  Tactile sensing in soft robots remains particularly challenging because of
the coupling between contact and deformation information which the sensor is
subject to during actuation and interaction with the environment. This often
results in severe interference and makes disentangling tactile sensing and
geometric deformation difficult. To address this problem, this paper proposes a
soft capacitive e-skin with a sparse electrode distribution and deep learning
for information decoupling. Our approach successfully separates tactile sensing
from geometric deformation, enabling touch recognition on a soft pneumatic
actuator subject to both internal (actuation) and external (manual handling)
forces. Using a multi-layer perceptron, the proposed e-skin achieves 99.88\%
accuracy in touch recognition across a range of deformations. When complemented
with prior knowledge, a transformer-based architecture effectively tracks the
deformation of the soft actuator. The average distance error in positional
reconstruction of the manipulator is as low as 2.905$\pm$2.207 mm, even under
operative conditions with different inflation states and physical contacts
which lead to additional signal variations and consequently interfere with
deformation tracking. These findings represent a tangible way forward in the
development of e-skins that can endow soft robots with proprioception and
exteroception.
