The grand challenges of Science Robotics

Field-Driven Out-of-Equilibrium Collective Patterns for Swarm Micro-Robotics

Soft robotics has been rapidly advancing, offering significant improvements over traditional rigid robotic systems through the use of compliant materials that enhance adaptability and interaction with the environment. However, current approaches face critical challenges, including the reliance on complex "top-down" fabrication techniques and the difficulty of wireless powering and control at the microscale. Swarm robotics introduces a paradigm shift, leveraging collective dynamics to achieve cooperative and adaptable behaviors among multiple robotic units. Inspired by nature, this "bottom-up" approach enables swarm robots to execute task-specific reconfigurations, enhancing flexibility and robustness. Field-driven active colloids emerge as a promising platform for swarm microrobotics, capable of self-propulsion and self-organization into dynamic collective patterns under external field excitation and manipulation. These systems mimic biologically inspired swarm behaviors, such as flocking and vortex formation, providing a versatile foundation for designing innovative swarm microrobots. This review discusses the principles of electric and magnetic field-driven collective self-organization, focusing on the particle dynamics, the emergence of collective swarm patterns, and illustrative examples of functional swarm microrobots. It concludes with future perspectives on harnessing these systems for adaptive, scalable, and multifunctional microrobotic applications.

A synchronous control strategy of robot social behavior driven by scenario information and neural modulation mechanism

Robots have been widely employed in scenarios that involve various environmental factors and social individuals. As one kind of social companion, robot is supposed to obey human social protocol and display anthropomorphic behaviors. In this paper, we focus on the problem of robot behavior control in multi-individuals scenarios, and build a coordinated robot behavior model containing body movement/orientation, head rotation and eyeball movement. Within the proposed model, a synchronous control strategy based on social space theory and neural modulation mechanism is proposed. This strategy collects RGB-D camera stream and acoustic field data perceived from multi-individuals scenario, and controls the robot to complete movement and social gaze behaviors. As for the eye-head coordinated gaze behavior, it is modulated by a novel optimal control algorithm based on the minimum neural transmission noise. Above works are validated on the Xiaopang robot platform, the experimental observations indicate that the robot can achieve anthropomorphic response in dynamic multi-individuals scenario. Within above promising results, the effectiveness of this strategies could be proven.

Reverse engineering the control law for schooling in zebrafish using virtual reality

Revealing the evolved mechanisms that give rise to collective behavior is a central objective in the study of cellular and organismal systems. In addition, understanding the algorithmic basis of social interactions in a causal and quantitative way offers an important foundation for subsequently quantifying social deficits. Here, with virtual reality technology, we used virtual robot fish to reverse engineer the sensory-motor control of social response during schooling in a vertebrate model: juvenile zebrafish (Danio rerio). In addition to providing a highly controlled means to understand how zebrafish translate visual input into movement decisions, networking our systems allowed real fish to swim and interact together in the same virtual world. Thus, we were able to directly test models of social interactions in situ. A key feature of social response is shown to be single- and multitarget-oriented pursuit. This is based on an egocentric representation of the positional information of conspecifics and is highly robust to incomplete sensory input. We demonstrated, including with a Turing test and a scalability test for pursuit behavior, that all key features of this behavior are accounted for by individuals following a simple experimentally derived proportional derivative control law, which we termed "BioPD." Because target pursuit is key to effective control of autonomous vehicles, we evaluated-as a proof of principle-the potential use of this simple evolved control law for human-engineered systems. In doing so, we found close-to-optimal pursuit performance in autonomous vehicle (terrestrial, airborne, and watercraft) pursuit while requiring limited system-specific tuning or optimization.

Multifunctional Magnetic Catheter Robot with Triaxial Force Sensing Capability for Minimally Invasive Surgery

Magnetic continuum robots offer flexibility and controllability, making them promising for minimally invasive surgery (MIS). However, the clinical application of these robots is relatively limited due to the difficulty of integrating miniaturized triaxial force sensors and their single functionality. This paper proposes a multifunctional magnetic catheter robot with magnetic actuation steering and triaxial force-sensing capabilities. The robot features 3 channels at its tip that integrate multi-segmented magnets, a novel triaxial force sensor, and various functional instruments. The sensor is calibrated, demonstrating high sensitivity and accuracy. The steering characterization of the robot confirms that the catheter tip exhibits effective flexibility and force sensing. Palpation experiments involving various hard lumps are performed on porcine kidney, with results verifying that the robot can reliably detect abnormal hard lumps within tissues. Additionally, palpation experiments in bronchial phantom demonstrate the robot's imaging and palpation capabilities for lung nodules with an integrated endoscope. Further, the robot, equipped with biopsy forceps, successfully performs palpation and biopsy functions on simulated stomach polyps, demonstrating its capability for effective tissue manipulation. By leveraging force-sensing capabilities and integrating multifunctional instruments, the robot shows potential for expanded applications in MIS, paving the way for important advancements in clinical procedures.

Robotic micromotors transforming oral drug administration

Oral medication is preferred for its convenience; however, efficient drug delivery remains challenging due to issues such as poor solubility, and absorption caused by mucosal barriers, which result in low bioavailability. In this review, we discuss new strategies integrating robotic capabilities into oral formulations to enhance drug delivery. Such robotic pill systems leverage the efficient propulsion of biological and synthetic micromotors to accelerate pill disintegration and overcome mucosal barriers, increasing bioavailability with lower doses and fewer side effects. In addition, advanced bioinspired robotic capsules, including microneedles, microinjectors, and microjet systems, offer enhanced macromolecule bioavailability comparable with that achieved with subcutaneous injections. The future of precision medicine lies in encapsulating diverse micromotors (with unique capabilities) within pharmaceutical carriers, offering groundbreaking opportunities for enhanced therapeutic interventions.

Advanced imaging techniques and artificial intelligence in pleural diseases: a narrative review

BACKGROUND

Pleural diseases represent a significant healthcare burden, affecting over 350 000 patients annually in the US alone and requiring accurate diagnostic approaches for optimal management. Traditional imaging techniques have limitations in differentiating various pleural disorders and invasive procedures are usually required for definitive diagnosis.

METHODS

We conducted a nonsystematic, narrative literature review aimed at describing the latest advances in imaging techniques and artificial intelligence (AI) applications in pleural diseases.

RESULTS

Novel ultrasound-based techniques, such as elastography and contrast-enhanced ultrasound, are described for their promising diagnostic accuracy in differentiating malignant from benign pleural lesions. Quantitative imaging techniques utilising pixel-density measurements to noninvasively distinguish exudative from transudative effusions are highlighted. AI algorithms, which have shown remarkable performance in pleural abnormality detection, malignant effusion characterisation and automated pleural fluid volume quantification, are also described. Finally, the role of deep-learning models in early complication detection and automated analysis of follow-up imaging studies is examined.

CONCLUSIONS

Advanced imaging techniques and AI applications show promise in the management and follow-up of pleural diseases, improving diagnostic accuracy and reducing the need for invasive procedures. However, larger prospective studies are needed for validation. The integration of AI-driven imaging analysis with molecular and genomic data offers potential for personalised therapeutic strategies, although challenges in data privacy, algorithm transparency and clinical validation persist. This comprehensive approach may revolutionise pleural disease management, enhancing patient outcomes through more accurate, noninvasive diagnostic strategies.

Novel automation, artificial intelligence, and biomimetic engineering advancements for insect studies and management

Entomology has seen remarkable advancements through the integration of robotics, artificial intelligence (AI), and biomimetic engineering. These technological innovations are revolutionizing how scientists study insect behavior, ecology, and management. Robotics and AI offer unprecedented precision and efficiency in monitoring and controlling insect populations. Biomimetics provides new ways to understand and replicate insect abilities in bioengineered systems. This mini-review highlights recent developments in these fields, focusing on key studies describing the transformative potential of these technologies. I explore their applications, benefits, and challenges, aiming at providing an overview of the current state and future directions in insect science and management.

Soft Materials and Devices Enabling Sensorimotor Functions in Soft Robots

Sensorimotor functions, the seamless integration of sensing, decision-making, and actuation, are fundamental for robots to interact with their environments. Inspired by biological systems, the incorporation of soft materials and devices into robotics holds significant promise for enhancing these functions. However, current robotics systems often lack the autonomy and intelligence observed in nature due to limited sensorimotor integration, particularly in flexible sensing and actuation. As the field progresses toward soft, flexible, and stretchable materials, developing such materials and devices becomes increasingly critical for advanced robotics. Despite rapid advancements individually in soft materials and flexible devices, their combined applications to enable sensorimotor capabilities in robots are emerging. This review addresses this emerging field by providing a comprehensive overview of soft materials and devices that enable sensorimotor functions in robots. We delve into the latest development in soft sensing technologies, actuation mechanism, structural designs, and fabrication techniques. Additionally, we explore strategies for sensorimotor control, the integration of artificial intelligence (AI), and practical application across various domains such as healthcare, augmented and virtual reality, and exploration. By drawing parallels with biological systems, this review aims to guide future research and development in soft robots, ultimately enhancing the autonomy and adaptability of robots in unstructured environments.

Autonomous robotic organizations for marine operations

Future marine operations objectives and tasks require a paradigm shift from robots to autonomous robotic organizations (AROs). These AROs must have advanced cooperative skills, control capabilities, and resilience both as individuals and as heterogeneous robot teams operating in space and air, on the sea surface, and underwater.

Active microgel particle swarms for intrabronchial targeted delivery

Intrabronchial delivery of therapeutic agents is critical to the treatment of respiratory diseases. Targeted delivery is demanded because of the off-target accumulation of drugs in normal lung tissues caused by inhalation and the limited motion dexterity of clinical bronchoscopes in tortuous bronchial trees. Herein, we developed microrobotic swarms consisting of magnetic hydrogel microparticles to achieve intrabronchial targeted delivery. Under programmed magnetic fields, the microgel particle swarms performed controllable locomotion and adaptative structure reconfiguration in tortuous and air-filled environments. The swarms were further integrated with imaging contrast agents for precise tracking under x-ray fluoroscopy and computed tomography imaging. Magnetic navigation of the swarms in an ex vivo lung phantom and in vivo delivery into deep branches of the bronchial trees were achieved. The on-demand reconfiguration of swarms for avoiding the microgel particles from entering nontarget bronchi and the precise delivery into tilted bronchi through climbing motion were validated.

Liquid-bodied antibiofilm robot with switchable viscoelastic response for biofilm eradication on complex surface topographies

Recalcitrant biofilm infections pose a great challenge to human health. Micro- and nanorobots have been used to eliminate biofilm infections in hard-to-reach regions inside the body. However, applying antibiofilm robots under physiological conditions is limited by the conflicting demands of accessibility and driving force. Here, we introduce a liquid-bodied antibiofilm robot constructed by a dynamically cross-linked magnetic hydrogel. Leveraging the viscoelastic response of the robot enables it to adapt to complex surface topographies such as medical meshes and stents. Upon actuation, the robot can mechanically destroy the biofilm matrix, chemically deactivate bacterial cells, and collect disrupted biofilm debris. The robot's antibiofilm performance is studied in vitro and demonstrated on a medical mesh and a biliary stent. Tracking and navigation under endoscopy and x-ray imaging in an ex vivo porcine bile duct are demonstrated. Last, in vivo antibiofilm treatment is conducted by indwelling infected stents into mice's abdominal cavity and clearing the biofilm infection using the proposed robot.

Untethered Soft Robots Based on 1D and 2D Nanomaterials

Biological structures exhibit autonomous and intelligent behaviors, such as movement, perception, and responses to environmental changes, through dynamic interactions with their surroundings. Inspired by natural organisms, future soft robots are also advancing toward autonomy, sustainability, and interactivity. This review summarizes the latest achievements in untethered soft robots based on 1D and 2D nanomaterials. First, the performance of soft actuators designed with different structures is compared. Then, the development of basic locomotion forms, including crawling, jumping, swimming, rolling, gripping, and multimodal, mimicking biological motion mechanisms under dynamic stimuli, is discussed. Subsequently, various self-sustained movements based on imbalance mechanisms under static stimuli are introduced, including light tracking, self-oscillating, self-crawling, self-rolling, and flying. Following that, the progress in soft actuators integrated with additional functionalities such as sensing, energy harvesting, and storage is summarized. Finally, the challenges faced in this field and the prospects for future development are discussed.

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 democratized bimodal model of research for soft robotics: Integrating slow and fast science

A shift toward a democratized, bimodal model of research would allow soft robotics to realize its full potential.

A soft, ultra-tough and multifunctional artificial muscle for volumetric muscle loss treatment

The escalating prevalence of skeletal muscle disorders highlights the critical need for innovative treatments for severe injuries such as volumetric muscle loss. Traditional treatments, such as autologous transplants, are constrained by limited availability and current scaffolds often fail to meet complex clinical needs. This study introduces a new approach to volumetric muscle loss treatment by using a shape-memory polymer (SMP) based on block copolymers of perfluoropolyether and polycaprolactone diol. This SMP mimics the biomechanical properties of natural muscle, exhibiting a low elastic modulus (2-6 MPa), high tensile strength (72.67 ± 3.19 MPa), exceptional toughness (742.02 ± 23.98 MJ m-3) and superior biocompatibility, thereby enhancing skeletal muscle tissue integration and regeneration within 4 weeks. Moreover, the polymer's shape-memory behavior and ability to lift >5000 times its weight showcase significant potential in both severe muscle disorder treatment and prosthetic applications, surpassing existing scaffold technologies. This advancement marks a pivotal step in the development of artificial muscles for clinical use.

High-Resolution Self-Assembly of Functional Materials and Microscale Devices via Selective Plasma Induced Surface Energy Programming

Current technologies preclude effective and efficient self-assembly of heterogeneous arrangements of functional materials between 10-1 and 10-5 m. Consequently, their fabrication is dominated by methods of direct material manipulation, which struggle to meet the designers' demands regarding resolution, material freedom, production time, and cost. A two-step, computer-controlled is presented, multi-material self-assembly technique that allows heterogenous patterns of several centimeters with features down to 12.5 µm in size. First, a micro plasma jet selectively programs the surface energy of a polydimethylsiloxane substrate through localized chemical functionalization. Second, polar fluids containing functional materials are simplistically introduced which then self-assemble according to the patterned regions of high surface energy over timescales of the order of seconds. In-process control enables both high-resolution patterning and high throughput. This approach is demonstrated to produce heterogenous patterns of materials with varying conductive, magnetic, and mechanical properties. These include magneto-mechanical films and flexible electronic devices with unprecedented processing times and economy for high-resolution patterns. This self-assembly approach can disrupt the current lithography/direct write paradigm that dominates micro/meso-fabrication, enabling the next generation of devices across a broad range of fields via a flexible, industrially scalable, and environmentally friendly manufacturing route.

Propulsion Mechanisms in Magnetic Microrobotics: From Single Microrobots to Swarms

Microrobots with different structures can exhibit multiple propulsion mechanisms under external magnetic fields. Swarms dynamically assembled by microrobots inherit the advantages of single microrobots, such as degradability and small dimensions, while also offering benefits like scalability and high flexibility. With control of magnetic fields, these swarms demonstrate diverse propulsion mechanisms and can perform precise actions in complex environments. Therefore, the relationship between single microrobots and their swarms is a significant area of study. This paper reviews the relationship between single microrobots and swarms by examining the structural design, control methods, propulsion mechanisms, and practical applications. At first, we introduce the structural design of microrobots, including materials and manufacturing methods. Then, we describe magnetic field generation systems, including gradient, rotating, and oscillating magnetic fields, and their characteristics. Next, we analyze the propulsion mechanisms of individual microrobots and the way microrobots dynamically assemble into a swarm under an external magnetic field, which illustrates the relationship between single microrobots and swarms. Finally, we discuss the application of different swarm propulsion mechanisms in water purification and targeted delivery, summarize current challenges and future work, and explore future directions.

A neuromechanics solution for adjustable robot compliance and accuracy

Robots have to adjust their motor behavior to changing environments and variable task requirements to successfully operate in the real world and physically interact with humans. Thus, robotics strives to enable a broad spectrum of adjustable motor behavior, aiming to mimic the human ability to function in unstructured scenarios. In humans, motor behavior arises from the integrative action of the central nervous system and body biomechanics; motion must be understood from a neuromechanics perspective. Nervous regions such as the cerebellum facilitate learning, adaptation, and coordination of our motor responses, ultimately driven by muscle activation. Muscles, in turn, self-stabilize motion through mechanical viscoelasticity. In addition, the agonist-antagonist arrangement of muscles surrounding joints enables cocontraction, which can be regulated to enhance motion accuracy and adapt joint stiffness, thereby providing impedance modulation and broadening the motor repertoire. Here, we propose a control solution that harnesses neuromechanics to enable adjustable robot motor behavior. Our solution integrates a muscle model that replicates mechanical viscoelasticity and cocontraction together with a cerebellar network providing motor adaptation. The resulting cerebello-muscular controller drives the robot through torque commands in a feedback control loop. Changes in cocontraction modify the muscle dynamics, and the cerebellum provides motor adaptation without relying on prior analytical solutions, driving the robot in different motor tasks, including payload perturbations and operation across unknown terrains. Experimental results show that cocontraction modulates robot stiffness, performance accuracy, and robustness against external perturbations. Through cocontraction modulation, our cerebello-muscular torque controller enables a broad spectrum of robot motor behavior.

Water-Stable Magnetic Lipiodol Micro-Droplets as a Miniaturized Robotic Tool for Drug Delivery

Magnetic microrobots, designed to navigate the complex environments of the human body, show promise for minimally invasive diagnosis and treatment. However, their clinical adoption faces hurdles such as biocompatibility, precise control, and intelligent tracking. Here a novel formulation (referred to water-stable magnetic lipiodol micro-droplets, MLMD), integrating clinically approved lipiodol, gelatin, and superparamagnetic iron oxide nanoparticles (SPION) with a fundamental understanding of the structure-property relationships is presented. This formulation demonstrates multiple improved properties including flowability, shape adaptability, efficient drug loading, and compatibility with digital subtraction angiography (DSA) imaging in both in vitro and in vivo experiments. This enables the MLMD as a versatile tool for image-guided therapy, supported by a close-looped magnetic navigation system featuring artificial intelligence (AI)-driven visual feedback for autonomous control. The system effectively performs navigational tasks, including pinpointing specific locations of MLMD, recognizing and avoiding obstacles, mapping and following predetermined paths, and utilizing magnetic fields for precise motion planning to achieve visual drug delivery. The MLMD combines magnetic actuation with an AI-directed close-looped navigation, offering a transformative platform for targeted therapeutic delivery.

Sub-millimeter fiberscopic robot with integrated maneuvering, imaging, and biomedical operation abilities

Small-scale continuum robots hold promise for interventional diagnosis and treatment, yet existing models struggle to achieve small size, precise steering, and visualized functional treatment simultaneously, termed an "impossible trinity". This study introduces an optical fiber-based continuum robot integrated imaging, high-precision motion, and multifunctional operation abilities at submillimeter-scale. With a slim profile of 0.95 mm achieved by microscale 3D printing and magnetic spray, this continuum robot delivers competitive imaging performance and extends obstacle detection distance up to ~9.4 mm, a tenfold improvement from the theoretical limits. Besides, the robot showcases remarkable motion precision (less than 30 μm) and substantially widens the imaging region by ~25 times the inherent view. Through ex vivo trials, we validate the robot's practicality in navigating constrained channels, such as the lung end bronchus, and executing multifunctional operations including sampling, drug delivery, and laser ablation. The proposed submillimeter continuum robot marks a significant advancement in developing biomedical robots, unlocking numerous potential applications in biomedical engineering.

A Bibliometric Review of Brain-Computer Interfaces in Motor Imagery and Steady-State Visually Evoked Potentials for Applications in Rehabilitation and Robotics

In this paper, a bibliometric review is conducted on brain-computer interfaces (BCI) in non-invasive paradigms like motor imagery (MI) and steady-state visually evoked potentials (SSVEP) for applications in rehabilitation and robotics. An exploratory and descriptive approach is used in the analysis. Computational tools such as the biblioshiny application for R-Bibliometrix and VOSViewer are employed to generate data on years, sources, authors, affiliation, country, documents, co-author, co-citation, and co-occurrence. This article allows for the identification of different bibliometric indicators such as the research process, evolution, visibility, volume, influence, impact, and production in the field of brain-computer interfaces for MI and SSVEP paradigms in rehabilitation and robotics applications from 2000 to August 2024.

Perspective on Wearable Systems for Human Underwater Perceptual Enhancement

Underwater areas have harsh environments with poor light, limited visibility, and high levels of noise. Humans have a weak perception of position, surroundings, and exterior information when staying underwater, which makes it difficult for humans to carry out complex underwater tasks, such as rescue, observation, and construction. Wearable devices have shown good results in enhancing human sensory function on land, thus they could potentially play a role in enhancing human underwater perception ability. This perspective aims to analyze the state-of-the-art of underwater wearable systems for human perception enhancement. This work discusses the core technology and challenges of human underwater perceptual enhancement, including wearable underwater navigation, underwater environment reconstruction, and underwater sensorial information delivery. Future research could focus on designing waterproof flexible human-machine interfaces for sensing and feedback, exploiting advanced sensors and fusion algorithms for wearable underwater positioning, and studying multimodal information interaction strategies of wearable systems.

Upgrading and extending the life cycle of soft robots with in situ free-form liquid three-dimensional printing

Soft robotics hardware, with numerous applications ranging from health care to exploration of unstructured environments, suffers from limited life cycles, which lead to waste generation and poor sustainability. Soft robots combine soft or hybrid components via complex assembly and disassembly workflows, which complicate the repair of broken components, hinder upgradability, and ultimately reduce their life spans. In this work, an advanced extrusion-based additive manufacturing process, in situ free-form liquid three-dimensional printing (iFL3DP), was developed to facilitate functional upgrades and repairs in soft robots. A yield-stress hydrogel-a type of material that can maintain its shape until sufficient stress is applied-was first printed directly onto the robot surface, serving as a support for printing new components. This technique enabled the fabrication of advanced components with seamless integration onto already assembled robots. These components could combine multiple materials with intricate geometries, including overhangs and high-aspect ratio shapes, that are considerably challenging to manufacture and integrate via traditional methods such as casting. This approach was successfully applied to upgrade an existing soft robot by adding three advanced functionalities: whisker-like sensors for tactile feedback, a grasping mechanism, and a multifunctional passive whisker array. This study showcases the easy repairability of features, new and old, substantially extending the robot's life span. This workflow has potential to enhance the sustainable development of soft robots.

State of the Art and Development Trend of Laparoscopic Surgical Robot and Master Manipulator

BACKGROUND

In recent years, laparoscopic surgical robots have rapidly developed. However, most focus on the overall robotic systems, with few summaries on the laparoscopic surgical robots and the master manipulators.

METHODS

This paper provides a summary and analysis of typical laparoscopic surgical robots, including the strengths and limitations of existing laparoscopic surgical robots. Additionally, the master manipulators are analysed and summarised from four aspects: structural design and optimization, time-varying delays, tremor suppression and force feedback. Further classification and summary are made based on the different methods used in each study.

RESULTS

Laparoscopic surgical robots and the master manipulators still have some limitations. Therefore, the development trends of the laparoscopic surgical robots and the master manipulators are discussed from four aspects: structural materials, remote surgery, intelligence and human-machine interaction.

CONCLUSION

With the continuous advancement of technology, laparoscopic surgical robots will play an increasingly important role in the field of surgery.

Physics of sliding on water explains morphological and behavioural allometry across a wide range of body sizes in water striders (Gerridae)

Laws of physics shape adaptations to locomotion, and semiaquatic habitats of water striders provide opportunities to explore adaptations to locomotion on water surface. The hydrodynamics of typical propelling with symmetrical strokes of midlegs is well understood, but the subsequent passive sliding on surface has not been explored. We hypothesized that morphological and behavioural adaptations to sliding vary by body size. Based on empirical observations of water striders across a wide range of body size, we constructed a theoretical model of floating and resistance during sliding. Our model predicts that large water striders are too heavy to support anterior body on forelegs during sliding when their two midlegs are off the surface symmetrically during a recovery phase after stroke in symmetric gait. Heavy species should either (i) develop sufficiently long forelegs to support their anterior body on surface during symmetric gait or (ii) use asymmetric gait when one forward-extended midleg supports anterior body. Observations were consistent with these predictions. Additionally, medium-sized insects were observed to switch between symmetrical and asymmetrical gait in the manner that reduces sliding resistance. Our results illustrate how habitat-specific physical processes cause morphological and behavioural diversity associated with body size among biological organisms.

Human short-latency reflexes show precise short-term gain adaptation after prior motion

The central nervous system adapts the gain of short-latency reflex loops to changing conditions. Experiments on biomimetic robots showed that reflex modulation could substantially increase energy efficiency and stability of periodic motions if, unlike known mechanisms, the reflex modulation both acted precisely on the muscles involved and lasted after the motion. This study tests the presence of such a mechanism by having participants repeatedly rotate either their right elbow or shoulder joint before perturbing either joint. The results demonstrate a mechanism that modulates short-latency reflex gains after prior motion with joint-specific precision. Enhanced gains were observed hundreds of milliseconds after movement cessation, a timescale well suited to quickly adapt overall periodic motion cycles. A serotonin antagonist significantly decreased these postmovement gains diffusely across joints. But blocking serotonin did not affect the joint specificity of the gain scaling more than a placebo, suggesting that serotonin sets the overall reflex gain across joints after movement by an effect that is modulated in a joint-specific manner by an unidentified neural circuit. These results confirm the existence of a new, joint-specific, fast, persistent adaptation of short-latency reflex loops induced by motion in human arms.NEW & NOTEWORTHY Our results expose a new spinal cord mechanism that modulates motoneuron gains, uniquely equipped to adapt movement in changing environments: it acts with joint-specific precision, reacts quickly to mechanical changes, and still persists long enough to accumulate information across movement cycles. The overall motoneuron gain across joints can be scaled down by an antagonist to serotonergic neuromodulation, whereas its joint specificity is unaffected by the antagonist and thus due to a complementary, unknown spinal mechanism.

Programmable spatial magnetization stereolithographic printing of biomimetic soft machines with thin-walled structures

Soft machines respond to external magnetic stimuli with targeted shape changes and motions due to anisotropic magnetization, showing great potential in biomimetic applications. However, mimicking biological functionalities, particularly the complex hollow structures of organs and their dynamic behaviors, remains challenging. Here, we develop a printing method based on three-dimensional uniform magnetic field-assisted stereolithography to fabricate thin-walled soft machines with internal cavities and programmable magnetization. This printing technique employs Halbach arrays and an electromagnetic solenoid to generate an adjustable uniform magnetic field (up to 80 millitesla), efficiently orienting ferromagnetic particles, followed by solidification with patterned ultraviolet light. A support strategy and optimized material composition enhance printing stability and success rates. Our developed method enables fabrication of magnetic-driven soft machines capable of peristaltic propulsion, unidirectional fluid transport, periodic pumping action, and intake-expulsion deformation. These structures, achieving hollow ratios as high as 0.92 and enabling parallel manufacturing, highlight this technique's considerable potential for biomedical applications by emulating complex biological behaviors and functions.

The multifunctional use of an aqueous battery for a high capacity jellyfish robot

The batteries that power untethered underwater vehicles (UUVs) serve a single purpose: to provide energy to electronics and motors; the more energy required, the bigger the robot must be to accommodate space for more energy storage. By choosing batteries composed primarily of liquid media [e.g., redox flow batteries (RFBs)], the increased weight can be better distributed for improved capacity with reduced inertial moment. Here, we formed an RFB into the shape of a jellyfish, using two redox chemistries and architectures: (i) a secondary ZnBr2 battery and (ii) a hybrid primary/secondary ZnI2 battery. A UUV was able to be powered solely by RFBs with increased volumetric (Q ~ 11 ampere-hours per liter) and areal (108 milliampere-hours per square centimeter) energy density, resulting in a long operational lifetime (T ~ 1.5 hours) for UUVs composed of primarily electrochemically energy-dense liquid (~90% of the robot's weight).

Bioinspired cooperation in a heterogeneous robot swarm using ferrofluid artificial pheromones for uncontrolled environments

This article presents a novel bioinspired technology for the cooperation and coordination of heterogeneous robot swarms in uncontrolled environments, utilizing an artificial pheromone composed of magnetized ferrofluids. Communication between different types of robots is achieved indirectly through stigmergy, where messages are inherently associated with specific locations. This approach is advantageous for swarm experimentation outside controlled laboratory spaces, where localization is typically managed through centralized camera systems (e.g. infrared, RGB). Applying pheromone principles has also proven beneficial for various swarm behaviors. We introduce a detection methodology for the artificial ferrofluid pheromone using low-cost magnetic sensors, along with signal processing and parameter characterization. Experiments involved a heterogeneous swarm consisting of two types of robots: one equipped with camera and image processing capabilities and the other with basic sensor technologies. Validation in multiple uncontrolled environments (with varying floor surfaces, wind, and light conditions) demonstrated successful cooperation among robots with differing technological complexities using the proposed technology.

Evaluating large language models in theory of mind tasks

Eleven large language models (LLMs) were assessed using 40 bespoke false-belief tasks, considered a gold standard in testing theory of mind (ToM) in humans. Each task included a false-belief scenario, three closely matched true-belief control scenarios, and the reversed versions of all four. An LLM had to solve all eight scenarios to solve a single task. Older models solved no tasks; Generative Pre-trained Transformer (GPT)-3-davinci-003 (from November 2022) and ChatGPT-3.5-turbo (from March 2023) solved 20% of the tasks; ChatGPT-4 (from June 2023) solved 75% of the tasks, matching the performance of 6-y-old children observed in past studies. We explore the potential interpretation of these results, including the intriguing possibility that ToM-like ability, previously considered unique to humans, may have emerged as an unintended by-product of LLMs' improving language skills. Regardless of how we interpret these outcomes, they signify the advent of more powerful and socially skilled AI-with profound positive and negative implications.

A Highly-Sensitive Omnidirectional Acoustic Sensor for Enhanced Human-Machine Interaction

Acoustic sensor-based human-machine interaction (HMI) plays a crucial role in natural and efficient communication in intelligent robots. However, accurately identifying and tracking omnidirectional sound sources, especially in noisy environments still remains a notable challenge. Here, a self-powered triboelectric stereo acoustic sensor (SAS) with omnidirectional sound recognition and tracking capabilities by a 3D structure configuration is presented. The SAS incorporates a porous vibrating film with high electron affinity and low Young's modulus, resulting in high sensitivity (3172.9 mVpp Pa-1) and a wide frequency response range (100-20 000 Hz). By utilizing its omnidirectional sound recognition capability and adjustable resonant frequency feature, the SAS can precisely identify the desired audio signal with an average deep learning accuracy of 98%, even in noisy environments. Moreover, the SAS can simultaneously recognize multiple individuals in the auxiliary conference system and the driving commands under background music in self-driving vehicles, which marks a notable advance in voice-based HMI systems.

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.

A fabrication strategy for millimeter-scale, self-sensing soft-rigid hybrid robots

Soft robots typically involve manual assembly of core hardware components like actuators, sensors, and controllers. This increases fabrication time and reduces consistency, especially in small-scale soft robots. We present a scalable monolithic fabrication method for millimeter-scale soft-rigid hybrid robots, simplifying the integration of core hardware components. Actuation is provided by soft-foldable polytetrafluoroethylene film-based actuators powered by ionic fluid injection. The desired motion is encoded by integrating a mechanical controller, comprised of rigid-flexible materials. The robot's motion can be self-sensed using an ionic resistive sensor by detecting electrical resistance changes across its body. Our approach is demonstrated by fabricating three distinct soft-rigid hybrid robotic modules, each with unique degrees of freedom: translational, bending, and roto-translational motions. These modules connect to form a soft-rigid hybrid continuum robot with real-time shape-sensing capabilities. We showcase the robot's capabilities by performing object pick-and-place, needle steering and tissue puncturing, and optical fiber steering tasks.

Transformable 3D curved high-density liquid metal coils - an integrated unit for general soft actuation, sensing and communication

Rigid solenoid coils have long been indispensable in modern intelligent devices. However, their sparse structure and challenging preparation of flexible coils for soft robots impose limitations. Here, a transformable 3D curved high-density liquid metal coil (HD-LMC) is introduced that surpasses the structural density level of enameled wire. The fabrication technique employed for high-density channels in elastomers is universally applicable. Such HD-LMCs demonstrated excellent performance in pressure, temperature, non-contact distance sensors, and near-field communication. Soft electromagnetic actuators thus achieved significantly improved the electromagnetic force and power density. Moreover, precise control of swinging tail motion enables a bionic pufferfish to swim. Finally, HD-LMC is further utilized to successfully implement a soft rotary robot with integrated sensing and actuation capabilities. This groundbreaking research provides a theoretical and experimental basis for expanding the applications of liquid metal-based multi-dimensional complex flexible electronics and is expected to be widely used in liquid metal-integrated robotic systems.

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.

Bioinspired Liquid Metal Based Soft Humanoid Robots

The pursuit of constructing humanoid robots to replicate the anatomical structures and capabilities of human beings has been a long-standing significant undertaking and especially garnered tremendous attention in recent years. However, despite the progress made over recent decades, humanoid robots have predominantly been confined to those rigid metallic structures, which however starkly contrast with the inherent flexibility observed in biological systems. To better innovate this area, the present work systematically explores the value and potential of liquid metals and their derivatives in facilitating a crucial transition towards soft humanoid robots. Through a comprehensive interpretation of bionics, an overview of liquid metals' multifaceted roles as essential components in constructing advanced humanoid robots-functioning as soft actuators, sensors, power sources, logical devices, circuit systems, and even transformable skeletal structures-is presented. It is conceived that the integration of these components with flexible structures, facilitated by the unique properties of liquid metals, can create unexpected versatile functionalities and behaviors to better fulfill human needs. Finally, a revolution in humanoid robots is envisioned, transitioning from metallic frameworks to hybrid soft-rigid structures resembling that of biological tissues. This study is expected to provide fundamental guidance for the coming research, thereby advancing the area.

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.

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.

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.

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.

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.