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Distributed sensor arrays capable of detecting multiple spatially distributedstimuli are considered an important element in the realisation of exteroceptiveand proprioceptive soft robots. This paper expands upon the previouslypresented idea of decoupling the measurements of pressure and location of alocal indentation from global deformation, using the overall stretchexperienced by a soft capacitive e-skin. We employed machine learning methodsto decouple and predict these highly coupled deformation stimuli, collectingdata from a soft sensor e-skin which was then fed to a machine learning systemcomprising of linear regressor, gaussian process regressor, SVM and randomforest classifier for stretch, force, detection and localisation respectively.We also studied how the localisation and forces are affected when two forcesare applied simultaneously. Soft sensor arrays aided by appropriately chosenmachine learning techniques can pave the way to e-skins capable of decipheringmulti-modal stimuli in soft robots.

Learning Decoupled Multi-touch Force Estimation, Localization and Stretch for Soft Capacitive E-skin