Embodying Intelligence into Mechanical Metamaterials via Reservoir Computing

TL;DR

This paper demonstrates how mechanical metamaterials can be used as physical neural networks through reservoir computing, enabling them to sense, process, and compute environmental vibrations with minimal digital intervention.

Contribution

It introduces a novel metamaterial reservoir with nonlinear contact units functioning as ReLU activations, showcasing embodied intelligence for diverse sensing and processing tasks.

Findings

Metamaterial reservoir effectively performs classification and proprioception tasks.

Nonlinearity significantly improves task performance over linear metamaterials.

Frequency content analysis reveals the mechanism of information separation.

Abstract

This study harnesses the embodied intelligence of mechanical metamaterials to sense and process environmental vibrations with minimal digital computation. Using physical reservoir computing (PRC), we turn the metamaterial and its nonlinear dynamics into a physical neural network that nonlinearly transforms the input vibrations and uses a simple linear training to compute a range of tasks. We introduce a novel metamaterial reservoir composed of a network of unit cells with contact nonlinearities that are the physical equivalent of leaky rectified linear unit (ReLU) activation functions. We experimentally show that the metamaterial reservoir can compute two classes of tasks: independent tasks, such as benchmark functions, and embodied tasks, such as proprioception, which we introduce to describe tasks coupled to the structure's dynamics. By comparing against a linear metamaterial, we demonstrate that nonlinearity is critical for high task performance, and we show that the metamaterial is robust to inputs of varying complexity. Through dimensionality reduction, we uncover the governing information separation mechanism and show that the metamaterial separates the input vibrations into new frequency content spatially distributed across the sensor readouts. We then confirm that frequency content is a key indicator of task performance by conducting an optimal sensor selection study using a frequency-based greedy algorithm. Finally, we demonstrate that a metamaterial's generalized performance for different tasks can be quantified using the memory vs. nonlinearity subspace, providing a design tool for other reservoir abstractions. These results establish the embodied intelligence of mechanical metamaterials and provide a path for sense-assess-response in intelligent systems.