  In this paper we focus on analyzing the thermal modality of tactile sensing
for material recognition using a large materials database. Many factors affect
thermal recognition performance, including sensor noise, the initial
temperatures of the sensor and the object, the thermal effusivities of the
materials, and the duration of contact. To analyze the influence of these
factors on thermal recognition, we used a semi-infinite solid based thermal
model to simulate heat-transfer data from all the materials in the CES Edupack
Level-1 database. We used support-vector machines (SVMs) to predict F1 scores
for binary material recognition for 2346 material pairs. We also collected data
using a real robot equipped with a thermal sensor and analyzed its material
recognition performance on 66 real-world material pairs. Additionally, we
analyzed the performance when the models were trained on the simulated data and
tested on the real-robot data. Our models predicted the material recognition
performance with a 0.980 F1 score for the simulated data, a 0.994 F1 score for
real-world data with constant initial sensor temperatures, a 0.966 F1 score for
real-world data with varied initial sensor temperatures, and a 0.815 F1 score
for sim-to-real transfer. Finally, we present some guidelines on sensor design
and parameter choice for thermal recognition based on the insights gained from
these results that would hopefully enable robotics researchers to use this
less-explored tactile sensing modality more effectively during physical
human-robot and robot-object interactions. We release our simulated and
real-robot datasets for further use by the robotics community.
