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.
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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