Soft actuators have received extensive attention in the fields of soft robotics, biomedicine, and intelligence systems owing to their advantages of pliancy, silence, and essential safety. However, most existing soft actuators have only single actuation elements and lack sensing. Therefore, it is difficult for them to perform complex motions with multiple degrees of freedom (multi-DOFs) and high precision. This article reports a miniature columnar dielectric elastomer actuator (DEA) with multi-DOF actuation and sensing, which was fabricated with an electroactive polymer acrylic film (Very High Bond [VHB] acrylic film by 3M Company) and carbon black grease electrodes. The arrangement of the simulation electrodes on the VHB was optimized to realize multi-DOF actuation, and the sensing electrodes were configured on the outer part of the DEA to realize real-time sensing. The results showed that the soft actuator can achieve all-round actuation through the selective power of the stimulation electrodes with a controllable voltage. The maximum bending angle and axial strain of the actuator reached 50° and 13%, respectively. Moreover, the deformation modes, direction, and quantity could be precisely measured using the integrative sensing function. In addition, to demonstrate the advantages of the proposed actuator, a manipulator with multiple actuators was designed and controlled to realize different actions of screwing and grasping with sensing. This research is useful not only for the design of multifunctional soft actuators but also for the development of soft robots with flexible, complex, and precisely controllable motions.

We propose label-free and nondestructive on-chip purification and characterization of optogenetically engineered cells, thus significantly enhancing their application performance.

The rapid evolution of next-generation electronics urgently demands high-performance functional materials. Two-dimensional (2D) semiconductors, characterized by tunable bandgaps, magnetic properties, and excellent optical and electronic properties, hold significant potential for applications in nanoelectronic devices, magnetic storage, and optoelectronics. However, the high computational cost of traditional Density Functional Theory (DFT) severely restricts large-scale high-throughput screening. Meanwhile, problems such as insufficient datasets and non-uniform data quality remain prevalent. Against this background, machine learning (ML), which captures intricate nonlinear correlations and accelerates the discovery of novel materials, has emerged as an efficient technical approach. This review systematically summarizes recent advances in ML-driven property prediction for 2D semiconductors. It first elaborates the fundamental properties and classifications of 2D semiconductors, and then compares traditional computational simulations with ML algorithms, clarifying the distinct advantages of data-driven approaches. Subsequently, this work focuses on the latest progress in predicting critical properties, including bandgap, magnetism, and other physical characteristics. For bandgap prediction, classical algorithms such as random forests are compared with deep learning models represented by graph neural networks. The results demonstrate that deep learning performs much better in low-data regimes and complex material systems. For magnetic property prediction, the impact of feature engineering strategies on model accuracy and efficiency is systematically analyzed. In addition, the research progress of other physical property prediction tasks is briefly summarized. Finally, future research directions for machine learning, including standardized materials databases, physics-informed machine learning, multimodal modeling, and the integration of machine learning with experimental and theoretical methods, are outlined to address challenges in data quality, model interpretability, and cross-system generalization ability. This work aims to provide a systematic theoretical foundation and methodological guidance for research on two-dimensional semiconductor materials assisted by machine learning.

Due to their high compliance, environmental adaptability, and inherent safety in human–robot interaction, soft robots have demonstrated considerable potential, including medical rehabilitation, compliant grasping, and operations in complex environments. Unlike conventional robots relying on rigid structures and discrete joint motions, soft robots use flexible materials such as elastomers and hydrogels to achieve motion and functionality through continuous deformation. Despite providing excellent adaptability, these compliant materials also introduce complex behaviors, including continuous morphological changes, material nonlinearities, time-varying properties, and environmental coupling, posing substantial challenges to modeling, control, and motion planning. In recent years, artificial intelligence (AI), through nonlinear representation, data-driven learning, adaptive optimization, and autonomous decision-making, has provided new technical pathways for addressing these challenges. Unlike previous reviews organized primarily by AI algorithm type, this work narratively reviews 39 studies and organizes AI-driven approaches according to the soft robotic system implementation process, encompassing modeling, control, and motion planning. Specifically, in modeling, neural networks, Koopman representations, and physics–data fusion methods have enhanced the representation of complex dynamical relationships. In control, learning-augmented control and reinforcement learning have reduced reliance on accurate system models and improved adaptability under uncertainty. In motion planning, learning from demonstration, reinforcement learning, and intelligent optimization methods have strengthened the capability for autonomous motion generation under complex constraints. Finally, this paper summarizes the current state of the field and discusses future trends in AI-driven soft robotics, emphasizing stronger coordination across functional modules. Closed-loop integration of prediction, decision-making, control, and feedback updates may further advance soft robots from stage-specific intelligence toward system-level autonomy.

While soft hand exoskeleton robots have approached human-level dexterity in terms of degrees of freedom, precise control methods for fine motor movements remain a significant challenge. Surface electromyography (sEMG) is widely employed in gesture recognition to enable patients to independently control a soft hand exoskeleton. However, individual finger control remains challenging through sEMG-based control due to the complexity of decoupling synergistic muscle activities. In this study, we propose a muscle fiber-based ultrasound perception strategy for fine hand motion recognition and soft hand exoskeleton control. Ultrasound imaging enables non-invasive visualization of forearm muscle morphology and provides information associated with underlying muscle-fiber activity. By reconstructing muscle morphology from ultrasound images, biologically relevant muscle-fiber features are extracted and fused to characterize fine hand movements. A lightweight Random Forest classifier is subsequently employed to map these biologically informed features to discrete hand actions, providing a computationally efficient recognition module for real-time control. To the best of our knowledge, publicly available ultrasound image datasets specifically designed for fine hand gesture recognition in rehabilitation applications remain limited. In the experiments, a dataset containing 21 hand gestures based on muscle ultrasound images was constructed to evaluate the proposed method. All data were collected from healthy participants as a preliminary proof-of-concept investigation. The results show that the proposed approach achieves an average recognition accuracy of 95.24% across three subjects in finger motion recognition. This preliminary study demonstrates the potential of machine learning-based ultrasound perception for improving fine hand gesture recognition and providing an intuitive control interface for soft hand exoskeletons, thereby enhancing their applicability in hand rehabilitation scenarios.