  In this work, we use MEMS microphones as vibration sensors to simultaneously
classify texture and estimate contact position and velocity. Vibration sensors
are an important facet of both human and robotic tactile sensing, providing
fast detection of contact and onset of slip. Microphones are an attractive
option for implementing vibration sensing as they offer a fast response and can
be sampled quickly, are affordable, and occupy a very small footprint. Our
prototype sensor uses only a sparse array (8-9 mm spacing) of distributed MEMS
microphones (<$1, 3.76 x 2.95 x 1.10 mm) embedded under an elastomer. We use
transformer-based architectures for data analysis, taking advantage of the
microphones' high sampling rate to run our models on time-series data as
opposed to individual snapshots. This approach allows us to obtain 77.3%
average accuracy on 4-class texture classification (84.2% when excluding the
slowest drag velocity), 1.8 mm mean error on contact localization, and 5.6 mm/s
mean error on contact velocity. We show that the learned texture and
localization models are robust to varying velocity and generalize to unseen
velocities. We also report that our sensor provides fast contact detection, an
important advantage of fast transducers. This investigation illustrates the
capabilities one can achieve with a MEMS microphone array alone, leaving
valuable sensor real estate available for integration with complementary
tactile sensing modalities.
