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In this paper, we experimentally examine the cognitive capability of asimple, paper-based Miura-ori -- using the physical reservoir computingframework -- to achieve different information perception tasks. The bodydynamics of Miura-ori (aka. its vertices displacements), which is excited by asimple harmonic base excitation, can be exploited as the reservoir computingresource. By recording these dynamics with a high-resolution camera and imageprocessing program and then using linear regression for training, we show thatthe origami reservoir has sufficient computing capacity to estimate the weightand position of a payload. It can also recognize the input frequency andmagnitude patterns. Furthermore, multitasking is achievable by simultaneouslyapplying two targeted functions to the same reservoir state matrix. Therefore,we demonstrate that Miura-ori can assess the dynamic interactions between itsbody and ambient environment to extract meaningful information -- anintelligent behavior in the mechanical domain. Given that Miura-ori has beenwidely used to construct deployable structures, lightweight materials, andcompliant robots, enabling such information perception tasks can add a newdimension to the functionality of such a versatile structure.

Building Intelligence in the Mechanical Domain -- Harvesting the Reservoir Computing Power in Origami to Achieve Information Perception Tasks