioning

confidence: 99%

Visuotactile Sensors With Emphasis on GelSight Sensor: A Review

2020

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This review paper focuses on vision and touch-based sensors known as visuotactile. The study of visuotactile sensation and perception became a multidisciplinary field of study by philosophers, psychologists, biologists, engineers, technologists, and roboticists in the fields of haptics, machine vision, and artificial intelligence and it dates back centuries. To the best of our knowledge, the earliest records of visuotactile sensor was not applied to robotics and was not even for hand or finger imprint analysis yet for recording the foot pressure distribution of a walking or standing human known as pedobarograph. Our review paper presents the different literature related to visuotactile sensors that lead to a high-resolution miniature pedobarographlike sensor known as the GelSight sensor. Moreover, this review paper focuses on architecture, different techniques, hardware, and software development of GelSight sensor since 2009 with its applications in haptics, robotics, and computer vision.

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“…The maximum covariance analysis was used to pair the learned features to learn the cross‐modal visual–tactile shared representation. Takahashi and Tan [128] and Gandarias et al [129] recently studied how to obtain tactile features from optical images based on self‐encoding networks and CNNs and how to use them to enhance the tactile perception capability. Falco et al [130] established an active exploration framework to realize cross‐modal visual–tactile object recognition.…”

Section: Embodied Tactile Learningmentioning

confidence: 99%

Embodied tactile perception and learning

Liu

Guo

Sun

et al. 2020

Brain Science Advances

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Various living creatures exhibit embodiment intelligence, which is reflected by a collaborative interaction of the brain, body, and environment. The actual behavior of embodiment intelligence is generated by a continuous and dynamic interaction between a subject and the environment through information perception and physical manipulation. The physical interaction between a robot and the environment is the basis for realizing embodied perception and learning. Tactile information plays a critical role in this physical interaction process. It can be used to ensure safety, stability, and compliance, and can provide unique information that is difficult to capture using other perception modalities. However, due to the limitations of existing sensors and perception and learning methods, the development of robotic tactile research lags significantly behind other sensing modalities, such as vision and hearing, thereby seriously restricting the development of robotic embodiment intelligence. This paper presents the current challenges related to robotic tactile embodiment intelligence and reviews the theory and methods of robotic embodied tactile intelligence. Tactile perception and learning methods for embodiment intelligence can be designed based on the development of new large‐scale tactile array sensing devices, with the aim to make breakthroughs in the neuromorphic computing technology of tactile intelligence.

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Deep Visuo-Tactile Learning: Estimation of Tactile Properties from Images

Cited by 52 publications

References 29 publications

Comprehensive Review on Reaching and Grasping of Objects in Robotics

Comprehensive Review on Reaching and Grasping of Objects in Robotics

Visuotactile Sensors With Emphasis on GelSight Sensor: A Review

Embodied tactile perception and learning

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