Analyzing Material Recognition Performance of Thermal Tactile Sensing using a Large Materials Database and a Real Robot

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.