Building Intelligence in the Mechanical Domain—Harvesting the Reservoir Computing Power in Origami to Achieve Information Perception Tasks - Jun Wang; Suyi Li (Advanced Intelligent Systems, 2023)
Herein, the cognitive capability of a simple, paper‐based Miura‐ori—using the physical reservoir computing framework—is experimentally examined 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, it is shown 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, it is demonstrated 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.