Robotic tactile perception of object properties: A review

Luo, Shan; Bimbo, João; Dahiya, Ravinder; Liu, Hongbin

doi:10.1016/j.mechatronics.2017.11.002

Cited by 339 publications

(223 citation statements)

References 192 publications

(307 reference statements)

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“…Material detection from tactile information is essential for robots, which identify object properties, such as texture, hardness, or friction [23]. Chu et al [9] proposed Hidden Markov Models (HMM) to analyze the temporal fluctuation of tactile data and trained a linear SVM to detect object material.…”

Section: Related Workmentioning

confidence: 99%

Grasping Force Control of Multi-Fingered Robotic Hands through Tactile Sensing for Object Stabilization

Deng

Jonetzko

Zhang

et al. 2020

Sensors

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Grasping force control is important for multi-fingered robotic hands to stabilize the grasped object. Humans are able to adjust their grasping force and react quickly to instabilities through tactile sensing. However, grasping force control through tactile sensing with robotic hands is still relatively unexplored. In this paper, we make use of tactile sensing for multi-fingered robot hands to adjust the grasping force to stabilize unknown objects without prior knowledge of their shape or physical properties. In particular, an online detection module based on Deep Neural Network (DNN) is designed to detect contact events and object material simultaneously from tactile data. In addition, a force estimation method based on Gaussian Mixture Model (GMM) is proposed to compute the contact information (i.e., contact force and contact location) from tactile data. According to the results of tactile sensing, an object stabilization controller is then employed for a robotic hand to adjust the contact configuration for object stabilization. The spatio-temporal property of tactile data is exploited during tactile sensing. Finally, the effectiveness of the proposed framework is evaluated in a real-world experiment with a five-fingered Shadow Dexterous Hand equipped with BioTac sensors.

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Section: Related Workmentioning

confidence: 99%

Grasping Force Control of Multi-Fingered Robotic Hands through Tactile Sensing for Object Stabilization

Deng

Jonetzko

Zhang

et al. 2020

Sensors

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“…In recent years, researchers started to use active learning for shape reconstruction from tactile sensing [33], [34], [35], [36]. Luo et al recently wrote a comprehensive review article on tactile perception which includes object shape perception [37].…”

Section: B Tactile Sensing For Shape Reconstructionmentioning

confidence: 99%

3D Shape Perception from Monocular Vision, Touch, and Shape Priors

Wang

Sun

et al. 2018

2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Perceiving accurate 3D object shape is important for robots to interact with the physical world. Current research along this direction has been primarily relying on visual observations. Vision, however useful, has inherent limitations due to occlusions and the 2D-3D ambiguities, especially for perception with a monocular camera. In contrast, touch gets precise local shape information, though its efficiency for reconstructing the entire shape could be low. In this paper, we propose a novel paradigm that efficiently perceives accurate 3D object shape by incorporating visual and tactile observations, as well as prior knowledge of common object shapes learned from large-scale shape repositories. We use vision first, applying neural networks with learned shape priors to predict an object's 3D shape from a single-view color image. We then use tactile sensing to refine the shape; the robot actively touches the object regions where the visual prediction has high uncertainty. Our method efficiently builds the 3D shape of common objects from a color image and a small number of tactile explorations (around 10). Our setup is easy to apply and has potentials to help robots better perform grasping or manipulation tasks on real-world objects.

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“…Thanks to the development of tactile sensor technologies [14]- [16], haptic object shape processing has received increasing attention in the recent years [17]. Some research arXiv:1806.07507v1 [cs.RO] 19 Jun 2018 produced point clouds by collecting contact points to constrain the geometry of the object [18], [19] whereas some others relied on tactile appearances by extracting features from tactile readings [5], [20], [21].…”

Section: Literature Reviewmentioning

confidence: 99%

iCLAP: shape recognition by combining proprioception and touch sensing

et al. 2018

Self Cite

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For humans, both the proprioception and touch sensing are highly utilized when performing haptic perception. However, most approaches in robotics use only either proprioceptive data or touch data in haptic object recognition. In this paper, we present a novel method named Iterative Closest Labeled Point (iCLAP) to link the kinesthetic cues and tactile patterns fundamentally and also introduce its extensions to recognize object shapes. In the training phase, the iCLAP first clusters the features of tactile readings into a codebook and assigns these features with distinct label numbers. A 4D point cloud of the object is then formed by taking the label numbers of the tactile features as an additional dimension to the 3D sensor positions; hence, the two sensing modalities are merged to achieve a synthesized perception of the touched object. Furthermore, we developed and validated hybrid fusion strategies, product based and weighted sum based, to combine decisions obtained from iCLAP and single sensing modalities. Extensive experimentation demonstrates a dramatic improvement of object recognition using the proposed methods and it shows great potential to enhance robot perception ability.

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Robotic tactile perception of object properties: A review

Cited by 339 publications

References 192 publications

Grasping Force Control of Multi-Fingered Robotic Hands through Tactile Sensing for Object Stabilization

Grasping Force Control of Multi-Fingered Robotic Hands through Tactile Sensing for Object Stabilization

3D Shape Perception from Monocular Vision, Touch, and Shape Priors

iCLAP: shape recognition by combining proprioception and touch sensing

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