Signal processing and fabrication of a biomimetic tactile sensor array with thermal, force and microvibration modalities

Lin, Chia Hsien; Erickson, Todd W.; Fishel, Jeremy A.; Wettels, Nicholas; Loeb, Gerald E.

doi:10.1109/robio.2009.5420611

Cited by 83 publications

(49 citation statements)

References 16 publications

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“…1) Hardware-centric: Many researchers have included thermal sensing as a part of multimodal tactile sensing hardware, such as absolute temperature sensors [10], [11] and sensors that use heat transfer [12], [13], [14], [15], [16]. However, this body of work focuses on hardware development with little evaluation of material recognition performance.…”

Section: B Long-duration Contact With Consistent Initial Conditionsmentioning

confidence: 99%

Material Recognition from Heat Transfer given Varying Initial Conditions and Short-Duration Contact

Bhattacharjee¹,

Wade²,

Kemp³

2015

Robotics: Science and Systems XI

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Abstract-When making contact with an object, a robot can use a tactile sensor consisting of a heating element and a temperature sensor to recognize the object's material based on conductive heat transfer from the tactile sensor to the object. When this type of tactile sensor has time to fully reheat prior to contact and the duration of contact is long enough to achieve a thermal steady state, numerous methods have been shown to perform well. In order to enable robots to more efficiently sense their environments and take advantage of brief contact events over which they lack control, we focus on the problem of material recognition from heat transfer given varying initial conditions and short-duration contact. We present both modelbased and data-driven methods. For the model-based method, we modeled the thermodynamics of the sensor in contact with a material as contact between two semi-infinite solids. For the data-driven methods, we used three machine learning algorithms (SVM+PCA, k-NN+PCA, HMMs) with time series of raw temperature measurements and temperature change estimates. When recognizing 11 materials with varying initial conditions and 3-fold cross-validation, SVM+PCA outperformed all other methods, achieving 84% accuracy with 0.5 s of contact and 98% accuracy with 1.5 s of contact.

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Section: B Long-duration Contact With Consistent Initial Conditionsmentioning

confidence: 99%

Material Recognition from Heat Transfer given Varying Initial Conditions and Short-Duration Contact

Bhattacharjee¹,

Wade²,

Kemp³

2015

Robotics: Science and Systems XI

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“…Furthermore, recent work has shown the necessity of having access to a rich set of sensory information in order to perform manipulation tasks of increasing complexity [21], [22], [5], [23]. Through the use of such advanced sensing devices, touch-based exploratory methods have developed that discover and learn object properties and manipulation strategies [19], [24], [25].…”

Section: A Dexterous Manipulationmentioning

confidence: 99%

“…The continued development of sensor technologies with increasing sophistication [21], [22], [5], [23] promoted the use of adaptive control schemes such as force and impedance control [16], [7], and later hybrid methods [5]. These approaches are hierarchically combined with high-level and predefined behavioral laws that triggers the controllers when appropriate.…”

Section: B Reactive Grasping and Contact Maintenancementioning

confidence: 99%

Iterative learning of grasp adaptation through human corrections

Sauser

Argall

Metta

et al. 2012

Robotics and Autonomous Systems

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Abstract-In the context of object interaction and manipulation, one characteristic of a robust grasp is its ability to comply with external perturbations applied to the grasped object while still maintaining the grasp. In this work we introduce an approach for grasp adaptation which learns a statistical model to adapt hand posture solely based on the perceived contact between the object and fingers. Using a multi-step learning procedure, the model dataset is built by first demonstrating an initial hand posture, which is then physically corrected by a human teacher pressing on the fingertips, exploiting compliance in the robot hand. The learner then replays the resulting sequence of hand postures, to generate a dataset of posture-contact pairs that are not influenced by the touch of the teacher. A key feature of this work is that the learned model may be further refined by repeating the correction-replay steps. Alternatively, the model may be reused in the development of new models, characterized by the contact signatures of a different object. Our approach is empirically validated on the iCub robot. We demonstrate grasp adaptation in response to changes in contact, and show successful model reuse and improved adaptation with additional rounds of model refinement.

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“…By having human like sensing skills, the system should be able to directly evaluate the contact state during interactions. Compared to imaging technologies where standards are established for data acquisition and representation, current tactile sensors posses a large variety of data acquisition techniques, which can be either based on electric [12], optic [15] or acoustic [6] effects. For example the authors in [2] discuss eight different technologies which are based on these three effects and are used in current state of the art tactile sensors.…”

Section: Introductionmentioning

confidence: 99%

“…The work presented in [13] used support vector machines and random forests to detect object slippage with a BioTac [6] sensor. The BioTac sensor offers multiple modalities such as 19 electrodes to measure local contacts with a sampling rate of 100 Hz, thermal sensors and two pressure transducers, one for low (up to 100 Hz) and one for high (up to 2.2 kHz) frequencies, respectively.…”

Section: Introductionmentioning

confidence: 99%

Tactile Convolutional Networks for Online Slip and Rotation Detection

Meier

Patzelt

Haschke

et al. 2016

Lecture Notes in Computer Science

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Abstract. We present a deep convolutional neural network which is capable to distinguish between different contact states in robotic manipulation tasks. By integrating spatial and temporal tactile sensor data from a piezo-resistive sensor array through deep learning techniques, the network is not only able to classify the contact state into stable versus slipping, but also to distinguish between rotational and translation slippage. We evaluated different network layouts and reached a final classification rate of more than 97%. Using consumer class GPUs, slippage and rotation events can be detected within 10 ms, which is still feasible for adaptive grasp control.

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Signal processing and fabrication of a biomimetic tactile sensor array with thermal, force and microvibration modalities

Cited by 83 publications

References 16 publications

Material Recognition from Heat Transfer given Varying Initial Conditions and Short-Duration Contact

Material Recognition from Heat Transfer given Varying Initial Conditions and Short-Duration Contact

Iterative learning of grasp adaptation through human corrections

Tactile Convolutional Networks for Online Slip and Rotation Detection

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