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Physical reservoir computing (RC) represents a computational framework that exploits information-processing capabilities of programmable matter, allowing the realization of energy-efficient neuromorphic hardware with fast learning and low training cost. Despite self-organized memristive networks have been demonstrated as physical reservoir able to extract relevant features from spatiotemporal input signals, multiterminal nanonetworks open the possibility for novel strategies of computing implementation. In this work, we report on implementation strategies of in materia RC with self-assembled memristive networks. Besides showing the spatiotemporal information processing capabilities of self-organized nanowire networks, we show through simulations that the emergent collective dynamics allows unconventional implementations of RC where the same electrodes can be used as both reservoir inputs and outputs. By comparing different implementation strategies on a digit recognition task, simulations show that the unconventional implementation allows a reduction of the hardware complexity without limiting computing capabilities, thus providing new insights for taking full advantage of in materia computing toward a rational design of neuromorphic systems.

Soft materials are increasingly utilized for various purposes in many engineering applications. These materials have been shown to perform a number of functions that were previously difficult to implement using rigid materials. Here, we argue that the diverse dynamics generated by actuating soft materials can be effectively used for machine learning purposes. This is demonstrated using a soft silicone arm through a technique of multiplexing, which enables the rich transient dynamics of the soft materials to be fully exploited as a computational resource. The computational performance of the soft silicone arm is examined through two standard benchmark tasks. Results show that the soft arm compares well to or even outperforms conventional machine learning techniques under multiple conditions. We then demonstrate that this system can be used for the sensory time series prediction problem for the soft arm itself, which suggests its immediate applicability to a real-world machine learning problem. Our approach, on the one hand, represents a radical departure from traditional computational methods, whereas on the other hand, it fits nicely into a more general perspective of computation by way of exploiting the properties of physical materials in the real world.

We propose a reservoir computing device utilizing spin waves that propagate in a garnet film equipped with multiple input/output electrodes. In recent years, reservoir computing has been expected to realize energy-efficient and/or high-speed machine learning. Our proposed device enhances such significant merits in a hardware approach. It utilizes the nonlinear interference of history-dependent asymmetrically propagating spin waves excited by the magneto-electric effect. First, we investigate a feasible device structure with practical physical parameters in micromagnetic numerical analysis, and show the detailed characteristics of the forward volume magnetostatic spin waves. Then, we demonstrate high generalization ability in the estimation of input-signal parameters performed by the spin-wave-based reservoir computing. We find that the hysteresis characteristics of the spin waves propagating asymmetrically with respect to excitation points, as well as the nonlinear interference, works advantageously to realize high diversity in the time-sequential signals in high-dimensional information space, which has the highest significance for effective learning in reservoir computing. The spin wave device is highly promising for next-generation machine-learning electronics.

Flapping‐wing unmanned aerial vehicles have potential advantages, such as consuming lower energy by leveraging the force of wind. Since the flapping movements of the soft wings contain information about the wind, measuring the movement of each part of the wings allows these vehicles to distinguish the direction of the wind. To confirm this prediction, herein, the detection of wind flow from the flapping‐wing motion of a bird robot using an integrated flexible strain sensor on its wing and a physical reservoir computing analysis is presented. In the presence of different wind directions, the movement of the flapping‐wings is measured using flexible strain sensors, and the current wind direction is detected by capitalizing on the intrinsic wing dynamics. As a result, it is found that the detection accuracy using our embedded flexible strain sensors is significantly high, showing a similar level of accuracy with a high‐speed camera recorded from the fixed position in the environment. The results indicate that flapping‐wing unmanned aerial vehicles can recognize wind direction by exploiting the natural dynamics of their wings.

Robots have proven very useful in automating industrial processes. Their rigid components and powerful actuators, however, render them unsafe or unfit to work in normal human environments such as schools or hospitals. Robots made of compliant, softer materials may offer a valid alternative. Yet, the dynamics of these compliant robots are much more complicated compared to normal rigid robots of which all components can be accurately controlled. It is often claimed that, by using the concept of morphological computation, the dynamical complexity can become a strength. On the one hand, the use of flexible materials can lead to higher power efficiency and more fluent and robust motions. On the other hand, using embodiment in a closed-loop controller, part of the control task itself can be outsourced to the body dynamics. This can significantly simplify the additional resources required for locomotion control. To this goal, a first step consists in an exploration of the trade-offs between morphology, efficiency of locomotion, and the ability of a mechanical body to serve as a computational resource. In this work, we use a detailed dynamical model of a Mass–Spring–Damper (MSD) network to study these trade-offs. We first investigate the influence of the network size and compliance on locomotion quality and energy efficiency by optimizing an external open-loop controller using evolutionary algorithms. We find that larger networks can lead to more stable gaits and that the system’s optimal compliance to maximize the traveled distance is directly linked to the desired frequency of locomotion. In the last set of experiments, the suitability of MSD bodies for being used in a closed loop is also investigated. Since maximally efficient actuator signals are clearly related to the natural body dynamics, in a sense, the body is tailored for the task of contributing to its own control. Using the same simulation platform, we therefore study how the network states can be successfully used to create a feedback signal and how its accuracy is linked to the body size.