Compressed Learning for Tactile Object Recognition

Abstract

We present a framework for object recognition using robotic skins with embedded arrays of tactile sensing elements. Our approach is based on theoretical foundations in compressed sensing and compressed learning. In our framework, tactile data is compressed during acquisition, potentially in-hardware, and we perform recognition directly on the compressed data. This dimensionality reduction allows for accurate recognition with a small number of training samples, reducing the time and computational effort needed to train the classifier. In addition, for tasks where the full-resolution tactile array signal is needed, it can be recovered efficiently from the compressed signal. We evaluate our method using data generated from a tactile array simulator. We also demonstrate the effectiveness of our framework in recognizing surface roughness using data from a physical system. Evaluation results show our approach achieves high recognition accuracy, even with a compression ratio of 64:1.