  The importance of haptic in-sensor computing devices has been increasing. In
this study, we successfully fabricated a haptic sensor with a hierarchical
structure via the sacrificial template method, using carbon
nanotubes-polydimethylsiloxane (CNTs-PDMS) nanocomposites for in-sensor
computing applications. The CNTs-PDMS nanocomposite sensors, with different
sensitivities, were obtained by varying the amount of CNTs. We transformed the
input stimuli into higher-dimensional information, enabling a new path for the
CNTs-PDMS nanocomposite application, which was implemented on a robotic hand as
an in-sensor computing device by applying a reservoir computing paradigm. The
nonlinear output data obtained from the sensors were trained using linear
regression and used to classify nine different objects used in everyday life
with an object recognition accuracy of >80 % for each object. This approach
could enable tactile sensation in robots while reducing the computational cost.
