Antonio Elia Forte

Soft robotic actuators enable lightweight and compliant motion, but their fabrication typically relies on silicone molding, 3D printing, or textile lamination—processes that require expensive materials, long production times, or complex fabrication protocols. We introduce a rapid manufacturing strategy using low‐cost thermoplastic pouches that combines vacuum processing and laser cutting. By removing air gaps between layers, this method enables precise sealing and cutting, allowing complex inflatable geometries to be fabricated in under 10 min at a material cost below $0.10 per actuator. Compared to silicone elastomers, the reduced compliance of thermoplastics minimizes deformation losses and channels more energy into effective stiffening. The reliability of the method is verified through material testing and repeatable pressurization experiments, including response times of approximately 0.4s at operating pressure of 50–70kPa. We further use finite element modeling to predict bending behavior, derive geometric rules for programmable deformation, and construct a surrogate model for inverse design of homogeneous and heterogeneous bending actuators. Using this framework, target shapes such as alphabetic letters and spirals are achieved, and functional soft robotic prototypes, including crawlers, swimmers, and soft grippers, are demonstrated. These results position vacuum–laser processing as an accessible and scalable platform for rapid fabrication of adaptive soft robotic systems.

Fluidic circuits have shown significant promise in enabling complex functionality in soft robots with a minimal number of input signals. However, implementing complex behaviors typically involves numerous specialized components, resulting in intricate and nonversatile circuits. To address this challenge, a multifunctional fluidic unit designed to operate flexibly as a valve, sensor, or actuator is introduced. This unit provides an extensive design space that allows precise tuning to achieve the desired functionality. In particular, one configuration integrates all three functions simultaneously, resulting in a self‐sensing oscillating actuator. By assembling multiple units—each customized for specific roles—complex robotic behaviors can be realized. The versatility and effectiveness of this modular approach are demonstrated by creating several robotic systems, including a controlled shaker, a multimodal hopper, and a crawler capable of sensing environmental boundaries. Furthermore, when these units are mechanically coupled via a shared body, it exhibit emergent passive behaviors, such as self‐synchronization—a behavior that is elucidated with a Kuramoto model of networks of oscillators. This study highlights the potential of multifunctionality as a powerful and efficient strategy for realizing embodied intelligence in fluidic robotic systems.

Active metamaterials are engineered structures that possess novel properties that can be changed after the point of manufacture. Their novel properties arise predominantly from their physical structure, as opposed to their chemical composition and can be changed through means such as direct energy addition into wave paths, or physically changing/morphing the structure in response to both a user or environmental input. Active metamaterials are currently of wide interest to the physics community and encompass a range of sub-domains in applied physics (e.g. photonic, microwave, acoustic, mechanical, etc.). They possess the potential to provide solutions that are more suitable to specific applications, or which allow novel properties to be produced which cannot be achieved with passive metamaterials, such as time-varying or gain enhancement effects. They have the potential to help solve some of the important current and future problems faced by the advancement of modern society, such as achieving net-zero, sustainability, healthcare and equality goals. Despite their huge potential, the added complexity of their design and operation, compared to passive metamaterials creates challenges to the advancement of the field, particularly beyond theoretical and lab-based experiments. This roadmap brings together experts in all types of active metamaterials and across a wide range of areas of applied physics. The objective is to provide an overview of the current state of the art and the associated current/future challenges, with the hope that the required advances identified create a roadmap for the future advancement and application of this field.

Entanglement between objects offers a powerful route to programmable collective behavior in granular media. Here, the concept of entangled granular metamaterials—clusters of geometrically complex grains is introduced, whose interlocking interactions drive emergent and tunable properties. The geometric features that promote entanglement are identified, distinguished from simple contact interactions, and the strength of entanglement is quantified through disentanglement dynamics. By varying the number and shape of grains, the degree of interlocking within the cluster is modulated, and its utility is demonstrated in robotic handling tasks. Using a ferromagnetic grain assembly manipulated by an electromagnet, the metamaterial is deployed onto a second, non‐ferromagnetic cluster of complex targets. The grains penetrate and entangle with the targets, enabling robust collective picking and even selective retrieval of individual objects with intricate geometry. Finally, strategies for controlled separation of the grains from the targets are outlined, establishing a foundation for programmable entanglement in matter manipulation.

Electronic‐free controls have recently emerged as one of the main topics in soft robotics. However, electronic‐free fluidic circuits still lack controllability and reconfigurability to achieve different functions. Here, reconfigurable pneumatic valves that widen the design space of fluidic circuits are presented. The significance of two parameters on the valve's operation: a pre‐defined manufacturing parameter that sets the initial operational range of the valve, and a second, on‐the‐fly modifiable geometric parameter that shifts the behavior of the valve during operation is shown. It is demonstrated that equipping the valve with these reconfigurable features enables the tuning of fluidic oscillatory circuits as illustrated by two examples: a frequency‐controlled relaxation oscillator and a reconfigurable ring oscillator. The relaxation oscillator is employed to control the actuation frequency of a soft hopper, which is able to achieve 80 to 125 hops min⁻¹ and a hopping speed ranging from ≈1 to ≈1.185 BL s⁻¹. Additionally, the reconfigurable ring oscillator is used to demonstrate how each output frequency can be controlled independently, via a soft robotic crawler that can navigate in three directions, and a volume‐controlled fluidic pump able to achieve mixing of solutions in environments where electronic components cannot operate.

When objects are packed in a cluster, physical interactions are unavoidable. Such interactions emerge because of the objects geometric features; some of these features promote entanglement, while others create repulsion. When entanglement occurs, the cluster exhibits a global, complex behaviour, which arises from the stochastic interactions between objects. We hereby refer to such a cluster as an entangled granular metamaterial. We investigate the geometrical features of the objects which make up the cluster, henceforth referred to as grains, that maximise entanglement. We hypothesise that a cluster composed from grains with high propensity to tangle, will also show propensity to interact with a second cluster of tangled objects. To demonstrate this, we use the entangled granular metamaterials to perform complex robotic picking tasks, where conventional grippers struggle. We employ an electromagnet to attract the metamaterial (ferromagnetic) and drop it onto a second cluster of objects (targets, non-ferromagnetic). When the electromagnet is re-activated, the entanglement ensures that both the metamaterial and the targets are picked, with varying degrees of physical engagement that strongly depend on geometric features. Interestingly, although the metamaterials structural arrangement is random, it creates repeatable and consistent interactions with a second tangled media, enabling robust picking of the latter.

Across fields of science, researchers have increasingly focused on designing soft devices that can shape‐morph to achieve functionality. However, identifying a rest shape that leads to a target 3D shape upon actuation is a non‐trivial task that involves inverse design capabilities. In this study, a simple and efficient platform is presented to design pre‐programmed 3D shapes starting from 2D planar composite membranes. By training neural networks with a small set of finite element simulations, the authors are able to obtain both the optimal design for a pixelated 2D elastomeric membrane and the inflation pressure required for it to morph into a target shape. The proposed method has potential to be employed at multiple scales and for different applications. As an example, it is shown how these inversely designed membranes can be used for mechanotherapy applications, by stimulating certain areas while avoiding prescribed locations.

Humans are highly skilled at analysing complex acoustic scenes. The segregation of different acoustic streams and the formation of corresponding neural representations is mostly attributed to the auditory cortex. Decoding of selective attention from neuroimaging has therefore focussed on cortical responses to sound. However, the auditory brainstem response to speech is modulated by selective attention as well, as recently shown through measuring the brainstem’s response to running speech. Although the response of the auditory brainstem has a smaller magnitude than that of the auditory cortex, it occurs at much higher frequencies and therefore has a higher information rate. Here we develop statistical models for extracting the brainstem response from multi-channel scalp recordings and for analysing the attentional modulation according to the focus of attention. We demonstrate that the attentional modulation of the brainstem response to speech can be employed to decode the attentional focus of a listener from short measurements of ten seconds or less in duration. The decoding remains accurate when obtained from three EEG channels only. We further show how out-of-the-box decoding that employs subject-independent models, as well as decoding that is independent of the specific attended speaker is capable of achieving similar accuracy. These results open up new avenues for investigating the neural mechanisms for selective attention in the brainstem and for developing efficient auditory brain-computer interfaces.