The grand challenges of Science Robotics

Nature-Inspired Intelligent Computing: A Comprehensive Survey

Nature, with its numerous surprising rules, serves as a rich source of creativity for the development of artificial intelligence, inspiring researchers to create several nature-inspired intelligent computing paradigms based on natural mechanisms. Over the past decades, these paradigms have revealed effective and flexible solutions to practical and complex problems. This paper summarizes the natural mechanisms of diverse advanced nature-inspired intelligent computing paradigms, which provide valuable lessons for building general-purpose machines capable of adapting to the environment autonomously. According to the natural mechanisms, we classify nature-inspired intelligent computing paradigms into 4 types: evolutionary-based, biological-based, social-cultural-based, and science-based. Moreover, this paper also illustrates the interrelationship between these paradigms and natural mechanisms, as well as their real-world applications, offering a comprehensive algorithmic foundation for mitigating unreasonable metaphors. Finally, based on the detailed analysis of natural mechanisms, the challenges of current nature-inspired paradigms and promising future research directions are presented.

Hybrid Zwitterionic Hydrogels with Encoded Differential Swelling and Programmed Deformation for Small-Scale Robotics

Stimuli-responsive shape-morphing hydrogels with self-healing and tunable physiochemical properties are excellent candidates for functional building blocks of untethered small-scale soft robots. With mechanical properties similar to soft organs and tissues, such robots enable minimally invasive medical procedures, such as cargo/cell transportation. In this work, responsive hydrogels based on zwitterionic/acrylate chemistry with self-healing and stimuli-responsiveness are synthesized. Such hydrogels are then judiciously cut and pasted to form hybrid constructs with predetermined swelling and elastic anisotropy. This method is used to program hydrogel constructs with predetermined 2D-to-3D deformation upon exposure to different environmental ionic strengths. Untethered soft robotic functionalities are demonstrated, such as actuation, magnetic locomotion, and targeted transport of soft and light cargo in flooded media. The proposed hydrogel expands the repertoire of functional materials for fabricating small-scale soft robots.

A droplet robotic system enabled by electret-induced polarization on droplet

Robotics for scientific research are evolving from grasping macro-scale solid materials to directly actuating micro-scale liquid samples. However, current liquid actuation mechanisms often restrict operable liquid types or compromise the activity of biochemical samples by introducing interfering mediums. Here, we propose a robotic liquid handling system enabled by a novel droplet actuation mechanism, termed electret-induced polarization on droplet (EPD). EPD enables all-liquid actuation in principle and experimentally exhibits generality for actuating various inorganic/organic liquids with relative permittivity ranging from 2.25 to 84.2 and volume from 500 nL to 1 mL. Moreover, EPD is capable of actuating various biochemical samples without compromising their activities, including various body fluids, living cells, and proteins. A robotic system is also coupled with the EPD mechanism to enable full automation. EPD's high adaptability with liquid types and biochemical samples thus promotes the automation of liquid-based scientific experiments across multiple disciplines.

Delivering Microrobots in the Musculoskeletal System

Disorders of the musculoskeletal system are the major contributors to the global burden of disease and current treatments show limited efficacy. Patients often suffer chronic pain and might eventually have to undergo end-stage surgery. Therefore, future treatments should focus on early detection and intervention of regional lesions. Microrobots have been gradually used in organisms due to their advantages of intelligent, precise and minimally invasive targeted delivery. Through the combination of control and imaging systems, microrobots with good biosafety can be delivered to the desired area for treatment. In the musculoskeletal system, microrobots are mainly utilized to transport stem cells/drugs or to remove hazardous substances from the body. Compared to traditional biomaterial and tissue engineering strategies, active motion improves the efficiency and penetration of local targeting of cells/drugs. This review discusses the frontier applications of microrobotic systems in different tissues of the musculoskeletal system. We summarize the challenges and barriers that hinder clinical translation by evaluating the characteristics of different microrobots and finally point out the future direction of microrobots in the musculoskeletal system.

Crawling, climbing, perching, and flying by FiBa soft robots

This paper introduces an approach to fabricating lightweight, untethered soft robots capable of diverse biomimetic locomotion. Untethering soft robotics from electrical or pneumatic power remains one of the prominent challenges within the field. The development of functional untethered soft robotic systems hinges heavily on mitigating their weight; however, the conventional weight of pneumatic network actuators (pneu-nets) in soft robots has hindered untethered operations. To address this challenge, we developed film-balloon (FiBa) modules that drastically reduced the weight of soft actuators. FiBa modules combine transversely curved polymer thin films and three-dimensionally printed pneumatic balloons to achieve varied locomotion modes. These lightweight FiBa modules serve as building blocks to create untethered soft robots mimicking natural movement strategies. These modules substantially reduce overall robot weight, allowing the integration of components such as pumps, valves, batteries, and control boards, thereby enabling untethered operation. FiBa modules integrated with electronic components demonstrated four bioinspired modes of locomotion, including turtle-inspired crawling, inchworm-inspired climbing, bat-inspired perching, and ladybug-inspired flying. Overall, our study offers an alternative tool for designing and customizing lightweight, untethered soft robots with advanced functionalities. The reduction of the weight of soft robots enabled by our approach opens doors to a wide range of applications, including disaster relief, space exploration, remote sensing, and search and rescue operations, where lightweight, untethered soft robotic systems are essential.

Intelligent Rock-Climbing Robot Capable of Multimodal Locomotion and Hybrid Bioinspired Attachment

Rock-climbing robots have significant potential in fieldwork and planetary exploration. However, they currently face limitations such as a lack of stability and adaptability on extreme terrains, slow locomotion, and single functionality. This study introduces a novel multimodal and adaptive rock-climbing robot (MARCBot), which addresses these limitations through spiny grippers that draw inspiration from morpho-functionalities observed in beetles, arboreal birds, and hoofed animals. This hybrid bioinspired design enables high attachment strength, passive adaptability to different terrains, and quick attachment on rock surfaces. The multimodal functionality of the gripper allows for attachment during climbing and support during walking. A novel control strategy using dynamics and quadratic programming (QP) optimizes attachment wrench distribution, reducing cost-of-transport by 20.03% and 6.05% compared to closed-loop inverse kinematic (CLIK) and virtual model control (VMC) methods, respectively. MARCBot achieved climbing speeds of 0.15 m min-1 on a vertical discrete rock surface under gravity and trotting speeds of up to 0.21 m s-1 on various complex terrains. It is the first robot capable of climbing on rock surfaces and trotting in complex terrains without the need for switching end-effectors. This study highlights significant advancements in climbing and multimodal locomotion for robots in extreme environments.

Hybrid YSGOA and neural networks based software failure prediction in cloud systems

In the realm of cloud computing, ensuring the dependability and robustness of software systems is paramount. The intricate and evolving nature of cloud infrastructures, however, presents substantial obstacles in the pre-emptive identification and rectification of software anomalies. This study introduces an innovative methodology that amalgamates hybrid optimization algorithms with Neural Networks (NN) to refine the prediction of software malfunctions. The core objective is to augment the purity metric of our method across diverse operational conditions. This is accomplished through the utilization of two distinct optimization algorithms: the Yellow Saddle Goat Fish Algorithm (YSGA), which is instrumental in the discernment of pivotal features linked to software failures, and the Grasshopper Optimization Algorithm (GOA), which further polishes the feature compilation. These features are then processed by Neural Networks (NN), capitalizing on their proficiency in deciphering intricate data patterns and interconnections. The NNs are integral to the classification of instances predicated on the ascertained features. Our evaluation, conducted using the Failure-Dataset-OpenStack database and MATLAB Software, demonstrates that the hybrid optimization strategy employed for feature selection significantly curtails complexity and expedites processing.

Socially adaptive cognitive architecture for human-robot collaboration in industrial settings

This paper introduces DAC-HRC, a novel cognitive architecture designed to optimize human-robot collaboration (HRC) in industrial settings, particularly within the context of Industry 4.0. The architecture is grounded in the Distributed Adaptive Control theory and the principles of joint intentionality and interdependence, which are key to effective HRC. Joint intentionality refers to the shared goals and mutual understanding between a human and a robot, while interdependence emphasizes the reliance on each other's capabilities to complete tasks. DAC-HRC is applied to a hybrid recycling plant for the disassembly and recycling of Waste Electrical and Electronic Equipment (WEEE) devices. The architecture incorporates several cognitive modules operating at different timescales and abstraction levels, fostering adaptive collaboration that is personalized to each human user. The effectiveness of DAC-HRC is demonstrated through several pilot studies, showcasing functionalities such as turn-taking interaction, personalized error-handling mechanisms, adaptive safety measures, and gesture-based communication. These features enhance human-robot collaboration in the recycling plant by promoting real-time robot adaptation to human needs and preferences. The DAC-HRC architecture aims to contribute to the development of a new HRC paradigm by paving the way for more seamless and efficient collaboration in Industry 4.0 by relying on socially adept cognitive architectures.

Bio-inspired multimodal learning with organic neuromorphic electronics for behavioral conditioning in robotics

Biological systems interact directly with the environment and learn by receiving multimodal feedback via sensory stimuli that shape the formation of internal neuronal representations. Drawing inspiration from biological concepts such as exploration and sensory processing that eventually lead to behavioral conditioning, we present a robotic system handling objects through multimodal learning. A small-scale organic neuromorphic circuit locally integrates and adaptively processes multimodal sensory stimuli, enabling the robot to interact intelligently with its surroundings. The real-time handling of sensory stimuli via low-voltage organic neuromorphic devices with synaptic functionality forms multimodal associative connections that lead to behavioral conditioning, and thus the robot learns to avoid potentially dangerous objects. This work demonstrates that adaptive neuro-inspired circuitry with multifunctional organic materials, can accommodate locally efficient bio-inspired learning for advancing intelligent robotics.

Multiple Magneto-Optical Microrobotic Collectives with Selective Control in Three Dimensions Under Water

Inspired by natural swarms, various methods are developed to create artificial magnetic microrobotic collectives. However, these magnetic collectives typically receive identical control inputs from a common external magnetic field, limiting their ability to operate independently. And they often rely on interfaces or boundaries for controlled movement, posing challenges for independent, three-dimensional(3D) navigation of multiple magnetic collectives. To address this challenge, self-assembled microrobotic collectives are proposed that can be selectively actuated in a combination of external magnetic and optical fields. By harnessing both actuation methods, the constraints of single actuation approaches are overcome. The magnetic field excites the self-assembly of colloids and maintains the self-assembled microrobotic collectives without disassembly, while the optical field drives selected microrobotic collectives to perform different tasks. The proposed magnetic-photo microrobotic collectives can achieve independent position and path control in the two-dimensional (2D) plane and 3D space. With this selective control strategy, the microrobotic collectives can cooperate in convection and mixing the dye in a confined space. The results present a systematic approach for realizing selective control of multiple microrobotic collectives, which can address multitasking requirements in complex environments.

Steering Muscle-Based Bio-Syncretic Robot Through Bionic Optimized Biped Mechanical Design

Bio-syncretic robots consisting of artificial structures and living muscle cells have attracted much attention owing to their potential advantages, such as high drive efficiency, miniaturization, and compatibility. Motion controllability, as an important factor related to the main performance of bio-syncretic robots, has been explored in numerous studies. However, most of the existing bio-syncretic robots still face challenges related to the further development of steerable kinematic dexterity. In this study, a bionic optimized biped fully soft bio-syncretic robot actuated by two muscle tissues and steered with a direction-controllable electric field generated by external circularly distributed multiple electrodes has been developed. The developed bio-syncretic robot could realize wirelessly steerable motion and effective transportation of microparticle cargo on artificial polystyrene and biological pork tripe surfaces. This study may provide an effective strategy for the development of bio-syncretic robots and other related studies, such as nonliving soft robot design and muscle tissue engineering.

Fiber Actuators Based on Reversible Thermal Responsive Liquid Crystal Elastomer

Soft actuators inspired by the movement of organisms have attracted extensive attention in the fields of soft robotics, electronic skin, artificial intelligence, and healthcare due to their excellent adaptability and operational safety. Liquid crystal elastomer fiber actuators (LCEFAs) are considered as one of the most promising soft actuators since they can provide reversible linear motion and are easily integrated or woven into complex structures to perform pre-programmed movements such as stretching, rotating, bending, and expanding. The research on LCEFAs mainly focuses on controllable preparation, structural design, and functional applications. This review, for the first time, provides a comprehensive and systematic review of recent advances in this important field by focusing on reversible thermal response LCEFAs. First, the thermal driving mechanism, and direct and indirect heating strategies of LCEFAs are systematically summarized and analyzed. Then, the fabrication methods and functional applications of LCEFAs are summarized and discussed. Finally, the challenges and technical difficulties that may hinder the performance improvement and large-scale production of LCEFAs are proposed, and the development opportunities of LCEFAs are prospected.

Roadmap for Clinical Translation of Mobile Microrobotics

Medical microrobotics is an emerging field to revolutionize clinical applications in diagnostics and therapeutics of various diseases. On the other hand, the mobile microrobotics field has important obstacles to pass before clinical translation. This article focuses on these challenges and provides a roadmap of medical microrobots to enable their clinical use. From the concept of a "magic bullet" to the physicochemical interactions of microrobots in complex biological environments in medical applications, there are several translational steps to consider. Clinical translation of mobile microrobots is only possible with a close collaboration between clinical experts and microrobotics researchers to address the technical challenges in microfabrication, safety, and imaging. The clinical application potential can be materialized by designing microrobots that can solve the current main challenges, such as actuation limitations, material stability, and imaging constraints. The strengths and weaknesses of the current progress in the microrobotics field are discussed and a roadmap for their clinical applications in the near future is outlined.

Autonomous assembly and disassembly of gliding molecular robots regulated by a DNA-based molecular controller

In recent years, there has been a growing interest in engineering dynamic and autonomous systems with robotic functionalities using biomolecules. Specifically, the ability of molecular motors to convert chemical energy to mechanical forces and the programmability of DNA are regarded as promising components for these systems. However, current systems rely on the manual addition of external stimuli, limiting the potential for autonomous molecular systems. Here, we show that DNA-based cascade reactions can act as a molecular controller that drives the autonomous assembly and disassembly of DNA-functionalized microtubules propelled by kinesins. The DNA controller is designed to produce two different DNA strands that program the interaction between the microtubules. The gliding microtubules integrated with the controller autonomously assemble to bundle-like structures and disassemble into discrete filaments without external stimuli, which is observable by fluorescence microscopy. We believe this approach to be a starting point toward more autonomous behavior of motor protein-based multicomponent systems with robotic functionalities.

Perception of experience influences altruism and perception of agency influences trust in human-machine interactions

As robots become increasingly integrated into social economic interactions, it becomes crucial to understand how people perceive a robot's mind. It has been argued that minds are perceived along two dimensions: experience, i.e., the ability to feel, and agency, i.e., the ability to act and take responsibility for one's actions. However, the influence of these perceived dimensions on human-machine interactions, particularly those involving altruism and trust, remains unknown. We hypothesize that the perception of experience influences altruism, while the perception of agency influences trust. To test these hypotheses, we pair participants with bot partners in a dictator game (to measure altruism) and a trust game (to measure trust) while varying the bots' perceived experience and agency, either by manipulating the degree to which the bot resembles humans, or by manipulating the description of the bots' ability to feel and exercise self-control. The results demonstrate that the money transferred in the dictator game is influenced by the perceived experience, while the money transferred in the trust game is influenced by the perceived agency, thereby confirming our hypotheses. More broadly, our findings support the specificity of the mind hypothesis: Perceptions of different dimensions of the mind lead to different kinds of social behavior.

Advances in the Application of AI Robots in Critical Care: Scoping Review

BACKGROUND

In recent epochs, the field of critical medicine has experienced significant advancements due to the integration of artificial intelligence (AI). Specifically, AI robots have evolved from theoretical concepts to being actively implemented in clinical trials and applications. The intensive care unit (ICU), known for its reliance on a vast amount of medical information, presents a promising avenue for the deployment of robotic AI, anticipated to bring substantial improvements to patient care.

OBJECTIVE

This review aims to comprehensively summarize the current state of AI robots in the field of critical care by searching for previous studies, developments, and applications of AI robots related to ICU wards. In addition, it seeks to address the ethical challenges arising from their use, including concerns related to safety, patient privacy, responsibility delineation, and cost-benefit analysis.

METHODS

Following the scoping review framework proposed by Arksey and O'Malley and the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, we conducted a scoping review to delineate the breadth of research in this field of AI robots in ICU and reported the findings. The literature search was carried out on May 1, 2023, across 3 databases: PubMed, Embase, and the IEEE Xplore Digital Library. Eligible publications were initially screened based on their titles and abstracts. Publications that passed the preliminary screening underwent a comprehensive review. Various research characteristics were extracted, summarized, and analyzed from the final publications.

RESULTS

Of the 5908 publications screened, 77 (1.3%) underwent a full review. These studies collectively spanned 21 ICU robotics projects, encompassing their system development and testing, clinical trials, and approval processes. Upon an expert-reviewed classification framework, these were categorized into 5 main types: therapeutic assistance robots, nursing assistance robots, rehabilitation assistance robots, telepresence robots, and logistics and disinfection robots. Most of these are already widely deployed and commercialized in ICUs, although a select few remain under testing. All robotic systems and tools are engineered to deliver more personalized, convenient, and intelligent medical services to patients in the ICU, concurrently aiming to reduce the substantial workload on ICU medical staff and promote therapeutic and care procedures. This review further explored the prevailing challenges, particularly focusing on ethical and safety concerns, proposing viable solutions or methodologies, and illustrating the prospective capabilities and potential of AI-driven robotic technologies in the ICU environment. Ultimately, we foresee a pivotal role for robots in a future scenario of a fully automated continuum from admission to discharge within the ICU.

CONCLUSIONS

This review highlights the potential of AI robots to transform ICU care by improving patient treatment, support, and rehabilitation processes. However, it also recognizes the ethical complexities and operational challenges that come with their implementation, offering possible solutions for future development and optimization.

Passive Polarized Vision for Autonomous Vehicles: A Review

This review article aims to address common research questions in passive polarized vision for robotics. What kind of polarization sensing can we embed into robots? Can we find our geolocation and true north heading by detecting light scattering from the sky as animals do? How should polarization images be related to the physical properties of reflecting surfaces in the context of scene understanding? This review article is divided into three main sections to address these questions, as well as to assist roboticists in identifying future directions in passive polarized vision for robotics. After an introduction, three key interconnected areas will be covered in the following sections: embedded polarization imaging; polarized vision for robotics navigation; and polarized vision for scene understanding. We will then discuss how polarized vision, a type of vision commonly used in the animal kingdom, should be implemented in robotics; this type of vision has not yet been exploited in robotics service. Passive polarized vision could be a supplemental perceptive modality of localization techniques to complement and reinforce more conventional ones.

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.