IEEE Project Abstract

Tactile sensing is becoming an indispensable robotic ability for object recognition and grasping manipulation despite dealing with tactile data as the force distribution over the array sensors continuously changes as a function of time. In this paper, we propose an efficient feature extractor named linear dynamic systems based fuzzy C-means method (LDS) to encode the tactile sequences, both spatially and temporally. To this end, we decompose every input sequence into multiple subsequences, each of which is locally described by a finite-ordered observability matrix of the LDS model. A fuzzy c-means method is then applied to cluster the local LDS descriptors for learning a codebook. Conditioned on the resulting codebook, the global tactile representation is formulated by employing two different frameworks to integrate the subsequences within each tactile sequence, namely, the Vector of locally aggregated descriptor and Bag-of-Word approaches. The effectiveness of the proposed model is verified by a variety of experimental evaluations on five benchmark datasets. Results reveal that our proposed method achieves a higher classification accuracy than the state-of-the-art models with a large margin.