  Tactile and textile skin technologies have become increasingly important for
enhancing human-robot interaction and allowing robots to adapt to different
environments. Despite notable advancements, there are ongoing challenges in
skin signal processing, particularly in achieving both accuracy and speed in
dynamic touch sensing. This paper introduces a new framework that poses the
touch sensing problem as an estimation problem of resistive sensory arrays.
Utilizing a Regularized Least Squares objective function which estimates the
resistance distribution of the skin. We enhance the touch sensing accuracy and
mitigate the ghosting effects, where false or misleading touches may be
registered. Furthermore, our study presents a streamlined skin design that
simplifies manufacturing processes without sacrificing performance.
Experimental outcomes substantiate the effectiveness of our method, showing
26.9% improvement in multi-touch force-sensing accuracy for the tactile skin.
