A soft matter computer for soft robots

Mechanical Neural Networks with Explicit and Robust Neurons

Mechanical computing provides an information processing method to realize sensing-analyzing-actuation integrated mechanical intelligence and, when combined with neural networks, can be more efficient for data-rich cognitive tasks. The requirement of solving implicit and usually nonlinear equilibrium equations of motion in training mechanical neural networks makes computation challenging and costly. Here, an explicit mechanical neuron is developed of which the response can be directly determined without the need of solving equilibrium equations. A training method is proposed to ensure the robustness of the neuron, i.e., insensitivity to defects and perturbations. The explicitness and robustness of the neurons facilitate the assembly of various network structures. Two exemplified networks, a robust mechanical convolutional neural network and a mechanical recurrent neural network with long short-term memory capabilities for associative learning, are experimentally demonstrated. The introduction of the explicit and robust mechanical neuron streamlines the design of mechanical neural networks fulfilling robotic matter with a level of intelligence.

In-memory mechanical computing

Mechanical computing requires matter to adapt behavior according to retained knowledge, often through integrated sensing, actuation, and control of deformation. However, inefficient access to mechanical memory and signal propagation limit mechanical computing modules. To overcome this, we developed an in-memory mechanical computing architecture where computing occurs within the interaction network of mechanical memory units. Interactions embedded within data read-write interfaces provided function-complete and neuromorphic computing while reducing data traffic and simplifying data exchange. A reprogrammable mechanical binary neural network and a mechanical self-learning perceptron were demonstrated experimentally in 3D printed mechanical computers, as were all 16 logic gates and truth-table entries that are possible with two inputs and one output. The in-memory mechanical computing architecture enables the design and fabrication of intelligent mechanical systems.

Autonomous hydrogel locomotion regulated by light and electric fields

Autonomous robotic functions in materials beyond simple stimulus-response actuation require the development of functional soft matter that can complete well-organized tasks without step-by-step control. We report the design of photo- and electroactivated hydrogels that can capture and deliver cargo, avoid obstacles, and return without external, stepwise control. By incorporating two spiropyran monomers with different chemical substituents in the hydrogel, we created chemically random networks that enabled photoregulated charge reversal and autonomous behaviors under a constant electric field. In addition, using perturbations in the electric field induced by a dielectric inhomogeneity, the hydrogel could be attracted to high dielectric constant materials and autonomously bypasses the low dielectric constant materials under the guidance of the electric field vector. The photo- and electroactive hydrogels investigated here can autonomously perform tasks using constant external stimuli, an encouraging observation for the potential development of molecularly designed intelligent robotic materials.

The morphological paradigm in robotics

In the paper, we are going to show how robotics is undergoing a shift in a bionic direction after a period of emphasis on artificial intelligence and increasing computational efficiency, which included isolation and extreme specialization. We assemble these new developments under the label of the morphological paradigm. The change in its paradigms and the development of alternatives to the principles that dominated robotics for a long time contains a more general epistemological significance. The role of body, material, environment, interaction and the paradigmatic status of biological and evolutionary systems for the principles of control are crucial here. Our focus will be on the introduction of the morphological paradigm in a new type of robotics and to contrast the interests behind this development with the interests shaping former models. The article aims to give a clear account of the changes in principles of orientation and control as well as concluding general observation in terms of historical epistemology, suggesting further political-epistemological analysis.

A Soft Reconfigurable Circulator Enabled by Magnetic Liquid Metal Droplet for Multifunctional Control of Soft Robots

Integrated control circuits with multiple computation functions are essential for soft robots to achieve diverse complex real tasks. However, designing compliant yet simple circuits to embed multiple computation functions in soft electronic systems above the centimeter scale is still a tough challenge. Herein, utilizing smooth cyclic motions of magnetic liquid metal droplets (MLMD) in specially designed and surface-modified circulating channels, a soft reconfigurable circulator (SRC) consisting of three simple and reconfigurable basic modules is described. Through these modules, MLMD can utilize their conductivity and extreme deformation capabilities to transfer their simple cyclic motions as input signals to programmable electrical output signals carrying computing information. The obtained SRCs make it possible for soft robots to perform complex computing tasks, such as logic, programming, and self-adaptive control (a combination of programming and feedback control). Following, a digital logic-based grasping function diagnosis, a locomotion reprogrammable soft car, and a self-adaptive control-based soft sorting gripper are demonstrated to verify SRCs' capabilities. The unique attributes of MLMD allow complex computations based on simple configurations and inputs, which provide new ways to enhance soft robots' computing capabilities.

Untethered Soft Microrobots with Adaptive Logic Gates

Integrating adaptative logic computation directly into soft microrobots is imperative for the next generation of intelligent soft microrobots as well as for the smart materials to move beyond stimulus-response relationships and toward the intelligent behaviors seen in biological systems. Acquiring adaptivity is coveted for soft microrobots that can adapt to implement different works and respond to different environments either passively or actively through human intervention like biological systems. Here, a novel and simple strategy for constructing untethered soft microrobots based on stimuli-responsive hydrogels that can switch logic gates according to the surrounding stimuli of environment is introduced. Different basic logic gates and combinational logic gates are integrated into a microrobot via a straightforward method. Importantly, two kinds of soft microrobots with adaptive logic gates are designed and fabricated, which can smartly switch logic operation between AND gate and OR gate under different surrounding environmental stimuli. Furthermore, a same magnetic microrobot with adaptive logic gate is used to capture and release the specified objects through the change of the surrounding environmental stimuli based on AND or OR logic gate. This work contributes an innovative strategy to integrate computation into small-scale untethered soft robots with adaptive logic gates.

Origami-based integration of robots that sense, decide, and respond

Origami-inspired engineering has enabled intelligent materials and structures to process and react to environmental stimuli. However, it is challenging to achieve complete sense-decide-act loops in origami materials for autonomous interaction with environments, mainly due to the lack of information processing units that can interface with sensing and actuation. Here, we introduce an integrated origami-based process to create autonomous robots by embedding sensing, computing, and actuating in compliant, conductive materials. By combining flexible bistable mechanisms and conductive thermal artificial muscles, we realize origami multiplexed switches and configure them to generate digital logic gates, memory bits, and thus integrated autonomous origami robots. We demonstrate with a flytrap-inspired robot that captures 'living prey', an untethered crawler that avoids obstacles, and a wheeled vehicle that locomotes with reprogrammable trajectories. Our method provides routes to achieve autonomy for origami robots through tight functional integration in compliant, conductive materials.

Soft robotics towards sustainable development goals and climate actions

Soft robotics technology can aid in achieving United Nations’ Sustainable Development Goals (SDGs) and the Paris Climate Agreement through development of autonomous, environmentally responsible machines powered by renewable energy. By utilizing soft robotics, we can mitigate the detrimental effects of climate change on human society and the natural world through fostering adaptation, restoration, and remediation. Moreover, the implementation of soft robotics can lead to groundbreaking discoveries in material science, biology, control systems, energy efficiency, and sustainable manufacturing processes. However, to achieve these goals, we need further improvements in understanding biological principles at the basis of embodied and physical intelligence, environment-friendly materials, and energy-saving strategies to design and manufacture self-piloting and field-ready soft robots. This paper provides insights on how soft robotics can address the pressing issue of environmental sustainability. Sustainable manufacturing of soft robots at a large scale, exploring the potential of biodegradable and bioinspired materials, and integrating onboard renewable energy sources to promote autonomy and intelligence are some of the urgent challenges of this field that we discuss in this paper. Specifically, we will present field-ready soft robots that address targeted productive applications in urban farming, healthcare, land and ocean preservation, disaster remediation, and clean and affordable energy, thus supporting some of the SDGs. By embracing soft robotics as a solution, we can concretely support economic growth and sustainable industry, drive solutions for environment protection and clean energy, and improve overall health and well-being.

Human–Machine Interaction through Advanced Haptic Sensors: A Piezoelectric Sensory Glove with Edge Machine Learning for Gesture and Object Recognition

Human–machine interaction (HMI) refers to systems enabling communication between machines and humans. Systems for human–machine interfaces have advanced significantly in terms of materials, device design, and production methods. Energy supply units, logic circuits, sensors, and data storage units must be flexible, stretchable, undetectable, biocompatible, and self-healing to act as human–machine interfaces. This paper discusses the technologies for providing different haptic feedback of different natures. Notably, the physiological mechanisms behind touch perception are reported, along with a classification of the main haptic interfaces. Afterward, a comprehensive overview of wearable haptic interfaces is presented, comparing them in terms of cost, the number of integrated actuators and sensors, their main haptic feedback typology, and their future application. Additionally, a review of sensing systems that use haptic feedback technologies—specifically, smart gloves—is given by going through their fundamental technological specifications and key design requirements. Furthermore, useful insights related to the design of the next-generation HMI devices are reported. Lastly, a novel smart glove based on thin and conformable AlN (aluminum nitride) piezoelectric sensors is demonstrated. Specifically, the device acquires and processes the signal from the piezo sensors to classify performed gestures through an onboard machine learning (ML) algorithm. Then, the design and testing of the electronic conditioning section of AlN-based sensors integrated into the smart glove are shown. Finally, the architecture of a wearable visual-tactile recognition system is presented, combining visual data acquired by a micro-camera mounted on the user’s glass with the haptic ones provided by the piezoelectric sensors.

Physical deep learning with biologically inspired training method: gradient-free approach for physical hardware

Ever-growing demand for artificial intelligence has motivated research on unconventional computation based on physical devices. While such computation devices mimic brain-inspired analog information processing, the learning procedures still rely on methods optimized for digital processing such as backpropagation, which is not suitable for physical implementation. Here, we present physical deep learning by extending a biologically inspired training algorithm called direct feedback alignment. Unlike the original algorithm, the proposed method is based on random projection with alternative nonlinear activation. Thus, we can train a physical neural network without knowledge about the physical system and its gradient. In addition, we can emulate the computation for this training on scalable physical hardware. We demonstrate the proof-of-concept using an optoelectronic recurrent neural network called deep reservoir computer. We confirmed the potential for accelerated computation with competitive performance on benchmarks. Our results provide practical solutions for the training and acceleration of neuromorphic computation.

Fabrication and Functionality Integration Technologies for Small-Scale Soft Robots

Small-scale soft robots are attracting increasing interest for visible and potential applications owing to their safety and tolerance resulting from their intrinsic soft bodies or compliant structures. However, it is not sufficient that the soft bodies merely provide support or system protection. More importantly, to meet the increasing demands of controllable operation and real-time feedback in unstructured/complicated scenarios, these robots are required to perform simplex and multimodal functionalities for sensing, communicating, and interacting with external environments during large or dynamic deformation with the risk of mismatch or delamination. Challenges are encountered during fabrication and integration, including the selection and fabrication of composite/materials and structures, integration of active/passive functional modules with robust interfaces, particularly with highly deformable soft/stretchable bodies. Here, methods and strategies of fabricating structural soft bodies and integrating them with functional modules for developing small-scale soft robots are investigated. Utilizing templating, 3D printing, transfer printing, and swelling, small-scale soft robots can be endowed with several perceptual capabilities corresponding to diverse stimulus, such as light, heat, magnetism, and force. The integration of sensing and functionalities effectively enhances the agility, adaptability, and universality of soft robots when applied in various fields, including smart manufacturing, medical surgery, biomimetics, and other interdisciplinary sciences.

Soft robotics for infrastructure protection

The paradigm change introduced by soft robotics is going to dramatically push forward the abilities of autonomous systems in the next future, enabling their applications in extremely challenging scenarios. The ability of soft robots to safely interact and adapt to the surroundings is key to operate in unstructured environments, where the autonomous agent has little or no knowledge about the world around it. A similar context occurs when critical infrastructures face threats or disruptions, for examples due to natural disasters or external attacks (physical or cyber). In this case, autonomous systems may be employed to respond to such emergencies and have to be able to deal with unforeseen physical conditions and uncertainties, where the mechanical interaction with the environment is not only inevitable but also desirable to successfully perform their tasks. In this perspective, I discuss applications of soft robots for the protection of infrastructures, including recent advances in pipelines inspection, rubble search and rescue, and soft aerial manipulation, and promising perspectives on operations in radioactive environments, underwater monitoring and space exploration.

Influence of microstructural alterations of liquid metal and its interfacial interactions with rubber on multifunctional properties of soft composite materials

Liquid metal (LM)-based polymer composites are currently new breakthrough and emerging classes of soft multifunctional materials (SMMs) having immense transformative potential for soft technological applications. Currently, room-temperature LMs, mostly eutectic gallium‑indium and Galinstan alloys are used to integrate with soft polymer due to their outstanding properties such as high conductivity, fluidity, low adhesion, high surface tension, low cytotoxicity, etc. The microstructural alterations and interfacial interactions controlling the efficient integration of LMs with rubber are the most critical aspects for successful implementation of multifunctionality in the resulting material. In this review article, a fundamental understanding of microstructural alterations of LMs to the formation of well-defined percolating networks inside an insulating rubber matrix has been established by exploiting several existing theoretical and experimental studies. Furthermore, effects of the chemical modifications of an LM surface and its interfacial interactions on the compatibility between solid rubber and fluid filler phase have been discussed. The presence of thin oxide layer on the LM surface and the effects and challenges it poses to the adequate functionalization of these materials have been discussed. Plausible applications of SMMs in different soft matter technologies, like soft robotics, flexible electronics, soft actuators, sensors, etc. have been provided. Finally, the current technical challenges and further prospective to the development of SMMs using non‑silicone rubbers have been critically discussed. This review is anticipated to infuse a new impetus to the associated research communities for the development of next generation SMMs.

Small-scale soft grippers with environmentally responsive logic gates

Small-scale soft grippers are adaptive and deformable, and can be utilized for confined environments (e.g., the human body). Small-scale soft grippers require logic-based computation to achieve intelligent control and perform logical analysis of the surrounding information. However, it is a great challenge to integrate electronic chips and power supplies (i.e., batteries) on their small systems. Here, the approach provides a route to add computational capabilities via environmentally responsive logic gates in small-scale soft grippers, without electronics, external control, or tethering. Various origami-inspired grippers performing YES, NOT, XOR, AND, OR, NOR and NAND gates, respectively, were developed by stimuli-responsive hydrogels as building blocks. Although the hydrogels respond to different kinds of stimuli, their outputs are the same: a change in hydrogel size, leading to the bending of the arms of the grippers. Hence, the logic gates can be integrated easily within a gripper (e.g., connecting an AND gate to another AND gate). Moreover, the gripper fabricated by dual-responsive hydrogels can intelligently and autonomously switch from an AND gate to an OR gate upon varied environmental stimuli. In addition, a magnetic gripper with an AND gate was fabricated that can analyse different stimuli, and capture and release the targeted object via the environmentally responsive logic gates. This strategy provides a new route to incorporate on-board perception, control and computation via environmentally responsive logic gates in small-scale soft robots and machines.

Review of Electronics-Free Robotics: Toward a Highly Decentralized Control Architecture

In recent years, conventional model-based motion control has become more challenging owing to the continuously increasing complexity of areas in which robots must operate and navigate. A promising approach for solving this issue is by employing interaction-based robotics, which includes behavior-based robotics, morphological computations, and soft robotics that generate control and computation functions based on interactions between the robot body and environment. These control strategies, which incorporate the diverse dynamics of the environment to generate control and computation functions, may alleviate the limitations imposed by the finite physical and computational resources of conventional robots. However, current interaction-based robots can only perform a limited number of actions compared with conventional robots. To increase the diversity of behaviors generated from body–environment interactions, a robotic body design methodology that can generate appropriate behaviors depending on the various situations and environmental stimuli that arise from them is necessitated. Electronics-free robotics is reviewed herein as a paradigm for designing robots with control and computing functions in each part of the body. In electronics-free robotics, instead of using electrical sensors or computers, a control system is constructed based on only mechanical or chemical reactions. Robotic bodies fabricated using this approach do not require bulky electrical wiring or peripheral circuits and can perform control and computational functions by obtaining energy from a central source. Therefore, by distributing these electronics-free controllers throughout the body, we hope to design autonomous and highly decentralized robotic bodies than can generate various behaviors in response to environmental stimuli. This new paradigm of designing and controlling robot bodies can enable realization of completely electronics-free robots as well as expand the range of conventional electronics-based robot designs.

Electrochemical Dual Transducer for Fluidic Self-Sensing Actuation

An electrochemical dual transducer (ECDT) based on a chemical reaction is a new fluidic machine for self-sensing actuation. Recently, incorporating sensors has enhanced the multifunctionality of soft robots with fluidic machines such as pumps or compressors. However, conventional fluidic systems have limitations such as heavy weight, noise, bloat, and complexity. In our previous research, we adopted small-sized, lightweight, and quiet electrohydrodynamic pumps for soft robots. In this paper, we propose a new ECDT by exploring the possibility of an electrohydrodynamic (EHD) pump to sense the flow of the working fluid. The current in the ECDT is proportional to 1/3 of the inflowing velocity. We also clarify its mechanism, mathematical model, range of detectable flow rate, sensitivity factor, relaxation time, response speed, and pumping characteristics. The advantages of the ECDT are their small size, light weight, simple fabrication process, extensibility of the sensing range, and sensitivity. We also demonstrate a suction cup driven by the ECDT, which can detect, hold, and release objects. We expect a bidirectional ECDT will realize a small, multifunctional, and straightforward fluidic system.

Embodied Intelligence in Soft Robotics Through Hardware Multifunctionality

The soft robotics community is currently wondering what the future of soft robotics is. Therefore, it is very important to identify the directions in which the community should focus its efforts to consolidate its impact. The identification of convincing applications is a priority, especially to demonstrate that some achievements already represent an attractive alternative to current technological approaches in specific scenarios. However, most of the added value of soft robotics has been only theoretically grasped. Embodied Intelligence, being of these theoretical principles, represents an interesting approach to fully exploit soft robotic’s potential, but a pragmatic application of this theory still remains difficult and very limited. A different design approach could be beneficial, i.e., the integration of a certain degree of continuous adaptability in the hardware functionalities of the robot, namely, a “flexible” design enabled by hardware components able to fulfill multiple functionalities. In this paper this concept of flexible design is introduced along with its main technological and theoretical basic elements. The potential of the approach is demonstrated through a biological comparison and the feasibility is supported by practical examples with state-of-the-art technologies.

Highly sensitive humidity-driven actuators based on metal-organic frameworks incorporating thermoplastic polyurethane with gradient polymer distribution

Ambient humidity plays an important role in the fields of industrial and agricultural production, food and drug storage, climate monitoring, and maintenance of precision instruments. To sense and control humidity, humidity-responsive actuators that mimick humidity responsive behavior existing in nature, have attracted intense attention. The most common and important class of humidity actuators is active bilayer structures. However, such bilayer structures generally show weak interfacial adhesion, tending to delaminate during frequent bending and restoration cycles. In this work, to address this problem, a novel monolayer humidity-driven actuator with no adhesive issue is developed by integrating the swellable metal-organic frameworks (MIL-88A) into thermoplastic polyurethane films. The proposed actuators display excellent humidity response that under the conditions of relative humidity simulated with saturated salt solution, the MIL-88A/polyurethane composite films show good self-folding response and stability for recycling use. In addition, a deep insight into the self-folding of the composite films is also provided and a new response mechanism is proposed. In this case, the results show that both the preparation method and response properties of the humidity actuators are improved. Therefore, it suggests a new promising way to develop and design flexible humidity actuators.

Biology and bioinspiration of soft robotics: Actuation, sensing, and system integration

Organisms in nature grow with senses, nervous, and actuation systems coordinated in ingenious ways to sustain metabolism and other essential life activities. The understanding of biological structures and functions guide the construction of soft robotics with unprecedented performances. However, despite the progress in soft robotics, there still remains a big gap between man-made soft robotics and natural lives in terms of autonomy, adaptability, self-repair, durability, energy efficiency, etc. Here, the actuation and sensing strategies in the natural biological world are summarized along with their man-made counterparts applied in soft robotics. The development trends of bioinspired soft robotics toward closed loop and embodiment are proposed. Challenges for obtaining autonomous soft robotics similar to natural organisms are outlined to provide a perspective in this field.

Morphological Sensitivity and Falling Behavior of Paper V-Shapes

Behavioral diversity seen in biological systems is, at the most basic level, driven by interactions between physical materials and their environment. In this context we are interested in falling paper systems, specifically the V-shaped falling paper (VSFP) system that exhibits a set of discrete falling behaviors across the morphological parameter space. Our previous work has investigated how morphology influences dominant falling behaviors in the VSFP system. In this article we build on this analysis to investigate the nature of behavioral transitions in the same system. First, we investigate stochastic behavior transitions. We demonstrate how morphology influences the likelihood of different transitions, with certain morphologies leading to a wide range of possible paths through the behavior-space. Second, we investigate deterministic transitions. To investigate behaviors over longer time periods than available in falling experiments we introduce a new experimental platform. We demonstrate how we can induce behavior transitions by modulating the energy input to the system. Certain behavior transitions are found to be irreversible, exhibiting a form of hysteresis, while others are fully reversible. Certain morphologies are shown to behave like simplistic sequential logic circuits, indicating that the system has a form of memory encoded into the morphology-environment interactions. Investigating the limits of how morphology-environment interactions induce non-trivial behaviors is a key step for the design of embodied artificial life-forms.

A pneumatic random-access memory for controlling soft robots

Pneumatically-actuated soft robots have advantages over traditional rigid robots in many applications. In particular, their flexible bodies and gentle air-powered movements make them more suitable for use around humans and other objects that could be injured or damaged by traditional robots. However, existing systems for controlling soft robots currently require dedicated electromechanical hardware (usually solenoid valves) to maintain the actuation state (expanded or contracted) of each independent actuator. When combined with power, computation, and sensing components, this control hardware adds considerable cost, size, and power demands to the robot, thereby limiting the feasibility of soft robots in many important application areas. In this work, we introduce a pneumatic memory that uses air (not electricity) to set and maintain the states of large numbers of soft robotic actuators without dedicated electromechanical hardware. These pneumatic logic circuits use normally-closed microfluidic valves as transistor-like elements; this enables our circuits to support more complex computational functions than those built from normally-open valves. We demonstrate an eight-bit nonvolatile random-access pneumatic memory (RAM) that can maintain the states of multiple actuators, control both individual actuators and multiple actuators simultaneously using a pneumatic version of time division multiplexing (TDM), and set actuators to any intermediate position using a pneumatic version of analog-to-digital conversion. We perform proof-of-concept experimental testing of our pneumatic RAM by using it to control soft robotic hands playing individual notes, chords, and songs on a piano keyboard. By dramatically reducing the amount of hardware required to control multiple independent actuators in pneumatic soft robots, our pneumatic RAM can accelerate the spread of soft robotic technologies to a wide range of important application areas.

Fully 3D-printed soft robots with integrated fluidic circuitry

The emergence of soft robots has presented new challenges associated with controlling the underlying fluidics of such systems. Here, we introduce a strategy for additively manufacturing unified soft robots comprising fully integrated fluidic circuitry in a single print run via PolyJet three-dimensional (3D) printing. We explore the efficacy of this approach for soft robots designed to leverage novel 3D fluidic circuit elements-e.g., fluidic diodes, "normally closed" transistors, and "normally open" transistors with geometrically tunable pressure-gain functionalities-to operate in response to fluidic analogs of conventional electronic signals, including constant-flow ["direct current (DC)"], "alternating current (AC)"-inspired, and preprogrammed aperiodic ("variable current") input conditions. By enabling fully integrated soft robotic entities (composed of soft actuators, fluidic circuitry, and body features) to be rapidly disseminated, modified on demand, and 3D-printed in a single run, the presented design and additive manufacturing strategy offers unique promise to catalyze new classes of soft robots.

Soft robotics: the route to true robotic organisms

Soft Robotics has come to the fore in the last decade as a new way of conceptualising, designing and fabricating robots. Soft materials empower robots with locomotion, manipulation, and adaptability capabilities beyond those possible with conventional rigid robots. Soft robots can also be made from biological, biocompatible and biodegradable materials. This offers the tantalising possibility of bridging the gap between robots and organisms. Here, we discuss the properties of soft materials and soft systems that make them so attractive for future robots. In doing so, we consider how future robots can behave like, and have abilities akin to, biological organisms. These include huge numbers, finite lifetime, homeostasis and minimal—and even positive—environmental impact. This paves the way for future robots, not as machines, but as robotic organisms.

The rise of intelligent matter

Artificial intelligence (AI) is accelerating the development of unconventional computing paradigms inspired by the abilities and energy efficiency of the brain. The human brain excels especially in computationally intensive cognitive tasks, such as pattern recognition and classification. A long-term goal is de-centralized neuromorphic computing, relying on a network of distributed cores to mimic the massive parallelism of the brain, thus rigorously following a nature-inspired approach for information processing. Through the gradual transformation of interconnected computing blocks into continuous computing tissue, the development of advanced forms of matter exhibiting basic features of intelligence can be envisioned, able to learn and process information in a delocalized manner. Such intelligent matter would interact with the environment by receiving and responding to external stimuli, while internally adapting its structure to enable the distribution and storage (as memory) of information. We review progress towards implementations of intelligent matter using molecular systems, soft materials or solid-state materials, with respect to applications in soft robotics, the development of adaptive artificial skins and distributed neuromorphic computing.

Progress and Roadmap for Intelligent Self-Healing Materials in Autonomous Robotics

Robots are increasingly assisting humans in performing various tasks. Like special agents with elite skills, they can venture to distant locations and adverse environments, such as the deep sea and outer space. Micro/nanobots can also act as intrabody agents for healthcare applications. Self-healing materials that can autonomously perform repair functions are useful to address the unpredictability of the environment and the increasing drive toward the autonomous operation. Having self-healable robotic materials can potentially reduce costs, electronic wastes, and improve a robot endowed with such materials longevity. This review aims to serve as a roadmap driven by past advances and inspire future cross-disciplinary research in robotic materials and electronics. By first charting the history of self-healing materials, new avenues are provided to classify the various self-healing materials proposed over several decades. The materials and strategies for self-healing in robotics and stretchable electronics are also reviewed and discussed. It is believed that this article encourages further innovation in this exciting and emerging branch in robotics interfacing with material science and electronics.

Neural Networks Predicting Microbial Fuel Cells Output for Soft Robotics Applications

The development of biodegradable soft robotics requires an appropriate eco-friendly source of energy. The use of Microbial Fuel Cells (MFCs) is suggested as they can be designed completely from soft materials with little or no negative effects to the environment. Nonetheless, their responsiveness and functionality is not strictly defined as in other conventional technologies, i.e. lithium batteries. Consequently, the use of artificial intelligence methods in their control techniques is highly recommended. The use of neural networks, namely a nonlinear autoregressive network with exogenous inputs was employed to predict the electrical output of an MFC, given its previous outputs and feeding volumes. Thus, predicting MFC outputs as a time series, enables accurate determination of feeding intervals and quantities required for sustenance that can be incorporated in the behavioural repertoire of a soft robot.

Electronics-free pneumatic circuits for controlling soft-legged robots

Pneumatically actuated soft robots have recently shown promise for their ability to adapt to their environment. Previously, these robots have been controlled with electromechanical components, such as valves and pumps, that are typically bulky and expensive. Here, we present an approach for controlling the gaits of soft-legged robots using simple pneumatic circuits without any electronic components. This approach produces locomotive gaits using ring oscillators composed of soft valves that generate oscillating signals analogous to biological central pattern generator neural circuits, which are acted upon by pneumatic logic components in response to sensor inputs. Our robot requires only a constant source of pressurized air to power both control and actuation systems. We demonstrate this approach by designing pneumatic control circuits to generate walking gaits for a soft-legged quadruped with three degrees of freedom per leg and to switch between gaits to control the direction of locomotion. In experiments, we controlled a basic walking gait using only three pneumatic memory elements (valves). With two oscillator circuits (seven valves), we were able to improve locomotion speed by 270%. Furthermore, with a pneumatic memory element we designed to mimic a double-pole double-throw switch, we demonstrated a control circuit that allowed the robot to select between gaits for omnidirectional locomotion and to respond to sensor input. This work represents a step toward fully autonomous, electronics-free walking robots for applications including low-cost robotics for entertainment and systems for operation in environments where electronics may not be suitable.

Tactile sensing biohybrid soft E-skin based on bioimpedance using aloe vera pulp tissues

Soft and flexible E-skin advances are a subset of soft robotics field where the soft morphology of human skin is mimicked. The number of prototypes that conformed the use of biological tissues within the structure of soft robots—to develop “Biohybrid Soft Robots”—has increased in the last decade. However, no research was conducted to realize Biohybrid E-skin. In this paper, a novel biohybrid E-skin that provides tactile sensing is developed. The biohybrid E-skin highly mimics the human skin softness and morphology and can sense forces as low as 0.01 newton . The tactile sensing feature is augmented through the use of Aloe Vera pulp, embedded in underlying channel, where the change in its bioimpedance is related to the amount of force exerted on the E-skin surface. The biohybrid E-skin employs high biomimicry as the sensorial output is an oscillating signal similar to signals sent from the human sensing neurons to the brain. After investigating different channel geometries, types of filling tissues, and usage of two silicone materials, their frequency-force behaviour is modelled mathematically. Finally, a functional multichannel prototype “ImpEdded Skin” is developed. This prototype could efficiently detect the position of a tactile touch. This work employs the development of discrete sensing system that exhibits morphological computation that consequently enhances performance.

Magnetic Printing of Liquid Metal for Perceptive Soft Actuators with Embodied Intelligence

Soft actuators with perception capability are essential for robots to intelligently interact with humans and the environment. However, existing perceptive soft actuators require complex integration and coupling between the discrete functional units to achieve autonomy. Here, we report entirely soft actuators with embodied sensing, actuation, and control at the single-unit level. This is achieved by synergistically harnessing the mechanosensing and electrothermal properties of liquid metal (LM) to actuate the thermally responsive liquid crystal elastomer (LCE). We create multifunctional LM circuits on the LCE surface using a simple and facile methodology based on magnetic printing. The fluidic LM circuit can not only be utilized as a conformable resistive heater but also as a sensory skin to perceive its own deformation. Moreover, the rational design of the LM circuits makes it possible to achieve biomimetic autonomous actuation in response to mechanical stimuli such as pressure or strain. In addition, the intrinsic stretchability of LM allows us to create 3D spring-like actuators via a simple prestretch step, and complex helical motions can be obtained upon mechanical stimulation. This work provides a unique and simple design for autonomous soft robotics with embodied intelligence.

Soft Robots for Ocean Exploration and Offshore Operations: A Perspective

The ocean and human activities related to the sea are under increasing pressure due to climate change, widespread pollution, and growth of the offshore energy sector. Data, in under-sampled regions of the ocean and in the offshore patches where the industrial expansion is taking place, are fundamental to manage successfully a sustainable development and to mitigate climate change. Existing technology cannot cope with the vast and harsh environments that need monitoring and sampling the most. The limiting factors are, among others, the spatial scales of the physical domain, the high pressure, and the strong hydrodynamic perturbations, which require vehicles with a combination of persistent autonomy, augmented efficiency, extreme robustness, and advanced control. In light of the most recent developments in soft robotics technologies, we propose that the use of soft robots may aid in addressing the challenges posed by abyssal and wave-dominated environments. Nevertheless, soft robots also allow for fast and low-cost manufacturing, presenting a new potential problem: marine pollution from ubiquitous soft sampling devices. In this study, the technological and scientific gaps are widely discussed, as they represent the driving factors for the development of soft robotics. Offshore industry supports increasing energy demand and the employment of robots on marine assets is growing. Such expansion needs to be sustained by the knowledge of the oceanic environment, where large remote areas are yet to be explored and adequately sampled. We offer our perspective on the development of sustainable soft systems, indicating the characteristics of the existing soft robots that promote underwater maneuverability, locomotion, and sampling. This perspective encourages an interdisciplinary approach to the design of aquatic soft robots and invites a discussion about the industrial and oceanographic needs that call for their application.

An autonomous untethered fast soft robotic insect driven by low-voltage dielectric elastomer actuators

Insects are a constant source of inspiration for roboticists. Their compliant bodies allow them to squeeze through small openings and be highly resilient to impacts. However, making subgram autonomous soft robots untethered and capable of responding intelligently to the environment is a long-standing challenge. One obstacle is the low power density of soft actuators, leading to small robots unable to carry their sense and control electronics and a power supply. Dielectric elastomer actuators (DEAs), a class of electrostatic electroactive polymers, allow for kilohertz operation with high power density but require typically several kilovolts to reach full strain. The mass of kilovolt supplies has limited DEA robot speed and performance. In this work, we report low-voltage stacked DEAs (LVSDEAs) with an operating voltage below 450 volts and used them to propel an insect-sized (40 millimeters long) soft untethered and autonomous legged robot. The DEAnsect body, with three LVSDEAs to drive its three legs, weighs 190 milligrams and can carry a 950-milligram payload (five times its body weight). The unloaded DEAnsect moves at 30 millimeters/second and is very robust by virtue of its compliance. The sub-500-volt operation voltage enabled us to develop 780-milligram drive electronics, including optical sensors, a microcontroller, and a battery, for two channels to output 450 volts with frequencies up to 1 kilohertz. By integrating this flexible printed circuit board with the DEAnsect, we developed a subgram robot capable of autonomous navigation, independently following printed paths. This work paves the way for new generations of resilient soft and fast untethered robots.