  Robots need to exploit high-quality information on grasped objects to
interact with the physical environment. Haptic data can therefore be used for
supplementing the visual modality. This paper investigates the use of
Convolutional Neural Networks (CNN) and Long-Short Term Memory (LSTM) neural
network architectures for object classification on Spatio-temporal tactile
grasping data. Furthermore, we compared these methods using data from two
different fingertip sensors (namely the BioTac SP and WTS-FT) in the same
physical setup, allowing for a realistic comparison across methods and sensors
for the same tactile object classification dataset. Additionally, we propose a
way to create more training examples from the recorded data. The results show
that the proposed method improves the maximum accuracy from 82.4% (BioTac SP
fingertips) and 90.7% (WTS-FT fingertips) with complete time-series data to
about 94% for both sensor types.
