A Machine Learning Approach to Contact Localization in Variable Density Three-Dimensional Tactile Artificial Skin

Estimating the location of contact is a primary function of artificial tactile sensing apparatuses that perceive the environment through touch. Existing contact
localization methods use flat geometry and uniform sensor distributions as a simplifying assumption, limiting their ability to be used on 3D surfaces with variable
density sensing arrays. This paper studies contact localization on an artificial skin
embedded with mutual capacitance tactile sensors, arranged non-uniformly in an
unknown distribution along a semi-conical 3D geometry. A fully connected neural
network is trained to localize the touching points on the embedded tactile sensors.
The studied online model achieves a localization error of 5.7 ± 3.0 mm. This
research contributes a versatile tool and robust solution for contact localization that
is ambiguous in shape and internal sensor distribution.

References

Murray, M., Zhang, Y., Kohlbrenner, C., Escobedo, C., Dunnington, T., Stevenson, N., Correll, N. and Roncone, A., A Machine Learning Approach to Contact Localization in Variable Density Three-Dimensional Tactile Artificial Skin. In 2nd NeurIPS Workshop on Touch Processing: From Data to Knowledge.