  In this paper, we experimentally examine the cognitive capability of a
simple, paper-based Miura-ori -- using the physical reservoir computing
framework -- to achieve different information perception tasks. The body
dynamics of Miura-ori (aka. its vertices displacements), which is excited by a
simple harmonic base excitation, can be exploited as the reservoir computing
resource. By recording these dynamics with a high-resolution camera and image
processing program and then using linear regression for training, we show that
the origami reservoir has sufficient computing capacity to estimate the weight
and position of a payload. It can also recognize the input frequency and
magnitude patterns. Furthermore, multitasking is achievable by simultaneously
applying two targeted functions to the same reservoir state matrix. Therefore,
we demonstrate that Miura-ori can assess the dynamic interactions between its
body and ambient environment to extract meaningful information -- an
intelligent behavior in the mechanical domain. Given that Miura-ori has been
widely used to construct deployable structures, lightweight materials, and
compliant robots, enabling such information perception tasks can add a new
dimension to the functionality of such a versatile structure.
