The grand challenges of Science Robotics

Nano revolution: pioneering the future of water reclamation with micro-/nano-robots

Earth's freshwater reserves are alarmingly limited, with less than 1% readily available. Factors such as industrialisation, population expansion, and climate change are compounding the scarcity of clean water. In this context, self-driven, programmable micro- and nano-scale synthetic robots offer a potential solution for enhancing water monitoring and remediation. With the aid of these innovative robots, diffusion-limited reactions can be overcome, allowing for active engagement with target pollutants, such as heavy metals, dyes, nano- and micro-plastics, oils, pathogenic microorganisms, and persistent organic pollutants. Herein, we introduced and reviewed recent influential and advanced studies on micro-/nano-robots (MNR) carried out over the past decade. Typical works are categorized by propulsion modes, analyzing their advantages and drawbacks in detail and looking at specific applications. Moreover, this review provides a concise overview of the contemporary advancements and applications of micro-/nano-robots in water-cleaning applications.

Flexible sensors with zero Poisson's ratio

Flexible sensors have been developed for the perception of various stimuli. However, complex deformation, usually resulting from forces or strains from multi-axes, can be challenging to measure due to the lack of independent perception of multiaxial stimuli. Herein, flexible sensors based on the metamaterial membrane with zero Poisson's ratio (ZPR) are proposed to achieve independent detection of biaxial stimuli. By deliberately designing the geometric dimensions and arrangement parameters of elements, the Poisson's ratio of an elastomer membrane can be modulated from negative to positive, and the ZPR membrane can maintain a constant transverse dimension under longitudinal stimuli. Due to the accurate monitoring of grasping force by ZPR sensors that are insensitive to curvatures of contact surfaces, rigid robotic manipulators can be guided to safely grasp deformable objects. Meanwhile, the ZPR sensor can also precisely distinguish different states of manipulators. When ZPR sensors are attached to a thermal-actuation soft robot, they can accurately detect the moving distance and direction. This work presents a new strategy for independent biaxial stimuli perception through the design of mechanical metamaterials, and may inspire the future development of advanced flexible sensors for healthcare, human-machine interfaces and robotic tactile sensing.

Why animals can outrun robots

Animals are much better at running than robots. The difference in performance arises in the important dimensions of agility, range, and robustness. To understand the underlying causes for this performance gap, we compare natural and artificial technologies in the five subsystems critical for running: power, frame, actuation, sensing, and control. With few exceptions, engineering technologies meet or exceed the performance of their biological counterparts. We conclude that biology's advantage over engineering arises from better integration of subsystems, and we identify four fundamental obstacles that roboticists must overcome. Toward this goal, we highlight promising research directions that have outsized potential to help future running robots achieve animal-level performance.

Highly Stretchable, Self-Healing, and Sensitive E-Skins at -78 °C for Polar Exploration

Ultralow temperature-tolerant electronic skins (e-skins) can endow polar robots with tactile feedback for exploring in extremely cold polar environments. However, it remains a challenge to develop e-skins that enable sensitive touch sensation and self-healing at ultralow temperatures. Herein, we describe the development of a sensitive robotic hand e-skin that can stretch, self-heal, and sense at temperatures as low as -78 °C. The elastomeric substrate of this e-skin is based on poly(dimethylsiloxane) supramolecular polymers and multistrength dynamic H-bonds, in particular with quadruple H-bonding motifs (UPy). The structure-performance relationship of the elastomer at ultralow temperatures is investigated. The results show that elastomers with side-chain UPy units exhibit higher stretchability (∼3257%) and self-healing efficiency compared to those with main-chain UPy units. This is attributed to the lower binding energy variation and lower potential well. Based on the elastomer with side-chain UPy and man-made electric ink, a sensitive robotic hand e-skin for usage at -78 °C is constructed to precisely sense the shape of objects and specific symbols, and its sensation can completely self-recover after being damaged. The findings of this study contribute to the concept of using robotic hands with e-skins in polar environments that make human involvement limited, dangerous, or impossible.

Rotary FoF1-ATP Synthase-Driven Flasklike Pentosan Colloidal Motors with ATP Synthesis and Storage

We report the hierarchical assembly of a chloroplast-derived rotary FoF1-ATPase motor-propelled flasklike pentosan colloidal motor (FPCM) with the ability of the synthesis, storage, and triggered release of biological energy currency ATP. These streamlined and submicrometer-sized hollow flasklike pentosan colloidal motors are prepared by combining a soft-template-based hydrothermal polymerization with a vacuum infusion of chloroplast-derived proteoliposomes containing rotary FoF1-ATPase motors. The generation of proton motive force across the proteoliposomes by injecting an acidic buffer solution promotes the rotation of FoF1-ATPase motors to drive the self-propelled motion of FPCMs, accompanying the inner ATP synthesis and storage. These rotary FoF1-ATPase motor-powered FPCMs exhibit a chemotactic behavior by migrating from their neck opening to their round bottom along a proton gradient of the external environment (negative chemotaxis). Such rotary biomolecular motor-driven flasklike pentosan colloidal motors with ATP synthesis and on-demand release make them promising candidates for engineering novel intelligent nanocarriers to actively regulate cellular metabolism.

Multimodal Sensors Enabled Autonomous Soft Robotic System with Self-Adaptive Manipulation

Human hands are amazingly skilled at recognizing and handling objects of different sizes and shapes. To date, soft robots rarely demonstrate autonomy equivalent to that of humans for fine perception and dexterous operation. Here, an intelligent soft robotic system with autonomous operation and multimodal perception ability is developed by integrating capacitive sensors with triboelectric sensor. With distributed multiple sensors, our robot system can not only sense and memorize multimodal information but also enable an adaptive grasping method for robotic positioning and grasp control, during which the multimodal sensory information can be captured sensitively and fused at feature level for crossmodally recognizing objects, leading to a highly enhanced recognition capability. The proposed system, combining the performance and physical intelligence of biological systems (i.e., self-adaptive behavior and multimodal perception), will greatly advance the integration of soft actuators and robotics in many fields.

A Survey of Recent Developments in Magnetic Microrobots for Micro-/Nano-Manipulation

Magnetically actuated microrobots have become a research hotspot in recent years due to their tiny size, untethered control, and rapid response capability. Moreover, an increasing number of researchers are applying them for micro-/nano-manipulation in the biomedical field. This survey provides a comprehensive overview of the recent developments in magnetic microrobots, focusing on materials, propulsion mechanisms, design strategies, fabrication techniques, and diverse micro-/nano-manipulation applications. The exploration of magnetic materials, biosafety considerations, and propulsion methods serves as a foundation for the diverse designs discussed in this review. The paper delves into the design categories, encompassing helical, surface, ciliary, scaffold, and biohybrid microrobots, with each demonstrating unique capabilities. Furthermore, various fabrication techniques, including direct laser writing, glancing angle deposition, biotemplating synthesis, template-assisted electrochemical deposition, and magnetic self-assembly, are examined owing to their contributions to the realization of magnetic microrobots. The potential impact of magnetic microrobots across multidisciplinary domains is presented through various application areas, such as drug delivery, minimally invasive surgery, cell manipulation, and environmental remediation. This review highlights a comprehensive summary of the current challenges, hurdles to overcome, and future directions in magnetic microrobot research across different fields.

Soft Sensors and Actuators for Wearable Human-Machine Interfaces

Haptic human-machine interfaces (HHMIs) combine tactile sensation and haptic feedback to allow humans to interact closely with machines and robots, providing immersive experiences and convenient lifestyles. Significant progress has been made in developing wearable sensors that accurately detect physical and electrophysiological stimuli with improved softness, functionality, reliability, and selectivity. In addition, soft actuating systems have been developed to provide high-quality haptic feedback by precisely controlling force, displacement, frequency, and spatial resolution. In this Review, we discuss the latest technological advances of soft sensors and actuators for the demonstration of wearable HHMIs. We particularly focus on highlighting material and structural approaches that enable desired sensing and feedback properties necessary for effective wearable HHMIs. Furthermore, promising practical applications of current HHMI technology in various areas such as the metaverse, robotics, and user-interactive devices are discussed in detail. Finally, this Review further concludes by discussing the outlook for next-generation HHMI technology.

Tracking and navigation of a microswarm under laser speckle contrast imaging for targeted delivery

Micro/nanorobotic swarms consisting of numerous tiny building blocks show great potential in biomedical applications because of their collective active delivery ability, enhanced imaging contrast, and environment-adaptive capability. However, in vivo real-time imaging and tracking of micro/nanorobotic swarms remain a challenge, considering the limited imaging size and spatial-temporal resolution of current imaging modalities. Here, we propose a strategy that enables real-time tracking and navigation of a microswarm in stagnant and flowing blood environments by using laser speckle contrast imaging (LSCI), featuring full-field imaging, high temporal-spatial resolution, and noninvasiveness. The change in dynamic convection induced by the microswarm can be quantitatively investigated by analyzing the perfusion unit (PU) distribution, offering an alternative approach to investigate the swarm behavior and its interaction with various blood environments. Both the microswarm and surrounding environment were monitored and imaged by LSCI in real time, and the images were further analyzed for simultaneous swarm tracking and navigation in the complex vascular system. Moreover, our strategy realized real-time tracking and delivery of a microswarm in vivo, showing promising potential for LSCI-guided active delivery of microswarm in the vascular system.

Stenus-inspired, swift, and agile untethered insect-scale soft propulsors

Mimicking living creatures, soft robots exhibit incomparable adaptability and various attractive new features. However, untethered insect-scale soft robots are often plagued with inferior controllability and low kinetic performance. Systematically inspired by the swift swingable abdomen, conducting canals for secretion transport, and body setae of Stenus comma, together with magnetic-induced fast-transformed postures, herein, we present a swift, agile untethered millimetre-scale soft propulsor propelling on water. The demonstrated propulsor, with a body length (BL) of 3.6 mm, achieved a recorded specific speed of ~201 BL/s and acceleration of ~8,372 BL/s2. The comprehensive kinetic performance of this propulsor surpasses those of previous ones at similar scales by several orders. Notably, we discovered momentum-transfer-induced over-biological on-demand braking (deceleration ~-5,010 BL/s2) and elucidated the underlying hydrodynamics. This work offers new insights into systematically bio-inspired artificial insect-scale soft robots, enabling them to push boundaries in performance, and potentially revolutionizing robot design, optimization, and control paradigms.

A Variable Stiffness Soft Gripper Based on Rotational Layer Jamming

This article presents the design and fabrication of a variable stiffness soft gripper based on layer jamming. Traditional layer jamming units have some limitations, such as complicated multistep fabrication, difficulties in system integration, and diminishing in stiffen effect. In this article, a variable stiffness soft gripper is proposed based on the rotational jamming layers to reduce the slippery phenomenon between layers. To fabricate the proposed complex design, a two-step fabrication method is presented. First, multimaterial 3D printing is applied to directly print out the soft finger body with jamming layers. Second, mold casting is used to fabricate the outer vacuum chamber. The proposed gripper contains a main framework and three identical variable stiffness soft fingers. To demonstrate the effectiveness of the design, the soft gripper is mounted on a robotic arm to test its ability of grasping heavy objects while following complex grasping trajectory. The gripper can successfully grasp an object up to 360 g. Grasping robustness of the proposed gripper can be guaranteed when the robotic arm is moving at acceleration up to 7 m/s2. The results prove that the proposed design of the soft gripper can improve the grippers grasping robustness during high-speed movement.

Multimodal Locomotion in a Soft Robot Through Hierarchical Actuation

Soft and continuum robots present the opportunity for extremely large ranges of motion, which can enable dexterous, adaptive, and multimodal locomotion behaviors. However, as the number of degrees of freedom (DOF) of a robot increases, the number of actuators should also increase to achieve the full actuation potential. This presents a dilemma in mobile soft robot design: physical space and power requirements restrict the number and type of actuators available and may ultimately limit the movement capabilities of soft robots with high-DOF appendages. Restrictions on actuation of continuum appendages ultimately may limit the various movement capabilities of soft robots. In this work, we demonstrate multimodal behaviors in an underwater robot called "Hexapus." A hierarchical actuation design for multiappendage soft robots is presented in which a single high-power motor actuates all appendages for locomotion, while smaller low-power motors augment the shape of each appendage. The flexible appendages are designed to be capable of hyperextension for thrust, and flexion for grasping with a peak pullout force of 32 N. For propulsion, we incorporate an elastic membrane connected across the base of each tentacle, which is stretched slowly by the high-power motor and released rapidly through a slip-gear mechanism. Through this actuation arrangement, Hexapus is capable of underwater locomotion with low cost of transport (COT = 1.44 at 16.5 mm/s) while swimming and a variety of multimodal locomotion behaviors, including swimming, turning, grasping, and crawling, which we demonstrate in experiment.

Separable amygdala activation patterns in the evaluations of robots

Given the increasing presence of robots in everyday environments and the significant challenge posed by social interactions with robots, it is crucial to gain a deeper understanding into the social evaluations of robots. One potentially effective approach to comprehend the fundamental processes underlying controlled and automatic evaluations of robots is to probe brain response to different perception levels of robot-related stimuli. Here, we investigate controlled and automatic evaluations of robots based on brain responses during viewing of suprathreshold (duration: 200 ms) and subthreshold (duration: 17 ms) humanoid robot stimuli. Our behavioral analysis revealed that despite participants' self-reported positive attitudes, they held negative implicit attitudes toward humanoid robots. Neuroimaging analysis indicated that subthreshold presentation of humanoid robot stimuli elicited significant activation in the left amygdala, which was associated with negative implicit attitudes. Conversely, no significant left amygdala activation was observed during suprathreshold presentation. Following successful attenuation of negative attitudes, the left amygdala response to subthreshold presentation of humanoid robot stimuli decreased, and this decrease correlated positively with the reduction in negative attitudes. These findings provide evidence for separable patterns of amygdala activation between controlled and automatic processing of robots, suggesting that controlled evaluations may influence automatic evaluations of robots.

Flexible Metasurfaces for Multifunctional Interfaces

Optical metasurfaces, capable of manipulating the properties of light with a thickness at the subwavelength scale, have been the subject of extensive investigation in recent decades. This research has been mainly driven by their potential to overcome the limitations of traditional, bulky optical devices. However, most existing optical metasurfaces are confined to planar and rigid designs, functions, and technologies, which greatly impede their evolution toward practical applications that often involve complex surfaces. The disconnect between two-dimensional (2D) planar structures and three-dimensional (3D) curved surfaces is becoming increasingly pronounced. In the past two decades, the emergence of flexible electronics has ushered in an emerging era for metasurfaces. This review delves into this cutting-edge field, with a focus on both flexible and conformal design and fabrication techniques. Initially, we reflect on the milestones and trajectories in modern research of optical metasurfaces, complemented by a brief overview of their theoretical underpinnings and primary classifications. We then showcase four advanced applications of optical metasurfaces, emphasizing their promising prospects and relevance in areas such as imaging, biosensing, cloaking, and multifunctionality. Subsequently, we explore three key trends in optical metasurfaces, including mechanically reconfigurable metasurfaces, digitally controlled metasurfaces, and conformal metasurfaces. Finally, we summarize our insights on the ongoing challenges and opportunities in this field.

Fiberbots: Robotic fibers for high-precision minimally invasive surgery

Precise manipulation of flexible surgical tools is crucial in minimally invasive surgical procedures, necessitating a miniature and flexible robotic probe that can precisely direct the surgical instruments. In this work, we developed a polymer-based robotic fiber with a thermal actuation mechanism by local heating along the sides of a single fiber. The fiber robot was fabricated by highly scalable fiber drawing technology using common low-cost materials. This low-profile (below 2 millimeters in diameter) robotic fiber exhibits remarkable motion precision (below 50 micrometers) and repeatability. We developed control algorithms coupling the robot with endoscopic instruments, demonstrating high-resolution in situ molecular and morphological tissue mapping. We assess its practicality and safety during in vivo laparoscopic surgery on a porcine model. High-precision motion of the fiber robot delivered endoscopically facilitates the effective use of cellular-level intraoperative tissue identification and ablation technologies, potentially enabling precise removal of cancer in challenging surgical sites.

From biomimicry to robotic co-creation: rethinking the boundaries between nature and technology

This paper is an invitation to an interdisciplinary dialogue on new possibilities for integrating robotics, design, and nature. I ask: how can new cross-movements between bio-inspired science and design be fostered? How might we envision the future possible intersection between technology and nature? First, I recall key aspects of classical bioinspired engineering and highlight the role of nature in the emergence of technology. Second, I introduce a new approach to bioinspired engineering. In this approach, robots play an active role in design and construction, learning from material properties to form new shapes and thus reshaping design paradigms. The distinctive elements of this approach depart from classical nature-inspired engineering and foster a symbiotic relationship between technology and nature. I conclude by reflecting on the intersections of nature, technology, and design, and envisioning new avenues for interdisciplinary dialogue that foster collaboration and innovation among diverse bio-inspired disciplines.

Intelligent Navigation of a Magnetic Microrobot with Model-Free Deep Reinforcement Learning in a Real-World Environment

Microrobotics has opened new horizons for various applications, especially in medicine. However, it also witnessed challenges in achieving maximum optimal performance. One key challenge is the intelligent, autonomous, and precise navigation control of microrobots in fluid environments. The intelligence and autonomy in microrobot control, without the need for prior knowledge of the entire system, can offer significant opportunities in scenarios where their models are unavailable. In this study, two control systems based on model-free deep reinforcement learning were implemented to control the movement of a disk-shaped magnetic microrobot in a real-world environment. The training and results of an off-policy SAC algorithm and an on-policy TRPO algorithm revealed that the microrobot successfully learned the optimal path to reach random target positions. During training, the TRPO exhibited a higher sample efficiency and greater stability. The TRPO and SAC showed 100% and 97.5% success rates in reaching the targets in the evaluation phase, respectively. These findings offer basic insights into achieving intelligent and autonomous navigation control for microrobots to advance their capabilities for various applications.

Micro/nanosystems for controllable drug delivery to the brain

Drug delivery to the brain is crucial in the treatment for central nervous system disorders. While significant progress has been made in recent years, there are still major challenges in achieving controllable drug delivery to the brain. Unmet clinical needs arise from various factors, including controlled drug transport, handling large drug doses, methods for crossing biological barriers, the use of imaging guidance, and effective models for analyzing drug delivery. Recent advances in micro/nanosystems have shown promise in addressing some of these challenges. These include the utilization of microfluidic platforms to test and validate the drug delivery process in a controlled and biomimetic setting, the development of novel micro/nanocarriers for large drug loads across the blood-brain barrier, and the implementation of micro-intervention systems for delivering drugs through intraparenchymal or peripheral routes. In this article, we present a review of the latest developments in micro/nanosystems for controllable drug delivery to the brain. We also delve into the relevant diseases, biological barriers, and conventional methods. In addition, we discuss future prospects and the development of emerging robotic micro/nanosystems equipped with directed transportation, real-time image guidance, and closed-loop control.

GAHLS: an optimized graph analytics based high level synthesis framework

The urgent need for low latency, high-compute and low power on-board intelligence in autonomous systems, cyber-physical systems, robotics, edge computing, evolvable computing, and complex data science calls for determining the optimal amount and type of specialized hardware together with reconfigurability capabilities. With these goals in mind, we propose a novel comprehensive graph analytics based high level synthesis (GAHLS) framework that efficiently analyzes complex high level programs through a combined compiler-based approach and graph theoretic optimization and synthesizes them into message passing domain-specific accelerators. This GAHLS framework first constructs a compiler-assisted dependency graph (CaDG) from low level virtual machine (LLVM) intermediate representation (IR) of high level programs and converts it into a hardware friendly description representation. Next, the GAHLS framework performs a memory design space exploration while account for the identified computational properties from the CaDG and optimizing the system performance for higher bandwidth. The GAHLS framework also performs a robust optimization to identify the CaDG subgraphs with similar computational structures and aggregate them into intelligent processing clusters in order to optimize the usage of underlying hardware resources. Finally, the GAHLS framework synthesizes this compressed specialized CaDG into processing elements while optimizing the system performance and area metrics. Evaluations of the GAHLS framework on several real-life applications (e.g., deep learning, brain machine interfaces) demonstrate that it provides 14.27× performance improvements compared to state-of-the-art approaches such as LegUp 6.2.

Artificial Neuron Devices

Efforts to design devices emulating complex cognitive abilities and response processes of biological systems have long been a coveted goal. Recent advancements in flexible electronics, mirroring human tissue's mechanical properties, hold significant promise. Artificial neuron devices, hinging on flexible artificial synapses, bioinspired sensors, and actuators, are meticulously engineered to mimic the biological systems. However, this field is in its infancy, requiring substantial groundwork to achieve autonomous systems with intelligent feedback, adaptability, and tangible problem-solving capabilities. This review provides a comprehensive overview of recent advancements in artificial neuron devices. It starts with fundamental principles of artificial synaptic devices and explores artificial sensory systems, integrating artificial synapses and bioinspired sensors to replicate all five human senses. A systematic presentation of artificial nervous systems follows, designed to emulate fundamental human nervous system functions. The review also discusses potential applications and outlines existing challenges, offering insights into future prospects. We aim for this review to illuminate the burgeoning field of artificial neuron devices, inspiring further innovation in this captivating area of research.

Multistimuli-Responsive Actuators Derived from Natural Materials for Entirely Biodegradable and Programmable Untethered Soft Robots

Untethered soft robots have attracted growing attention due to their safe interaction with living organisms, good flexibility, and accurate remote control. However, the materials involved are often nonbiodegradable or are derived from nonrenewable resources, leading to serious environmental problems. Here, we report a biomass-based multistimuli-responsive actuator based on cuttlefish ink nanoparticles (CINPs), wood-derived cellulose nanofiber (CNF), and bioderived polylactic acid (PLA). Taking advantage of the good photothermal conversion performance and exceptionally hygroscopic sensitivity of the CINPs/CNF composite (CICC) layer and the opposite thermally induced deformation behavior between the CICC layer and PLA layer, the soft actuator exhibits reversible deformation behaviors under near-infrared (NIR) light, humidity, and temperature stimuli, respectively. By introducing patterned or alignment structures and combining them with a macroscopic reassembly strategy, diverse programmable shape-morphing from 2D to 3D such as letter-shape, coiling, self-folding, and more sophisticated 3D deformations have been demonstrated. All of these deformations can be successfully predicted by finite element analysis (FEA) . Furthermore, this actuator has been further applied as an untethered grasping robot, weightlifting robot, and climbing robot capable of climbing a vertical pole. Such actuators consisting entirely of biodegradable materials will offer a sustainable future for untethered soft robots.

Combining brain-computer interfaces with deep reinforcement learning for robot training: a feasibility study in a simulation environment

Deep reinforcement learning (RL) is used as a strategy to teach robot agents how to autonomously learn complex tasks. While sparsity is a natural way to define a reward in realistic robot scenarios, it provides poor learning signals for the agent, thus making the design of good reward functions challenging. To overcome this challenge learning from human feedback through an implicit brain-computer interface (BCI) is used. We combined a BCI with deep RL for robot training in a 3-D physical realistic simulation environment. In a first study, we compared the feasibility of different electroencephalography (EEG) systems (wet- vs. dry-based electrodes) and its application for automatic classification of perceived errors during a robot task with different machine learning models. In a second study, we compared the performance of the BCI-based deep RL training to feedback explicitly given by participants. Our findings from the first study indicate the use of a high-quality dry-based EEG-system can provide a robust and fast method for automatically assessing robot behavior using a sophisticated convolutional neural network machine learning model. The results of our second study prove that the implicit BCI-based deep RL version in combination with the dry EEG-system can significantly accelerate the learning process in a realistic 3-D robot simulation environment. Performance of the BCI-based trained deep RL model was even comparable to that achieved by the approach with explicit human feedback. Our findings emphasize the usage of BCI-based deep RL methods as a valid alternative in those human-robot applications where no access to cognitive demanding explicit human feedback is available.

The AI Act Grand Challenge shows how autonomous robots will be regulated

One of the winning teams of the EU AI Act Grand Challenge analyzes how the AI Act will regulate robots.

Bioinspired self-assembled colloidal collectives drifting in three dimensions underwater

Active matter systems feature a series of unique behaviors, including the emergence of collective self-assembly structures and collective migration. However, realizing collective entities formed by synthetic active matter in spaces without wall-bounded support makes it challenging to perform three-dimensional (3D) locomotion without dispersion. Inspired by the migration mechanism of plankton, we propose a bimodal actuation strategy in the artificial colloidal systems, i.e., combining magnetic and optical fields. The magnetic field triggers the self-assembly of magnetic colloidal particles to form a colloidal collective, maintaining numerous colloids as a dynamically stable entity. The optical field allows the colloidal collectives to generate convective flow through the photothermal effect, enabling them to use fluidic currents for 3D drifting. The collectives can perform 3D locomotion underwater, transit between the water-air interface, and have a controlled motion on the water surface. Our study provides insights into designing smart devices and materials, offering strategies for developing synthetic active matter capable of controllable collective movement in 3D space.

Submillimeter Multifunctional Ferromagnetic Fiber Robots for Navigation, Sensing, and Modulation

Small-scale robots capable of remote active steering and navigation offer great potential for biomedical applications. However, the current design and manufacturing procedure impede their miniaturization and integration of various diagnostic and therapeutic functionalities. Herein, submillimeter fiber robots that can integrate navigation, sensing, and modulation functions are presented. These fiber robots are fabricated through a scalable thermal drawing process at a speed of 4 meters per minute, which enables the integration of ferromagnetic, electrical, optical, and microfluidic composite with an overall diameter of as small as 250 µm and a length of as long as 150 m. The fiber tip deflection angle can reach up to 54o under a uniform magnetic field of 45 mT. These fiber robots can navigate through complex and constrained environments, such as artificial vessels and brain phantoms. Moreover, Langendorff mouse hearts model, glioblastoma micro platforms, and in vivo mouse models are utilized to demonstrate the capabilities of sensing electrophysiology signals and performing a localized treatment. Additionally, it is demonstrated that the fiber robots can serve as endoscopes with embedded waveguides. These fiber robots provide a versatile platform for targeted multimodal detection and treatment at hard-to-reach locations in a minimally invasive and remotely controllable manner.

Ions-Silica Percolated Ionic Dielectric Elastomer Actuator for Soft Robots

Soft robotics systems are currently under development using ionic electroactive polymers (i-EAP) as soft actuators for the human-machine interface. However, this endeavor has been impeded by the dilemma of reconciling the competing demands of force and strain in i-EAP actuators. Here, the authors present a novel design called "ions-silica percolated ionic dielectric elastomer (i-SPIDER)", which exhibits ionic liquid-confined silica microstructures that effectively resolve the chronic issue of conventional i-EAP actuators. The i-SPIDER actuator demonstrates remarkable electromechanical conversion capacity at low voltage, thanks to improved ion accumulation facilitated by interpreting electrode polarization at the electrolyte-electrode interface. This approach concurrently enhances both strain (by approximately 1.52%) and force (by roughly 1.06 mN) even at low Young's modulus (merely 5.9 MPa). Additionally, by demonstrating arachnid-inspired soft robots endowed with user-desired tasks through control of various form factors, the development of soft robots using the i-SPIDER that can concomitantly enhance strain and force holds promise as a compelling avenue for ushering in the next generation of miniaturized, low-powered soft robotics.

Actuation-enhanced multifunctional sensing and information recognition by magnetic artificial cilia arrays

Artificial cilia integrating both actuation and sensing functions allow simultaneously sensing environmental properties and manipulating fluids in situ, which are promising for environment monitoring and fluidic applications. However, existing artificial cilia have limited ability to sense environmental cues in fluid flows that have versatile information encoded. This limits their potential to work in complex and dynamic fluid-filled environments. Here, we propose a generic actuation-enhanced sensing mechanism to sense complex environmental cues through the active interaction between artificial cilia and the surrounding fluidic environments. The proposed mechanism is based on fluid-cilia interaction by integrating soft robotic artificial cilia with flexible sensors. With a machine learning-based approach, complex environmental cues such as liquid viscosity, environment boundaries, and distributed fluid flows of a wide range of velocities can be sensed, which is beyond the capability of existing artificial cilia. As a proof of concept, we implement this mechanism on magnetically actuated cilia with integrated laser-induced graphene-based sensors and demonstrate sensing fluid apparent viscosity, environment boundaries, and fluid flow speed with a reconfigurable sensitivity and range. The same principle could be potentially applied to other soft robotic systems integrating other actuation and sensing modalities for diverse environmental and fluidic applications.

Untethered Magnetic Soft Robot with Ultra-Flexible Wirelessly Rechargeable Micro-Supercapacitor as an Onboard Power Source

Soft robotics has developed rapidly in recent years as an emergent research topic, offering new avenues for various industrial and biomedical settings. Despite these advancements, its applicability is limited to locomotion and actuation due to the lack of an adequate charge storage system that can support the robot's sensory system in challenging conditions. Herein, an ultra-flexible, lightweight (≈50 milligrams), and wirelessly rechargeable micro-supercapacitor as an onboard power source for miniaturized soft robots, capable of powering a range of sensory is proposed. The simple and scalable direct laser combustion technique is utilized to fabricate the robust graphene-like carbon micro-supercapacitor (GLC-MSC) electrode. The GLC-MSC demonstrates superior areal capacitance (8.76 mF cm-2 ), and maintains its original capacitance even under extreme actuation frequency (1-30 Hz). As proof of conceptthe authors fabricate a fully integrated magnetic-soft robot that shows outstanding locomotion aptitude and charged wirelessly (up to 2.4 V within 25s), making it an ideal onboard power source for soft robotics.

Robotic ultrasound imaging: State-of-the-art and future perspectives

Ultrasound (US) is one of the most widely used modalities for clinical intervention and diagnosis due to the merits of providing non-invasive, radiation-free, and real-time images. However, free-hand US examinations are highly operator-dependent. Robotic US System (RUSS) aims at overcoming this shortcoming by offering reproducibility, while also aiming at improving dexterity, and intelligent anatomy and disease-aware imaging. In addition to enhancing diagnostic outcomes, RUSS also holds the potential to provide medical interventions for populations suffering from the shortage of experienced sonographers. In this paper, we categorize RUSS as teleoperated or autonomous. Regarding teleoperated RUSS, we summarize their technical developments, and clinical evaluations, respectively. This survey then focuses on the review of recent work on autonomous robotic US imaging. We demonstrate that machine learning and artificial intelligence present the key techniques, which enable intelligent patient and process-specific, motion and deformation-aware robotic image acquisition. We also show that the research on artificial intelligence for autonomous RUSS has directed the research community toward understanding and modeling expert sonographers' semantic reasoning and action. Here, we call this process, the recovery of the "language of sonography". This side result of research on autonomous robotic US acquisitions could be considered as valuable and essential as the progress made in the robotic US examination itself. This article will provide both engineers and clinicians with a comprehensive understanding of RUSS by surveying underlying techniques. Additionally, we present the challenges that the scientific community needs to face in the coming years in order to achieve its ultimate goal of developing intelligent robotic sonographer colleagues. These colleagues are expected to be capable of collaborating with human sonographers in dynamic environments to enhance both diagnostic and intraoperative imaging.

Programmable nanocomposites of cellulose nanocrystals and zwitterionic hydrogels for soft robotics

Stimuli-responsive hydrogels have garnered significant attention as a versatile class of soft actuators. Introducing anisotropic properties, and shape-change programmability to responsive hydrogels promises a host of opportunities in the development of soft robots. Herein we report the synthesis of pH-responsive hydrogel nanocomposites with predetermined microstructural anisotropy, shape-transformation, and self-healing. Our hydrogel nanocomposites are largely composed of zwitterionic monomers and asymmetric cellulose nanocrystals. While the zwitterionic nature of the network imparts both self-healing and cytocompatibility to our hydrogel nanocomposites, the shear-induced alignment of cellulose nanocrystals renders their anisotropic swelling and mechanical properties. Thanks to the self-healing properties, we utilized a cut-and-paste approach to program reversible, and complex deformation into our hydrogels. As a proof-of-concept, we demonstrated the transport of light cargo using tethered and untethered soft robots made from our hydrogels. We believe the proposed material system introduce a powerful toolbox for the development of future generations of biomedical soft robots.

Chemically fueling new microrobot abilities

A combustion-powered soft actuator takes microrobots to new heights and speeds.

Dexterous electrical-driven soft robots with reconfigurable chiral-lattice foot design

Dexterous locomotion, such as immediate direction change during fast movement or shape reconfiguration to perform diverse tasks, are essential animal survival strategies which have not been achieved in existing soft robots. Here, we present a kind of small-scale dexterous soft robot, consisting of an active dielectric elastomer artificial muscle and reconfigurable chiral-lattice foot, that enables immediate and reversible forward, backward and circular direction changes during fast movement under single voltage input. Our electric-driven soft robot with the structural design can be combined with smart materials to realize multimodal functions via shape reconfigurations under the external stimulus. We experimentally demonstrate that our dexterous soft robots can reach arbitrary points in a plane, form complex trajectories, or lower the height to pass through a narrow tunnel. The proposed structural design and shape reconfigurability may pave the way for next-generation autonomous soft robots with dexterous locomotion.

Biohybrid Soft Robots Powered by Myocyte: Current Progress and Future Perspectives

Myocyte-driven robots, a type of biological actuator that combines myocytes with abiotic systems, have gained significant attention due to their high energy efficiency, sensitivity, biocompatibility, and self-healing capabilities. These robots have a unique advantage in simulating the structure and function of human tissues and organs. This review covers the research progress in this field, detailing the benefits of myocyte-driven robots over traditional methods, the materials used in their fabrication (including myocytes and extracellular materials), and their properties and manufacturing techniques. Additionally, the review explores various control methods, robot structures, and motion types. Lastly, the potential applications and key challenges faced by myocyte-driven robots are discussed and summarized.

FlyDetector-Automated Monitoring Platform for the Visual-Motor Coordination of Honeybees in a Dynamic Obstacle Scene Using Digital Paradigm

Vision plays a crucial role in the ability of compound-eyed insects to perceive the characteristics of their surroundings. Compound-eyed insects (such as the honeybee) can change the optical flow input of the visual system by autonomously controlling their behavior, and this is referred to as visual-motor coordination (VMC). To analyze an insect's VMC mechanism in dynamic scenes, we developed a platform for studying insects that actively shape the optic flow of visual stimuli by adapting their flight behavior. Image-processing technology was applied to detect the posture and direction of insects' movement, and automatic control technology provided dynamic scene stimulation and automatic acquisition of perceptual insect behavior. In addition, a virtual mapping technique was used to reconstruct the visual cues of insects for VMC analysis in a dynamic obstacle scene. A simulation experiment at different target speeds of 1-12 m/s was performed to verify the applicability and accuracy of the platform. Our findings showed that the maximum detection speed was 8 m/s, and triggers were 95% accurate. The outdoor experiments showed that flight speed in the longitudinal axis of honeybees was more stable when facing dynamic barriers than static barriers after analyzing the change in geometric optic flow. Finally, several experiments showed that the platform can automatically and efficiently monitor honeybees' perception behavior, and can be applied to study most insects and their VMC.

Electrodeposited Superhydrophilic-Superhydrophobic Composites for Untethered Multi-Stimuli-Responsive Soft Millirobots

To navigate in complex and unstructured real-world environments, soft miniature robots need to possess multiple functions, including autonomous environmental sensing, self-adaptation, and multimodal locomotion. However, to achieve multifunctionality, artificial soft robots should respond to multiple stimuli, which can be achieved by multimaterial integration using facile and flexible fabrication methods. Here, a multimaterial integration strategy for fabricating soft millirobots that uses electrodeposition to integrate two inherently non-adherable materials, superhydrophilic hydrogels and superhydrophobic elastomers, together via gel roots is proposed. This approach enables the authors to electrodeposit sodium alginate hydrogel onto a laser-induced graphene-coated elastomer, which can then be laser cut into various shapes to function as multi-stimuli-responsive soft robots (MSRs). Each MSR can respond to six different stimuli to autonomously transform their shapes, and mimic flowers, vines, mimosas, and flytraps. It is demonstrated that MSRs can climb slopes, switch locomotion modes, self-adapt between air-liquid environments, and transport cargo between different environments. This multimaterial integration strategy enables creating untethered soft millirobots that have multifunctionality, such as environmental sensing, self-propulsion, and self-adaptation, paving the way for their future operation in complex real-world environments.

An embarrassingly simple approach for visual navigation of forest environments

Navigation in forest environments is a challenging and open problem in the area of field robotics. Rovers in forest environments are required to infer the traversability of a priori unknown terrains, comprising a number of different types of compliant and rigid obstacles, under varying lighting and weather conditions. The challenges are further compounded for inexpensive small-sized (portable) rovers. While such rovers may be useful for collaboratively monitoring large tracts of forests as a swarm, with low environmental impact, their small-size affords them only a low viewpoint of their proximal terrain. Moreover, their limited view may frequently be partially occluded by compliant obstacles in close proximity such as shrubs and tall grass. Perhaps, consequently, most studies on off-road navigation typically use large-sized rovers equipped with expensive exteroceptive navigation sensors. We design a low-cost navigation system tailored for small-sized forest rovers. For navigation, a light-weight convolution neural network is used to predict depth images from RGB input images from a low-viewpoint monocular camera. Subsequently, a simple coarse-grained navigation algorithm aggregates the predicted depth information to steer our mobile platform towards open traversable areas in the forest while avoiding obstacles. In this study, the steering commands output from our navigation algorithm direct an operator pushing the mobile platform. Our navigation algorithm has been extensively tested in high-fidelity forest simulations and in field trials. Using no more than a 16 × 16 pixel depth prediction image from a 32 × 32 pixel RGB image, our algorithm running on a Raspberry Pi was able to successfully navigate a total of over 750 m of real-world forest terrain comprising shrubs, dense bushes, tall grass, fallen branches, fallen tree trunks, small ditches and mounds, and standing trees, under five different weather conditions and four different times of day. Furthermore, our algorithm exhibits robustness to changes in the mobile platform's camera pitch angle, motion blur, low lighting at dusk, and high-contrast lighting conditions.

Robot swarms neutralize harmful Byzantine robots using a blockchain-based token economy

Through cooperation, robot swarms can perform tasks or solve problems that a single robot from the swarm could not perform/solve by itself. However, it has been shown that a single Byzantine robot (such as a malfunctioning or malicious robot) can disrupt the coordination strategy of the entire swarm. Therefore, a versatile swarm robotics framework that addresses security issues in inter-robot communication and coordination is urgently needed. Here, we show that security issues can be addressed by setting up a token economy between the robots. To create and maintain the token economy, we used blockchain technology, originally developed for the digital currency Bitcoin. The robots were given crypto tokens that allowed them to participate in the swarm's security-critical activities. The token economy was regulated via a smart contract that decided how to distribute crypto tokens among the robots depending on their contributions. We designed the smart contract so that Byzantine robots soon ran out of crypto tokens and could therefore no longer influence the rest of the swarm. In experiments with up to 24 physical robots, we demonstrated that our smart contract approach worked: The robots could maintain blockchain networks, and a blockchain-based token economy could be used to neutralize the destructive actions of Byzantine robots in a collective-sensing scenario. In experiments with more than 100 simulated robots, we studied the scalability and long-term behavior of our approach. The obtained results demonstrate the feasibility and viability of blockchain-based swarm robotics.

Lab2Field transfer of a robotic raspberry harvester enabled by a soft sensorized physical twin

Robotic fruit harvesting requires dexterity to handle delicate crops and development relying upon field testing possible only during the harvesting season. Here we focus on raspberry crops, and explore how the research methodology of harvesting robots can be accelerated through soft robotic technologies. We propose and demonstrate a physical twin of the harvesting environment: a sensorized physical simulator of a raspberry plant with tunable properties, used to train a robotic harvester in the laboratory regardless of season. The sensors on the twin allow for direct comparison with human demonstrations, used to tune the robot controllers. In early field demonstrations, an 80% harvesting success rate was achieved without any modifications on the lab trained robot.

Kai Junge and colleagues designed a soft sensorized physical twin of a raspberry plant which they use to collect force data on fruit picking to train a robotic harvester. Early field demonstrations showed promise in rapid training of a robot for the delicate task of soft fruit picking.

Complex Three-Dimensional Terrains Traversal of Insect-Scale Soft Robot

This article proposes a piezoelectric-driven insect-scale soft robot with ring-like curved legs, enabling it to traverse complex three-dimensional (3D) terrain only by body-terrain mechanical action. Relying on the repeated deformation of the main body's n and u shapes, the robot's leg-ground mechanical action produces an "elastic gait" to move. Regarding the detailed design, first, a theoretical curve of the front leg with a fixed angle of attack of 75° is designed by finite element simulation and comparative experiments. It ensures no increase in drag and no decrease in the lift when climbing steps. Second, a ring-like leg structure with 100% closed degree is proposed to ensure a smooth pass through small-sized uneven terrain without getting stuck. Then, the design of the overall asymmetrical structure of the robot can improve the conversion ratio of vibration to forward force. The shape of curved legs is controlled by pulling the flexible leg structure with two metal wires working as spokes. The semirigid leg structure made of fully flexible materials has shape stability and structural robustness. Compared with the plane-legged robot, the curved-legged robot can smoothly traverse different rugged 3D terrains and cross the terrain covering obstacles 0.36 times body height (BH) at a speed of >4 body lengths per second. Moreover, the curved-legged robot shows 100% and 64% chances of climbing steps with 1.2- and 1.9-times BH, respectively.

The neuromechanics of animal locomotion: From biology to robotics and back

Robotics and neuroscience are sister disciplines that both aim to understand how agile, efficient, and robust locomotion can be achieved in autonomous agents. Robotics has already benefitted from neuromechanical principles discovered by investigating animals. These include the use of high-level commands to control low-level central pattern generator-like controllers, which, in turn, are informed by sensory feedback. Reciprocally, neuroscience has benefited from tools and intuitions in robotics to reveal how embodiment, physical interactions with the environment, and sensory feedback help sculpt animal behavior. We illustrate and discuss exemplar studies of this dialog between robotics and neuroscience. We also reveal how the increasing biorealism of simulations and robots is driving these two disciplines together, forging an integrative science of autonomous behavioral control with many exciting future opportunities.

Coordination tending towards an anti-phase relationship determines greater sway reduction during entrainment with a simulated partner

The increased risk of falls in the older aged population demands the development of assistive robotic devices capable of effective balance support. For the development and increased user acceptance of such devices, which provide balance support in a human-like way, it is important to understand the simultaneous occurrence of entrainment and sway reduction in human-human interaction. However, sway reduction has not been observed yet during a human touching an external, continuously moving reference, which rather increased human body sway. Therefore, we investigated in 15 healthy young adults (27.20±3.55 years, 6 females) how different simulated sway-responsive interaction partners with different coupling modes affect sway entrainment, sway reduction and relative interpersonal coordination, as well as how these human behaviours differ depending on the individual body schema accuracy. For this, participants were lightly touching a haptic device that either played back an average pre-recorded sway trajectory ("Playback") or moved based on the sway trajectory simulated by a single-inverted pendulum model with either a positive (Attractor) or negative (Repulsor) coupling to participant's body sway. We found that body sway reduced not only during the Repulsor-interaction, but also during the Playback-interaction. These interactions also showed a relative interpersonal coordination tending more towards an anti-phase relationship, especially the Repulsor. Moreover, the Repulsor led to the strongest sway entrainment. Finally, a better body schema contributed to a reduced body sway in both the "reliable" Repulsor and the "less reliable" Attractor mode. Consequently, a relative interpersonal coordination tending more towards an anti-phase relationship and an accurate body schema are important to facilitate sway reduction.

Drug-Free Antimicrobial Nanomotor for Precise Treatment of Multidrug-Resistant Bacterial Infections

Manufacturing heteronanostructures with specific physicochemical characteristics and tightly controllable designs is very appealing. Herein, we reported NIR-II light-driven dual plasmonic (AuNR-SiO₂-Cu₇S₄) antimicrobial nanomotors with an intended Janus configuration through the overgrowth of copper-rich Cu₇S₄ nanocrystals at only one high-curvature site of Au nanorods (Au NRs). These nanomotors were applied for photoacoustic imaging (PAI)-guided synergistic photothermal and photocatalytic treatment of bacterial infections. Both the photothermal performance and photocatalytic activity of the nanomotors are dramatically improved owing to the strong plasmon coupling between Au NRs and the Cu₇S₄ component and enhanced energy transfer. The motion behavior of nanomotors promotes transdermal penetration and enhances the matter-bacteria interaction. More importantly, the directional navigation and synergistic antimicrobial activity of the nanomotors could be synchronously driven by NIR-II light. The marriage of active motion and enhanced antibacterial activity resulted in the expected good antibacterial effects in an abscess infection mouse model.

Recent Developments in the Theory and Applicability of Swarm Search

Swarm intelligence (SI) is a collective behaviour exhibited by groups of simple agents, such as ants, bees, and birds, which can achieve complex tasks that would be difficult or impossible for a single individual [...].

Nanomaterial-decorated micromotors for enhanced photoacoustic imaging

Micro-and nanorobots have the potential to perform non-invasive drug delivery, sensing, and surgery in living organisms, with the aid of diverse medical imaging techniques. To perform such actions, microrobots require high spatiotemporal resolution tracking with real-time closed-loop feedback. To that end,  photoacoustic imaging has appeared as a promising technique for imaging microrobots in deep tissue with higher molecular specificity and contrast. Here, we present different strategies to track magnetically-driven micromotors with improved contrast and specificity using dedicated contrast agents (Au nanorods and nanostars). Furthermore, we discuss the possibility of improving the light absorption properties of the employed nanomaterials considering possible light scattering and coupling to the underlying metal-oxide layers on the micromotor's surface. For that, 2D COMSOL simulation and experimental results were correlated, confirming that an increased spacing between the Au-nanostructures and the increase of thickness of the underlying oxide layer lead to enhanced light absorption and preservation of the characteristic absorption peak. These characteristics are important when visualizing the micromotors in a complex in vivo environment, to distinguish them from the light absorption properties of the surrounding natural chromophores.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12213-023-00156-7.

Exploiting compositional disorder in collectives of light-driven circle walkers

Emergent behavior in collectives of "robotic" units with limited capabilities that is robust and programmable is a promising route to perform tasks on the micro and nanoscale that are otherwise difficult to realize. However, a comprehensive theoretical understanding of the physical principles, in particular steric interactions in crowded environments, is still largely missing. Here, we study simple light-driven walkers propelled through internal vibrations. We demonstrate that their dynamics is well captured by the model of active Brownian particles, albeit with an angular speed that differs between individual units. Transferring to a numerical model, we show that this polydispersity of angular speeds gives rise to specific collective behavior: self-sorting under confinement and enhancement of translational diffusion. Our results show that, while naively perceived as imperfection, disorder of individual properties can provide another route to realize programmable active matter.

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.

Liquid Crystal Elastomer Based Dexterous Artificial Motor Unit

Despite the great advancement in designing diverse soft robots, they are not yet as dexterous as animals in many aspects. One challenge is that they still lack the compact design of an artificial motor unit with a great comprehensive performance that can be conveniently fabricated, although many recently developed artificial muscles have shown excellent properties in one or two aspects. Herein, an artificial motor unit is developed based on gold-coated ultrathin liquid crystal elastomer (LCE) film. Subject to a voltage, Joule heating generated by the gold film increases the temperature of the LCE film underneath and causes it to contract. Due to the small thermal inertial and electrically controlling method of the ultrathin LCE structure, its cyclic actuation speed is fast and controllable. It is shown that under electrical stimulation, the actuation strain of the LCE-based motor unit reaches 45%, the strain rate reaches 750%/s, and the output power density is as high as 1360 W kg^-1 . It is further demonstrated that the LCE-based motor unit behaves like an actuator, a brake, or a nonlinear spring on demand, analogous to most animal muscles. Finally, as a proof-of-concept, multiple highly dexterous artificial neuromuscular systems are demonstrated using the LCE-based motor unit.

Synaptic Resistor Circuits Based on Al Oxide and Ti Silicide for Concurrent Learning and Signal Processing in Artificial Intelligence Systems

Neurobiological circuits containing synapses can process signals while learning concurrently in real time. Before an artificial neural network (ANN) can execute a signal-processing program, it must first be programmed by humans or trained with respect to a large and defined data set during learning processes, resulting in significant latency, high power consumption, and poor adaptability to unpredictable changing environments. In this work, a crossbar circuit of synaptic resistors (synstors) is reported, each synstor integrating a Si channel with an Al oxide memory layer and Ti silicide Schottky contacts. Individual synstors are characterized and analyzed to understand their concurrent signal-processing and learning abilities. Without any prior training, synstor circuits concurrently execute signal processing and learning in real time to fly drones toward a target position in an aerodynamically changing environment faster than human controllers, and with learning speed, performance, power consumption, and adaptability to the environment significantly superior to an ANN running on computers. The synstor circuit provides a path to establish power-efficient intelligent systems with real-time learning and adaptability in the capriciously mutable real world.

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.