Telerobotic ultrasound imaging has emerged as a critical advancement in the intersection of medical robotics, communication systems, and measurement science. As demand for diagnostic services rises—particularly in underserved or remote regions—technologies that enable clinicians to interact with patients through reliable, real-time systems remotely have become vital. At the heart of these systems lies precise measurement: force sensing, haptic feedback, position tracking, and network monitoring. Any error in these measurements could lead to incorrect diagnoses, potentially endangering patient safety. Just as imaging modalities such as X-ray and MRI transformed diagnostic confidence, haptic tele-ultrasound introduces a new class of challenges and opportunities rooted in instrumentation accuracy, network resilience, and physical interaction fidelity. In this roadmap, we examine the evolution of teleoperated ultrasound systems, focusing on their instrumentation and measurement components. From early rigid-arm setups to collaborative robots equipped with force/torque sensors and stereovision, we outline the technological progression, critical milestones, and lessons learned. We present a case study of HaptiScan, a system codeveloped with Telstra, which integrates real-time haptic feedback, six-axis sensing, and safety protocols for fully remote scanning. We also highlight open challenges in signal delay, system stability, and measurement uncertainty, and we conclude with a discussion on future directions in autonomous operation, error correction, and globally scalable remote diagnostics. This review is designed for engineers, clinicians, and researchers seeking to understand how measurement science underpins the next frontier of remote healthcare delivery.

Micro/nanorobots (MNRs) have emerged as versatile microscale platforms for precise tasks in complex biological environments, enabled by advances in microfabrication, smart materials, and multiscale actuation. Despite progress, most MNRs remain heavily dependent on external control and show limited adaptability under dynamic, heterogeneous in vivo conditions. These limitations have motivated the development of intelligent micro/nanorobots, integrating responsiveness across structure, materials, motion, and functional application. This review presents a framework for intelligent MNRs along three interconnected dimensions. Structural and material intelligence, via geometry, multistability, and stimuli-responsive materials, enables intrinsic adaptability and environment-responsive behaviors. Motion and navigation intelligence, encompassing multimodal locomotion, imaging-guided control, swarm coordination, and AI-assisted strategies, supports robust, autonomous operation under physiological complexity. Applications of intelligent MNRs facilitate context-aware drug delivery, adaptive therapeutic interventions, real-time sensing, and microenvironment modulation, enabling functionally integrated performance. By synthesizing advances across these domains, we highlight intelligent MNRs as a central strategy for bridging laboratory demonstrations and clinical translation. Key challenges and future directions include integrated intelligence architectures, improved biocompatibility and manufacturability, and rigorous in vivo validation. Collectively, intelligent MNRs hold the potential to evolve into adaptive, safe, and semi-autonomous microscale systems for precise biomedical operation.

Efficient liquid manipulation is crucial in chemical engineering, biological research, clinical applications, and materials science. Bubbles, such as boiling, rising, and cavitating bubbles, have been widely employed to enhance mixing and mass transfer through their unique hydrodynamic behaviors. Yet, conventional bubble-based approaches often face limited scalability and poor performance in high-viscosity environments. Here, we introduce a strategy that employs low-energy acoustic excitation of rising microbubbles to achieve scalable and efficient mass transfer across macroscale and microscale domains. By coupling buoyancy-driven convection with localized acoustic microstreaming, acoustic rising microbubbles simultaneously extend the operational workspace and intensify local mass transfer. Particle image velocimetry and computational fluid dynamics analyses characterize the distinct contributions of buoyancy-induced flows, acoustically induced microstreaming, and their superimposed effects. Various chemical and biomedical applications, including efficient high-viscosity mixing, accelerated chemical material synthesis, altered cell membrane permeability, promoted cell lysis, and thrombus clearance, demonstrate the great potential of the proposed acoustic rising bubbles for efficient mass transfer in laboratory and industrial liquid manipulations.

Precise motion control of magnetic microrobots in complex and dynamic environments remains a critical challenge for enabling key applications such as targeted therapy and micromanipulation. Purely manual teleoperation is prone to operator fatigue and error, while fully autonomous systems often lack the robustness and adaptability to handle. Here, we propose a human-machine shared cascade control method for magnetically driven microrobots, which effectively integrates human cognitive intelligence with machine autonomy for collision-free navigation in dynamic environments. The outer-loop hybrid shared control unit smoothly modulates control authority in response to real-time collision risk, dynamically integrating the operator instructions and the autonomous navigation system output guided by the enhanced artificial potential field method to formulate the guidance law. For the inner-loop motion tracking, a data-driven adaptive orientation controller is designed, which integrates a nonlinear feedforward compensator leveraging a Gaussian process regression (GPR) model with a linear feedback controller whose parameters are optimized using the virtual reference feedback tuning (VRFT) method, ensuring fast and precise tracking of the desired motion. The effectiveness of the proposed method was validated through both simulation and physical experiments. In human-subject studies conducted on a physical magnetic actuation platform featuring both static and dynamic obstacle scenarios, quantitative results demonstrate that the shared control strategy significantly outperforms both purely manual and fully autonomous modes across all key metrics, including success rate, task completion time, stability, and safety ( $p \lt 0.001$ ). Furthermore, successful navigation within a complex gastric model demonstrates the potential of the shared control system for practical application in unstructured environments.

Reliable state estimation on low-cost quadruped robots is hindered by sensor noise, mechanical backlash, and structural compliance. Current methods face distinct limitations: the reliance on precise contact sensors in pure kinematics, training-coverage dependence in end-to-end networks, and sub-optimal mode averaging in conventional hybrid models. To address this, we propose a physics-informed Latent Regime Mixture-of-Experts (MoE) framework. First, we establish a robust kinematic baseline using a heuristic contact estimator to bypass unreliable force sensing and provide a stable physical prior. Second, we introduce a lightweight MoE network to predict nonlinear velocity residuals. By inferring latent motion regimes from a temporal window of proprioceptive history, the network performs regime-aware routing and fuses specialized experts for context-aware residual compensation. Consequently, the framework provides both accurate velocity corrections and adaptive covariance estimation for downstream fusion. Furthermore, due to the limited availability of dense velocity ground truth in self-collected outdoor data, we adopt a two-stage training strategy combining indoor velocity and sparse outdoor positional supervision. Experiments demonstrate a 14.2% velocity RMSE reduction on the ANYmal benchmark and a 28.2% drift reduction on Unitree Go1 across diverse scenarios. The system operates at over 240 Hz on an embedded Jetson platform.

Traditional microfluidic chips for single-cell mechanical characterization face challenges such as cell aggregation and low throughput, limiting their clinical applicability. While fluid-driven methods such as constricted extrusion, pipette aspiration, and shear-induced or stretch-induced deformation have demonstrated laboratory success, they require improvements in accuracy and scalability. To overcome these limitations, integration of external physical fields, including acoustic, optical, electrical, and magnetic, enables non-contact, high-throughput cell operations and analysis. Acoustic waves and magnetic fields provide precise control over cell deformation, optical tweezers enable contact-free trapping, and electric fields facilitate dielectrophoretic manipulation. These techniques improve measurement sensitivity and throughput, making them more suitable for clinical applications, but also increase follow-up processing time. Artificial intelligence (AI) further enhances microfluidic automation across all these methodologies by enabling real-time image processing, parameter optimization, and data analysis to shorten processing time. This review particularly explores how AI is poised to solve fundamental, long-standing problems in cell mechanics that are intractable for conventional methods. Future microfluidic systems will integrate multiple physical fields controlled with AI, improving precision and scalability. The convergence of microfluidics, external fields, and AI is expected to revolutionize single-cell mechanobiology, advancing both fundamental research and clinical applications.

Marine ecosystems, particularly coral reef communities, reveal how morphological diversification in fish species facilitates specialized locomotion through evolutionary optimization of body-fin coordination and hydrodynamic adaptations. Inspired by these biomechanical principles, we developed a morphology-encoded patterned magnetic millirobot (MPMR), whose anterior-to-posterior (AP) length ratio and body contour are predefined during fabrication to yield distinct hydrodynamic responses under the same uniform magnetic actuation. These MPMRs, with various morphologies, successfully emulated the undulatory swimming patterns of different fish species in a fluidic environment. Morphological differentiation in MPMRs has been shown to directly influence their motion performance, with an optimal AP ratio (approximately 1:1) and streamlined body contour maximizing propulsion efficiency. Furthermore, MPMRs with distinct morphologies display different frequency-dependent responses to magnetic actuation, leading to morphology-specific velocity profiles. By leveraging these morphology-encoded performance variations, we achieved effective selective control and multitarget delivery of multiple MPMRs under uniform magnetic fields, both in vitro and ex vivo (gastrointestinal tissue). These findings provide a foundation for future designs of flexible millirobots in similar environments and serve as a reference for advancing selective control methods for multiple millirobots in uniform magnetic fields.

Single-cell microgels, engineered to replicate complex 3-D in vivo niches, have shown tremendous potential for advancing biomedical research. Studies have demonstrated that microgels recapitulating tissues' stiffness facilitate the observation of cellular behavior in vitro. However, encapsulating single cells in microgels with composable stiffness gradients to understand cellular responses at microscale interfaces remains challenging. Here, we propose a versatile fabrication method that integrates an enhanced vision model with time-discrete bioprinting to encapsulate single cells in microgels featuring composable stiffness. An enhanced deep learning algorithm was developed to localize single cells in microscopic images, integrating traditional image processing algorithms for improved precision. By combining single-cell positioning data with a time-discrete array based on a digital micromirror device, dynamic digital masks can be generated to control each micromirror in real time. Using these masks, MDA-MB-231 cells were encapsulated in Gelatin methacrylate microgels with tunable stiffness ranging from 1.42 to 11.42 kPa. Experimental results indicated that when the microgel stiffness gradient exceeded 2 kPa, MDA-MB-231 cells exhibited a significant response, extending toward the softer regions of the microgels. These findings illustrate that our approach provides a valuable biofabrication strategy for investigating single-cell behaviors in microgels, showing great potential for applications in 3-D single-cell research.

Transbronchial lung biopsy (TBLB) has increasingly been recognized as a clinically significant procedure for the early diagnosis and treatment of lung cancer. However, the complex anatomical anatomy and narrow bronchial pathways present substantial challenges for conventional bronchoscopy, demanding exceptional surgical expertise, skills, and meticulous precision. To address these limitations, robot-assisted bronchoscopic systems integrated with flexible continuum bending sections and advanced sensing technologies have been developed to enable dexterous access and ensure safe tissue interaction. This review systematically examines the recent advancements in robot-assisted bronchoscopic systems and classifies them into two primary categories based on actuation mechanisms: tendon-driven and magnetic-driven approaches. The innovative mechanical designs, intelligent sensing techniques, control strategies, clinical progress, and current limitations of these robotic systems have been critically analyzed and summarized. Furthermore, the evolutionary trends of flexible bronchoscopic robots suitable for TBLB have been outlined, and the remaining challenges and potential technical solutions

Digital light processing (DLP) enables rapid fabrication of photocurable hydrogel microstructures, which serve as critical functional components in micro-optical systems and microrobotics, and whose performance depends on micron-scale morphological features and local stiffness. Traditional visual feedback methods provide horizontal data but struggle to quantify axial topography and local stiffness in real-time. Although digital holographic microscopy (DHM) offers advantages for sample characterization, its real-time capabilities are limited by the challenge of dynamically correcting for optical distortions. To address this, we present a real-time feedback control algorithm featuring a novel partial matrix Zernike fitting (PMZF) method. PMZF is analytically derived to estimate background phase distortion from partially occluded fields of view, enabling accurate phase reconstruction at 5 fps. With PMZF-based feedback, the system achieves 4.88 mu m axial precision and 4.29 kPa stiffness precision, improving accuracy by 71.9% over open-loop DLP. Moreover, the spatially resolved control of stiffness within single microgels leads to region-specific fluorescent release, demonstrating a functional behavior not attainable with conventional printing. This work provides an effective closed-loop strategy for controlling both geometry and stiffness, paving the way for advanced functional devices in tissue engineering, MEMS, and beyond.

Magnetic microrobots have emerged as transformative tools for biomedical applications, particularly in targeted cargo delivery within confined environments. Despite their potential, existing systems lack the capability for coencapsulation of various incompatible components in isolated compartments. In this paper, we propose a magnetic compartmentalized microrobot (MCM) fabricated on-chip with hydrogel. Our approach leverages a microfluidic aqueous two-phase system to create hydrogel capsules with dual cores, where two core flows composed of dextran and shell flow containing sodium alginate and magnetic particles are sequentially sheared by oil phase, crosslinked by calcium ion, and magnetized via an electromagnet, to produce MCMs massively. The resulting architecture enables separate encapsulation of incompatible components within two cores isolated by a hydrogel shell. Precise control over flow rates allows tunable sizing of both core and capsule dimensions. Under rotating magnetic fields, MCMs demonstrate programmable individual and collective behaviors, including directional motion, dispersion, and aggregation. We further characterize the system's drug-release kinetics and swelling properties in simulated gastrointestinal environments. These findings highlight MCMs' significant potential for tissue engineering, combinatorial drug delivery, and transport of incompatible bioactive agents.

Biohybrid robots with autonomous motility can recapitulate existing biological structures and interact with their surroundings, attracting broad attention from researchers regarding their locomotion characteristics. However, muscle-driven biohybrid millirobots often struggle to maintain stable and tunable locomotion beyond obstacle-free fluidic environments, thereby limiting their applicability in task-oriented operations such as trajectory-specific directional modulation and cargo transport. To address this issue, we developed a muscle-driven biohybrid thin-film millirobot (MBF-Robot) by patterning cardiomyocytes onto a flexible thin-film substrate in distinct spatial arrangements. This design allows MBF-Robots with identical geometrical configurations to exhibit distinct propulsion modes and motion directions, with a maximum speed of 0.79 mm/s (1 Hz). Moreover, by incorporating a small quantity of Fe3O4 particles into the robot's structural body, we implemented a synergistic control strategy that integrates inherent muscle-driven propulsion with non-contact directional regulation via an external magnetic field. This approach, while retaining muscle actuation as the sole driving force, imparts the MBF-Robot with continuous, rapid, and reversible navigation capability. Consequently, the MBF-Robot successfully executed tasks such as microsphere transport along prescribed trajectories and selective control of multiple millirobots. Overall, this work establishes a design paradigm and engineering foundation for achieving controlled locomotion in biohybrid millirobots.

Abstract Single-cell operations are crucial technologies across fundamental biology, diagnostics, and therapeutics. The precise characterization and measurement of the physical and biological properties of cells provide critical support for disease diagnosis, clinical trial, and pharmaceutical research. Based on different cellular properties and operational objectives, researchers have developed two principal modalities for these operations: contact and non-contact micromanipulation. Contact micromanipulation achieves precise cell operations, such as grasping and injection, through direct mechanical interaction. In contrast, non-contact micromanipulation relies on various physical fields (e.g., electric, optical, acoustic, magnetic, and microfluidics) to apply remote forces, enabling indirect, high-throughput operations. This review systematically covers recent advances in both contact and non-contact single-cell operations. We provide a critical analysis of their core operational principles and a comparative assessment of their advantages and disadvantages across diverse application environments. Furthermore, this review provides an outlook on key future development trends, particularly in advanced materials, multimodal systems, and fluidics, aiming to facilitate the realization of more precise and stable cell operations.

The clinical efficacy of many conventional passive drug delivery systems is frequently constrained by their low targeting efficiency, important off-target toxicity, and inadequate capacity for traversing biological barriers. Autonomous microrobots, as miniature intelligent platforms capable of active navigation and on-demand responsiveness, offer an active delivery strategy for achieving spatiotemporally precise targeted therapy. This review aims to systematically consolidate and critique the theoretical foundations, key technologies, cutting-edge applications, and future challenges of this emergent interdisciplinary field. We first provide an in-depth analysis of the technological frameworks underpinning the 2 core functionalities: targeted delivery and on-demand release. This encompasses a diverse array of propulsion and navigation strategies-from chemical and physical fields to biohybrid systems-as well as programmed drug release mechanisms responsive to endogenous and exogenous stimuli. Building on this, we introduce a hierarchical paradigm organized by biological-barrier traversal capability to review the preclinical progress of microrobots, from localized delivery in accessible body cavities to deep-tissue and trans-barrier applications. This function-oriented framework more directly links microrobot design to the progressive physiological constraints encountered in vivo, thereby providing a more integrated and translationally relevant perspective on biomedical applications and clinical potential. Concurrently, this paper examines the bottlenecks impeding their clinical translation, including biosafety, systemic controllability, and regulatory science. Looking forward, the deep integration of microrobotics with smart materials, artificial intelligence, and theranostic systems is poised to cultivate a new generation of intelligent medical robots capable of personalized treatment via closed-loop manners.

Active micromotors with optoelectronic guidance that are capable of autonomous motion have garnered significant interest, particularly in the field of targeted drug delivery, detoxification, and immune-sensing, etc. However, time-varying uncertain fluctuation in self-propelling velocity can cause active micromotors to encounter unexpected accidents or deviate from their preset state. Here, we propose a novel navigation method for Janus micromotors with guidance of optoelectronic virtual electrodes, involving visual recognition, decision making and motion control. A deep learning model detects the real-time state of Janus micromotors, providing position and velocity feedback for path planning and motion control. Velocity control is achieved by dynamic regulation of the electric peak-to-peak voltages, with tracking errors eliminated through the proxy-based sliding-mode control (PSMC) framework. To avoid obstacle interference, motion strategies for Janus micromotor are formulated by the Hierarchical Value Iteration Networks (HVINs). The Janus micromotor enabled navigating through a confined space containing multiple obstacles and following arbitrary velocity functions with small errors. Simulation and experimental results demonstrated that our navigation method achieves high accuracy in single-micromotor motion control and path planning, which holds significant promising for intricate tasks in biomedical applications.

This review highlights the advantages of transanal quasi-single-port surgery (taQSPS) and offers a comprehensive analysis of the development, current applications, and future trends of robotic systems utilized in taQSPS. Timely surgical intervention is crucial for optimal outcomes in rectal cancer treatment, with techniques evolving from open surgery to endoscopic surgery and, more recently, to taQSPS. This innovative technique accesses lesions via a transanal route while utilizing instruments and methods similar to those in single-port surgery (SPS). Building on this foundation, SPS robotic surgical systems have emerged as a promising advancement for taQSPS, offering enhanced precision and maneuverability. However, despite their progress, general-purpose SPS robotic systems are often limited by their bulky design and lack of adaptability to the rectal cavity. In contrast, specialized taQSPS robotic systems are tailored to transanal procedures’ unique anatomical and operational demands, making them highly suitable for such surgeries. This review highlights the potential of specialized taQSPS robotic systems, serving as a valuable reference for researchers and clinicians, and aims to promote further development and adoption of these systems in clinical practice.

Bio-integrated microrobots (BIMs), which are fabricated with biofriendly materials, biological units (e.g. cells or biomolecules), or cell-material hybrids have emerged as a promising technology for minimally invasive biomedicine. The diminutive size and flexible structures enable BIMs to navigate within narrow, deep, and challenging-to-reach in vivo regions, performing biopsy, diagnostic, drug delivery, and therapeutic functions with minimal invasiveness. However, the clinical deployment of BIMs is a highly orchestrated task that requires consideration of material properties, structural design, locomotion, observation, therapeutic outcomes, and side effects on cells and tissues, etc. In this review, we review and discuss the latest advances in the bio-integrated microrobot domain, evaluating various methods associated with materials, fabrication, actuation, and the implementation of biomedical functions in BIMs. By comparing the advantages and shortcomings of these techniques, this review highlights the challenges and future trends in highly intelligent bio-integrated microrobots, which have huge potential in minimally invasive biomedicine.

Micromanipulation is crucial for operating and analyzing microobjects in advanced biomedical applications. However, safe, low-cost, multifunctional micromanipulation for operating bio-objects across scales and modalities remains inaccessible. Here, we propose a versatile micromanipulation method driven by acoustic gas-liquid-solid interactions, named μSonic-hand. The bubble contained at the end of a micropipette and the surrounding liquid form a gas-liquid multiphase system susceptible to acoustic waves. Driven by a piezoelectric transducer, the oscillating gas-liquid interface induces acoustic microstreaming, markedly increasing the mass transfer efficiency. It enables multiple liquid micromanipulations, including mixing, dispersion, enhancing cell membrane permeability, and harvesting selected cells. Furthermore, a controllable three-dimensional axisymmetric vortex in an open environment overcomes the constraints of microfluidic chip, enabling stable trapping, rapid transportation, and multidirectional rotation of HeLa cells, embryos, and other bio-objects ranging from micrometers to millimeters. A variety of applications demonstrate that the μSonic-hand, with its wide-range capabilities, inherent biocompatibility, and extremely low cost could remarkably advance biomedical science.

Magnetic helical microrobots have attracted considerable attraction in microscale targeted delivery due to their high propulsion efficiency and movement flexibility. However, for biomedical applications in unstructured and multi-branched liquid environments, the capabilities of high swimming performance and precise selective control over a robot group of are essential. Here, we introduce a method for achieving high-performance propulsion and selective control of individual magnetic microrobots within a group by modulating surface wettability through localized surface microstructure modifications. We treated the surface of the helical microrobots with dimples and pimples of varying diameters and spacings to effectively distinguish the wettability. Our findings demonstrate that helical microrobots after surface modification exhibit higher step-out frequencies (${{{{\omega }}}_{\text{step} - \text{out}}}$) and maximum velocities (${{v}_{\mathrm{r} - \text{max}}}$), where the modified microrobots become more hydrophobic compared to the microrobots before modification. The variation of microrobots' step-out frequencies (${{{{\omega }}}_{\text{step} - \text{out}}}$) and the maximum velocities (${{v}_{\mathrm{r} - \text{max}}}$) correlate positively with the surface hydrophobicity. The swimming performance on the surface-modified microrobots is performed which demonstrates a maximum increase of 67% in forward velocity and 76% in step-out frequency. Furthermore, our method was effectively employed to actuate a helical microrobots group to achieve selective navigation in a multi-branched microchannel. We anticipate that this approach can be applied to achieve high-efficiency and precise targeted delivery in biomedical applications.

Owing to their high task efficiency and load capacity in closed space operations, multiple millirobots system has drawn extensive attention recently. However, the limited global magnetic fields and nonlinear interactions between individual robots make it challenging to control multiple millirobots in close proximity to each other, resulting in difficulty in achieving accurate paired interactions. Here, we propose a paired interactive control method for multiple millirobots, which enables the precise formation of two millirobots within a multiple millirobot system. The paired interactive motion is modeled within a singular point tracking framework to facilitate the implementation of an independent control strategy for multiple microrobots. Then, a data-driven actuation-movement mapping model for two millirobots is established as a nonlinear inversion controller, enabling the control system to rapidly achieve the desired state. To eliminate residual errors, a feedback controller is designed based on the active disturbance rejection concept, which estimates and eliminates generalized disturbances via an extended state observer. The control method is validated by accurately achieving planar formations via two millirobots both in isolation and within a multiple millirobot system, in which the root mean square error is less than 3% of the single-robot length.