Title:
A Model that Predicts the Material Recognition Performance of Thermal Tactile Sensing
A Model that Predicts the Material Recognition Performance of Thermal Tactile Sensing
Author(s)
Bhattacharjee, Tapomayukh
Bai, Haoping
Chen, Haofeng
Kemp, Charles C.
Bai, Haoping
Chen, Haofeng
Kemp, Charles C.
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Abstract
Tactile sensing can enable a robot to infer properties of its surroundings, such as the material of an object. Heat transfer
based sensing can be used for material recognition due to differences in the thermal properties of materials. While datadriven
methods have shown promise for this recognition problem, many factors can influence performance, including
sensor noise, the initial temperatures of the sensor and the object, the thermal effusivities of the materials, and the
duration of contact. We present a physics-based mathematical model that predicts material recognition performance
given these factors. Our model uses semi-infinite solids and a statistical method to calculate an F1 score for the binary
material recognition. We evaluated our method using simulated contact with 69 materials and data collected by a
real robot with 12 materials. Our model predicted the material recognition performance of support vector machine
(SVM) with 96% accuracy for the simulated data, with 92% accuracy for real-world data with constant initial sensor
temperatures, and with 91% accuracy for real-world data with varied initial sensor temperatures. Using our model, we
also provide insight into the roles of various factors on recognition performance, such as the temperature difference
between the sensor and the object. Overall, our results suggest that our model could be used to help design better
thermal sensors for robots and enable robots to use them more effectively.
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Date Issued
2016
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Text
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Pre-print