  The potential of large tactile arrays to improve robot perception for safe
operation in human-dominated environments and of high-resolution tactile arrays
to enable human-level dexterous manipulation is well accepted. However, the
increase in the number of tactile sensing elements introduces challenges
including wiring complexity, data acquisition, and data processing. To help
address these challenges, we develop a tactile sensing technique based on
compressed sensing. Compressed sensing simultaneously performs data sampling
and compression with recovery guarantees and has been successfully applied in
computer vision. We use compressed sensing techniques for tactile data
acquisition to reduce hardware complexity and data transmission, while allowing
fast, accurate reconstruction of the full-resolution signal. For our simulated
test array of 4096 taxels, we achieve reconstruction quality equivalent to
measuring all taxel signals independently (the full signal) from just 1024
measurements (the compressed signal) at a rate over 100Hz. We then apply
tactile compressed sensing to the problem of object classification.
Specifically, we perform object classification on the compressed tactile data
based on a method called compressed learning. We obtain up to 98%
classification accuracy, even with a compression ratio of 64:1.
