  Artificial skin with the sense of touch can support robots to interact with
the surrounding environment efficiently and accomplish complex tasks.
Traditional multi-layered artificial skins require complex manufacturing
processes which can result in high cost as well as limitations on the material
and size of the skin. In this paper, we demonstrate a machine learning based
approach to predict positions of point loads using the most direct response as
input signal: strain distribution. Starting with the simplest problem,
predicting the position of a single point load acting on a flat surface, an ML
model is developed, trained, and tested. Accurate predictions are obtained from
the ML model, parameters that affect the accuracy are discussed, and validation
tests are performed. After that, the ML model is modified to solve
multi-objective prediction problems: predicting positions and magnitudes of
multiple point loads. In the end, the ML model is upgraded to a 2-step model to
predict the position of a point load acting on a deformable surface. The
demonstrated approach enables a normal untreated surface to feel a touch no
matter what the surface is made of or how large or small the size of the
surface is. Therefore, we believe this ML-based load position prediction
approach could be a promising tool for applications such as flexible touch
screens, smart skin for robots, and micro touch sensors.
