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Over the past decade, soft robot research has expanded to diverse fields, including biomedicine, bionics, service robots, human–robot interaction, and artificial intelligence. Much work has been done in modeling the kinematics and dynamics of soft robots, but closed‐loop control is still in its early stages due to limited sensory feedback. Thanks to the advancement in functional materials, structures, and manufacturing techniques for flexible electronics, flexible and stretchable sensors are developing rapidly. These sensors provide feedback for closed‐loop control tasks and enable soft robots to effectively explore the unknown and safely interact with humans and the environment. Herein, recent advances in perceptive soft robots that utilize flexible/stretchable sensors and functional materials are outlined. The perceptive functions of soft robots from two different aspects, that is, proprioception and exteroception, are summarized. Furthermore, the constructions of autonomous soft robots by integrating both proprioceptive and exteroceptive capabilities for closed‐loop control tasks and other challenging tasks in the real world are discussed. Soft robots have shown potential in bionics, human–robot interaction, and artificial intelligence. To improve their interactivity and adaptability, developing closed‐loop control systems is essential. Herein, a review of the proprioceptive and exteroceptive functions and closed‐loop control systems is presented to provide readers with a better understanding of recent advances in perceptive intelligence for soft robotics.

The past decades have seen the rapid development of tactile sensors in material, fabrication, and mechanical structure design. The advancement of tactile sensors has heightened the expectation of sensor functions, and thus put forward a higher demand for data processing. However, conventional analysis techniques have not kept pace with the tactile sensor development and still suffer from some severe drawbacks, like cumbersome models, poor efficiency, and expensive costs. Machine learning, with its prominent ability for big data analysis and fast processing speed, can offer many possibilities for tactile data analysis. Herein, the machine learning techniques employed for processing tactile signals are reviewed. Supervised learning and unsupervised learning for analog signals are covered, and processing spike signals with machine learning are summarized. Furthermore, the applications in robotic tactile perception and human activity monitoring are presented. Finally, the current challenges and future prospects in sensors, data, algorithms, and benchmarks are discussed. Tactile sensors have developed rapidly, which increases the need for data processing. While machine learning, rather than conventional approaches, provides new support for tactile data analysis, thanks to its strong capacity for processing massive data quickly, the machine learning methods for tactile data processing and their applications are reviewed. The current issues and future directions are then discussed.

Microrobots have shown great potential in many applications, such as non‐invasive surgery, tissue engineering, precision medicine, and environmental remediation. Within the past decade, soft microrobot has become one of the important branches. It is aimed to create soft and deformable microrobots with high bioaffinity, which can perform complex tasks noninvasively in inaccessible small spaces in the body. Herein, the latest research progress of soft microrobots regarding the three cornerstones of this field is reviewed: material, fabrication, and actuation. First, various materials that are used for the fabrication of soft microrobots are summarized, and their characteristics and functions are discussed. Second, various fabrication methods of soft microrobots are introduced, and their applicability to different materials is discussed. Third, the actuation methods of soft microrobots are discussed, as well as their pros, cons, and adaptability. Moreover, the outstanding behaviors of soft microrobots in biomedical and environmental applications are introduced with some typical examples published recently. Finally, current clinical use challenges of soft microrobots are pointed out, and their intelligentization is proposed and discussed for further innovative development. Soft microrobots have emerged as a significant branch of microrobots due to their low invasiveness and safety, making them highly beneficial for biomedicine applications. The development of soft microrobots relies on the innovations of their materials, fabrication, and actuation. This review summarizes the recent progress, limitations, and prospects of soft microrobots, offering valuable inspiration for future research.

The application of stretchable strain sensors in human movement recognition, health monitoring, and soft robotics has attracted wide attention. Compared with traditional electronic conductors, stretchable ionic hydrogels are more attractive to organization‐like soft electronic devices yet suffer poor sensitivity due to limited ion conduction modulation caused by their intrinsic soft chain network. This paper proposes a strategy to modulate ion transport behavior by geometry‐induced strain concentration to adjust and improve the sensitivity of ionic hydrogel‐based strain sensors (IHSS). Inspired by the phenomenon of vehicles slowing down and changing lanes when the road narrows, the strain redistribution of ionic hydrogel is optimized by structural and mechanical parameters to produce a strain‐induced resistance boost. As a result, the gauge factor of the IHSS is continuously tunable from 1.31 to 9.21 in the strain range of 0–100%, which breaks through the theoretical limit of homogeneous strain‐distributed ionic hydrogels and ensures a linear electromechanical response simultaneously. Overall, this study offers a universal route to modulate the ion transport behavior of ionic hydrogels mechanically, resulting in a tunable sensitivity for IHSS to better serve different application scenarios, such as health monitoring and human–machine interface.

Recent achievements in the field of computer vision, reinforcement learning, and locomotion control have largely extended legged robots’ maneuverability in complex natural environments. However, little research focuses on sensing and analyzing the physical properties of the ground, which is crucial to robots’ locomotion during their interaction with highly irregular profiles, deformable terrains, and slippery surfaces. A biomimetic, flexible, multimodal sole sensor (FMSS) designed for legged robots to identify the ontological status and ground information, such as reaction force mapping, contact situation, terrain, and texture information, to achieve agile maneuvers was innovatively presented in this paper. The FMSS is flexible and large-loaded (20 Pa–800 kPa), designed by integrating a triboelectric sensing coat, embedded piezoelectric sensor, and piezoresistive sensor array. To evaluate the effectiveness and adaptability in different environments, the multimodal sensor was mounted on one of the quadruped robot’s feet and one of the human feet then traversed through different environments in real-world tests. The experiment’s results demonstrated that the FMSS could recognize terrain, texture, hardness, and contact conditions during locomotion effectively and retrain its sensitivity (0.66 kPa−1), robustness, and compliance. The presented work indicates the FMSS’s potential to extend the feasibility and dexterity of tactile perception for state estimation and complex scenario detection.

Objective The aim of this study is to ascertain whether COVID‐19 Omicron infection is associated with exacerbations in these myasthenia gravis (MG) patients. Result In total, 289 MG patients (comprising 60% females, with an average age of 46 ± 15 years) were enrolled. A total of 80.9% of MG patients reported a COVID‐19 infection, with the majority experiencing a benign course (88%). MG patients who experienced COVID‐19 infection demonstrated a higher likelihood of MG exacerbation, compared to those without the infection (18.8% vs. 7.3%, p = 0.039). In the survival analysis, after adjusting for confounding factors, the hazard ratio (HR) for exacerbation post‐infection was found to be 3.38 (95% CI 1.20–9.53, p = 0.021). Compared to the exacerbation rates observed in JTA21, an increase was noted in DTM23 among COVID‐19‐infected MG patients (4.4% vs. 17.2%, p < 0.001). Conclusion The COVID‐19 is the risk of MG exacerbation. This retrospective cohort study employed questionnaires to investigate the COVID‐19 infection status and exacerbation of myasthenia gravis (MG) patients after the relaxation of prevention and control measures in China. Statistical results demonstrate that COVID‐19 infection poses a significant risk for MG exacerbation, compared to the MG group without COVID‐19.

Epidermal sensing devices, which mimic functionalities and mechanical properties of natural skin, offer great potential for real-time health monitoring via continuous checking of vital signs. However, most existing skin-mounted electronics use a flexible film with high elastic modulus, which hinders physical activity and causes interfacial delamination and skin irritation. The compliance of hydrogel-based devices can firmly conform to complex, curved surfaces without introducing excessive interfacial stresses. However, most hydrogels still suffer from the weakness of stable and reproducible sensing. In this work, we report a skin-friendly epidermal electronic made of a resilient, self-healing, and recyclable polyvinyl alcohol (PVA) hydrogel. The hydrogel is reinforced through a ternary heterogeneous network for good mechanical robustness while maintaining high stretchability and exceptional conformability. Simultaneously, the abundant dynamic hydrogen bonds give the hydrogel rapid self-healing ability. The assembled hydrogel epidermal electronic is able to stably monitor multiple physiological signals as well as sense the strain level of the skin motion and joint bending. The unique, versatile, environmental and biological friendly epidermal electronics will have broad applications in health care, human-machine interface, augmented reality, and so on.

Hydrogels are stretchable ion conductors that can be used as strain sensors by transmitting strain-dependent electrical signals. However, hydrogels are susceptible to dehydration in the air, leading to a loss of flexibility and functions. Here, a simple and general strategy for encapsulating hydrogel with hydrophobic elastomer is proposed to realize excellent water-retention capacity. Elastomers, such as polydimethylsiloxanes (PDMS), whose hydrophobicity and dense crosslinking network can act as a barrier against water evaporation (lost 4.6 wt.% ± 0.57 in 24 h, 28 °C, and ≈30% humidity). To achieve strong adhesion between the hydrogel and elastomer, a porous structured thermoplastic polyurethane (TPU) is used at the hydrogel-elastomer interface to interlock the hydrogel and bond the elastomer simultaneously (the maximum interfacial toughness is over 1200 J/m2). In addition, a PDMS encapsulated ionic hydrogel strain sensor is proposed, demonstrating an excellent water-retention ability, superior mechanical performance, highly linear sensitivity (gauge factor = 2.21, at 100% strain), and robust interface. Various human motions were monitored, proving the effectiveness and practicability of the hydrogel-elastomer hybrid.

Tactile perception in artificial systems remains constrained by the von Neumann architecture, where the separation of memory and computation leads to significant latency and energy inefficiency. Neuromorphic engineering provides a biologically inspired alternative by adopting event-driven, spike-based coding, akin to neural signaling in human somatosensory systems. This review systematically examines spike-based neural coding techniques for tactile perception, focusing on three key aspects: encoding strategies, neuromorphic hardware implementations, and decoding methodologies. It compares rate coding and temporal coding in terms of biological plausibility and computational efficiency, particularly in dynamic and high-speed tactile tasks. A range of hardware platforms is evaluated, including oscillator-based encoding circuits, CMOS and memristor-based spiking neurons, and self-powered tactile sensors using triboelectric nanogenerators. On the decoding side, mechanisms such as spike-timing-dependent plasticity and spiking neural networks are analyzed for their potential to support adaptive, online learning in tactile systems. The review emphasizes co-design approaches that integrate sensing, encoding, and processing within a unified framework to achieve system-level efficiency. By bridging advances in functional materials, low-power hardware, and brain-inspired computation, this work outlines a roadmap toward artificial tactile systems with millisecond-level latency, sub-milliwatt power consumption, and high perceptual fidelity. These capabilities are essential for future applications in robotics, prosthetics, and wearable electronics.

Many insects have the capacity to increase their resistance to high temperatures by undergoing heat hardening at nonlethal temperatures. Although this response is well established, its molecular underpinnings have only been investigated in a few species where it seems to relate at least partly to the expression of heat shock protein (Hsp) genes. Here, we studied the mechanism of hardening and associated transcription responses in larvae of two invasive fruit fly species in China, Bactrocera dorsalis and Bactrocera correcta. Both species showed hardening which increased resistance to 45°C, although the more widespread B. dorsalis hardened better at higher temperatures compared to B. correcta which hardened better at lower temperatures. Transcriptional analyses highlighted expression changes in a number of genes representing different biochemical pathways, but these changes and pathways were inconsistent between the two species. Overall B. dorsalis showed expression changes in more genes than B. correcta. Hsp genes tended to be upregulated at a hardening temperature of 38°C in both species, while at 35°C many Hsp genes tended to be upregulated in B. correcta but not B. dorsalis. One candidate gene (the small heat shock protein gene, Hsp23) with a particularly high level of upregulation was investigated functionally using RNA interference (RNAi). We found that RNAi may be more efficient in B. dorsalis, in which suppression of the expression of this gene removed the hardening response, whereas in B. correcta RNAi did not decrease the hardening response. The different patterns of gene expression in these two species at the two hardening temperatures highlight the diverse mechanisms underlying hardening even in closely related species. These results may provide target genes for future control efforts against such pest species.