An approach to integrated tactile perception

Taddeucci, D.; Laschi, Cecilia; Lazzarini, R.; Magni, R.; Dario, P.; Starita, Antonina

doi:10.1109/robot.1997.606759

Cited by 27 publications

(11 citation statements)

References 14 publications

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“…3) Thermal and Other Sensing Modalities: Taddeucci et al [30] used a multimodal haptic sensing finger with thermal and vibration feedback and a high resolution array of tactile sensors to identify 14 objects during idealized sliding contact using neural networks. [31]- [33] used the BioTAC TM sensor with thermal feedback to classify objects using Bayesian learning techniques, ANNs and HMMs.…”

Section: A Human Vs Object Recognitionmentioning

confidence: 99%

Data-driven thermal recognition of contact with people and objects

Bhattacharjee

Wade

Chitalia

et al. 2016

2016 IEEE Haptics Symposium (HAPTICS)

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Many tactile sensors can readily detect physical contact with an object, but tactile recognition of the type of object remains challenging. In this paper, we provide evidence that data-driven thermal tactile sensing can be used to recognize contact with people and objects in real-world settings. We created a portable handheld device with three tactile sensing modalities: a heat-transfer sensor that is actively heated, a small thermally-isolated temperature sensor, and a force sensor to detect the onset of contact. Using this device, we collected data from contact with the arms of 10 people (3 locations on the right arm) and contact with 80 objects relevant to robotic assistance (8 object types in 10 residential bathrooms). We then used support vector machines (SVMs) to perform binary classifications relevant to assistive robots. When classifying contact as person vs. object, classifiers that only used the temperature sensor performed best (average accuracy of 98.75% for 3.65s of contact, 93.13% for 1.0s, and 82.13% for 0.5s). When classifying contact into two task-relevant object types (e.g., towel vs. towel rack), classifiers that used the heattransfer sensor together with the temperature sensor performed best. Performance was good when generalizing to new contact locations in the same environment (average accuracy of 92.14% for 3.65s of contact, 91.43% for 1.0s, and 84.29% for 0.5s), but weaker when generalizing to new environments (average accuracy of 84% for 3.65s of contact, 71% for 1.0s, and 65% for 0.5s).

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Section: A Human Vs Object Recognitionmentioning

confidence: 99%

Data-driven thermal recognition of contact with people and objects

Bhattacharjee

Wade

Chitalia

et al. 2016

2016 IEEE Haptics Symposium (HAPTICS)

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“…Early work into texture detection includes the development of a fingertip for identifying texture [13]. This fingertip combined slip detection, temperature, pressure sensors and vision to train a neural network to distinguish between 20 different textures with 100% accuracy.…”

Section: Introductionmentioning

confidence: 99%

A soft, amorphous skin that can sense and localize textures

Hughes

Correll

2014

2014 IEEE International Conference on Robotics and Automation (ICRA)

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We present a soft, amorphous skin that can sense and localize textures. The skin consists of a series of sensing and computing elements that are networked with their local neighbors and mimic the function of the Pacinian corpuscle in human skin. Each sensor node samples a vibration signal at 1KHz, transforms the signal into the frequency domain, and classifies up to 15 textures using logistic regression. By measuring the power spectrum of the signal and comparing it with its local neighbors, computing elements can then collaboratively estimate the location of the stimulus. The resulting low-bandwidth information, consisting of the texture probability distribution and its location are then routed to a sink anywhere in the skin in a multi-hop fashion. We describe the design, manufacturing, classification, localization and networking algorithms and experimentally validate the proposed approach. In particular, we demonstrate texture classification with 71% accuracy and centimeter accuracy in localization over an area of approximately three square feet using ten networked sensor nodes.

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“…Robot hand research has mainly focused on grasping and object manipulation [1][2][3][4], and many models of hand control have focused on the motor aspect rather than on haptic perception [5,6], although there are some exceptions [7][8][9][10][11][12][13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Haptic Perception with Self-Organizing ANNs and an Anthropomorphic Robot Hand

Johnsson

Balkenius

2010

Journal of Robotics

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We have implemented and compared four biologically motivated self-organizing haptic systems based on proprioception. All systems employ a 12-d.o.f. anthropomorphic robot hand, the LUCS Haptic Hand 3. The four systems differ in the kind of selforganizing neural network used for clustering. For the mapping of the explored objects, one system uses a Self-Organizing Map (SOM), one uses a Growing Cell Structure (GCS), one uses a Growing Cell Structure with Deletion of Neurons (GCS-DN), and one uses a Growing Grid (GG). The systems were trained and tested with 10 different objects of different sizes from two different shape categories. The generalization abilities of the systems were tested with 6 new objects. The systems showed good performance with the objects from the training set as well as in the generalization experiments. Thus the systems could discriminate individual objects, and they clustered the activities into small cylinders, large cylinders, small blocks, and large blocks. Moreover, the selforganizing ANNs were also organized according to size. The GCS-DN system also evolved disconnected networks representing the different clusters in the input space (small cylinders, large cylinders, small blocks, large blocks), and the generalization samples activated neurons in a proper subnetwork in all but one case.

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An approach to integrated tactile perception

Cited by 27 publications

References 14 publications

Data-driven thermal recognition of contact with people and objects

Data-driven thermal recognition of contact with people and objects

A soft, amorphous skin that can sense and localize textures

Haptic Perception with Self-Organizing ANNs and an Anthropomorphic Robot Hand

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