Constructing Contact Estimation Models for Barometric Tactile Sensors

Abstract

Barometric tactile sensors present a cheap and customizable method for adding tactile sensing to robotic platforms. These sensors consist of commercially available MEMS barometers embedded in an elastomer. However, as the sensing surface and elastomer volume increase in complexity, time-dependent material dynamics reduce sensing accuracy. We present a collection of inference and usage recommendations towards mitigating these dynamics and improving sensor force and localization resolution. Using two custom, curved, barometric tactile sensors as case studies, we demonstrate that a new data collection regime alone can improve normal force predictions by 30.4% compared to prior work. We further introduce a Binned-RNN inference architecture and demonstrate its efficacy through select ablations. Small enough to run on the sensor’s integrated microcontroller at 100Hz, we find our model achieves a minimum spatial resolution of 0.86 mm on an ellipsoid tactile sensor. Finally, we demonstrate the robustness of these sensing capabilities through freeform contact and controlled object rolling.