  The missing signal caused by the objects being occluded or an unstable sensor
is a common challenge during data collection. Such missing signals will
adversely affect the results obtained from the data, and this issue is observed
more frequently in robotic tactile perception. In tactile perception, due to
the limited working space and the dynamic environment, the contact between the
tactile sensor and the object is frequently insufficient and unstable, which
causes the partial loss of signals, thus leading to incomplete tactile data.
The tactile data will therefore contain fewer tactile cues with low information
density. In this paper, we propose a tactile representation learning method,
named TacMAE, based on Masked Autoencoder to address the problem of incomplete
tactile data in tactile perception. In our framework, a portion of the tactile
image is masked out to simulate the missing contact region. By reconstructing
the missing signals in the tactile image, the trained model can achieve a
high-level understanding of surface geometry and tactile properties from
limited tactile cues. The experimental results of tactile texture recognition
show that our proposed TacMAE can achieve a high recognition accuracy of 71.4%
in the zero-shot transfer and 85.8% after fine-tuning, which are 15.2% and 8.2%
higher than the results without using masked modeling. The extensive
experiments on YCB objects demonstrate the knowledge transferability of our
proposed method and the potential to improve efficiency in tactile exploration.
