Tactile sensors have a wide range of applications, from utilization in robotic grippers to human motion measurement. If tactile sensors could be fabricated and integrated more easily, their applicability would further expand. In this study, we propose a tactile sensor-M3D-skin-that can be easily fabricated with high versatility by leveraging the infill patterns of a multi-material fused deposition modeling (FDM) 3D printer as the sensing principle. This method employs conductive and non-conductive flexible filaments to create a hierarchical structure with a specific infill pattern. The flexible hierarchical structure deforms under pressure, leading to a change in electrical resistance, enabling the acquisition of tactile information. We measure the changes in characteristics of the proposed tactile sensor caused by modifications to the hierarchical structure. Additionally, we demonstrate the fabrication and use of a multi-tile sensor. Furthermore, as applications, we implement motion pattern measurement on the sole of a foot, integration with a robotic hand, and tactile-based robotic operations. Through these experiments, we validate the effectiveness of the proposed tactile sensor.

In this study, we classify the magnetic chirality of solar filaments from H-Alpha observations using state-of-the-art image classification models. We establish the first reproducible baseline for solar filament chirality classification on the MAGFiLO dataset. The MAGFiLO dataset contains over 10,000 manually-annotated filaments from GONG H-Alpha observations, making it the largest dataset for filament detection and classification to date. Prior studies relied on much smaller datasets, which limited their generalizability and comparability. We fine-tuned several pre-trained, image classification architectures, including ResNet, WideResNet, ResNeXt, and ConvNeXt, and also applied data augmentation and per-class loss weights to optimize the models. Our best model, ConvNeXtBase, achieves a per-class accuracy of 0.69 for left chirality filaments and $0.73$ for right chirality filaments.

Abstract-- We present a computational framework for simulating filaments interacting with rigid bodies through contact. Filaments are challenging to simulate due to their codimen-sionality, i.e., they are one-dimensional structures embedded in three-dimensional space. Existing methods often assume that filaments remain permanently attached to rigid bodies. Our framework unifies discrete elastic rod (DER) modeling, a pressure field patch contact model, and a convex contact formulation to accurately simulate frictional interactions between slender filaments and rigid bodies - capabilities not previously achievable. Owing to the convex formulation of contact, each time step can be solved to global optimality, guaranteeing complementarity between contact velocity and impulse. Finally, we demonstrate its applicability in both soft robotics, such as a stochastic filament-based gripper, and deformable object manipulation, such as shoelace tying, providing a versatile simulator for systems involving complex filament-filament and filament-rigid body interactions.

Breakthroughs, discoveries, and DIY tips sent every weekday. The golden silk, a luxury once reserved for Roman emperors, has been recreated by modern scientists. In a study published in Advanced Materials, a research team at POSTECH (Pohang University of Science and Technology) announced they have successfully produced the 2,000-year-old textile known as Sea Silk. They accomplished this using threads from the common pen shell, farmed along the Korean coast. The team's work also explains the origin of the material's characteristic golden hue and its famed resistance to fading over millennia.

Soft machines are poised to deliver significant real-world impact, with soft robotics emerging as a key sub-discipline. This field integrates biological inspiration, materials science, and embodied intelligence to create bio-robotic hybrids, blurring the boundary between engineered systems and biology. Over the past 15 years, research in fluidically controlled soft robots has led to commercialised systems that leverage "softness" to improve human-machine interaction or to handle delicate objects. However, translating laboratory advancements into scalable applications remains challenging due to difficulties in prototyping and manufacturing ultra-flexible materials, as well as the absence of standardised design processes. Here we show that the Flex Printer, an open-source, low-cost FDM platform, enables reliable printing of ultra-flexible soft robots with embedded fluidic logic. By employing an innovative upside-down print orientation, the system significantly expands the range of printable geometries. We demonstrate how this approach allows robots to autonomously walk off the print bed immediately after fabrication - a milestone achievement in soft robotics. This work provides a foundation for standardisation and scalable manufacturing, critical for accelerating the field's impact. More broadly, by lowering barriers to entry, this platform has the potential to democratise soft robotics research and facilitate the development of new applications. We invite the community to contribute to the shared development of this technology to drive the next wave of breakthroughs in soft robotics.

Solar filaments are one of the most prominent features observed on the Sun, and their evolutions are closely related to various solar activities, such as flares and coronal mass ejections. Real-time automated identification of solar filaments is the most effective approach to managing large volumes of data. Existing models of filament identification are characterized by large parameter sizes and high computational costs, which limit their future applications in highly integrated and intelligent ground-based and space-borne observation devices. Consequently, the design of more lightweight models will facilitate the advancement of intelligent observation equipment. In this study, we introduce Flat U-Net, a novel and highly efficient ultralightweight model that incorporates simplified channel attention (SCA) and channel self-attention (CSA) convolutional blocks for the segmentation of solar filaments in full-disk H$\alpha$ images. Feature information from each network layer is fully extracted to reconstruct interchannel feature representations. Each block effectively optimizes the channel features from the previous layer, significantly reducing parameters. The network architecture presents an elegant flattening, improving its efficiency, and simplifying the overall design. Experimental validation demonstrates that a model composed of pure SCAs achieves a precision of approximately 0.93, with dice similarity coefficient (DSC) and recall rates of 0.76 and 0.64, respectively, significantly outperforming the classical U-Net. Introducing a certain number of CSA blocks improves the DSC and recall rates to 0.82 and 0.74, respectively, which demonstrates a pronounced advantage, particularly concerning model weight size and detection effectiveness. The data set, models, and code are available as open-source resources.

In this article, an active contours without edges (ACWE)-based algorithm has been proposed for the detection of solar filaments in H-alpha full-disk solar images. The overall algorithm consists of three main steps of image processing. These are image pre-processing, image segmentation, and image post-processing. Here in the work, contours are initialized on the solar image and allowed to deform based on the energy function. As soon as the contour reaches the boundary of the desired object, the energy function gets reduced, and the contour stops evolving. The proposed algorithm has been applied to few benchmark datasets and has been compared with the classical technique of object detection. The results analysis indicates that the proposed algorithm outperforms the results obtained using the existing classical algorithm of object detection.

The Solar Ultraviolet Imaging Telescope(SUIT) onboard Aditya-L1 is an imager that observes the solar photosphere and chromosphere through observations in the wavelength range of 200-400 nm. A comprehensive understanding of the plasma and thermodynamic properties of chromospheric and photospheric morphological structures requires a large sample statistical study, necessitating the development of automatic feature detection methods. To this end, we develop the feature detection algorithm SPACE-SUIT: Solar Phenomena Analysis and Classification using Enhanced vision techniques for SUIT, to detect and classify the solar chromospheric features to be observed from SUIT's Mg II k filter. Specifically, we target plage regions, sunspots, filaments, and off-limb structures. SPACE uses You Only Look Once(YOLO), a neural network-based model to identify regions of interest. We train and validate SPACE using mock-SUIT images developed from Interface Region Imaging Spectrometer(IRIS) full-disk mosaic images in Mg II k line, while we also perform detection on Level-1 SUIT data. SPACE achieves an approximate precision of 0.788, recall 0.863 and MAP of 0.874 on the validation mock SUIT FITS dataset. Given the manual labeling of our dataset, we perform "self-validation" by applying statistical measures and Tamura features on the ground truth and predicted bounding boxes. We find the distributions of entropy, contrast, dissimilarity, and energy to show differences in the features. These differences are qualitatively captured by the detected regions predicted by SPACE and validated with the observed SUIT images, even in the absence of labeled ground truth. This work not only develops a chromospheric feature extractor but also demonstrates the effectiveness of statistical metrics and Tamura features for distinguishing chromospheric features, offering independent validation for future detection schemes.

Abstract-- The purpose of this study is to construct a contact point estimation system for the both side of a finger, and to realize a motion of bending the finger after inserting the finger into a tool (hereinafter referred to as the bending after insertion motion). In order to know the contact points of the full finger including the joints, we propose to fabricate a nerve inclusion flexible epidermis by combining a flexible epidermis and a nerve line made of conductive filaments, and estimate the contact position from the change of resistance of the nerve line. A nerve inclusion flexible epidermis attached to a thin fingered robotic hand was combined with a twin-armed robot and tool use experiments were conducted. The contact information can be used for tool use, confirming the effectiveness of the proposed method. I. Introduction A. Outline of the Bending after Insertion Motion degree of freedom to grasp and use scissors.

Abstract-- Developing whole-body tactile skins for robots remains a challenging task, as existing solutions often prioritize modular, one-size-fits-all designs, which, while versatile, fail to account for the robot's specific shape and the unique demands of its operational context. In this work, we introduce the GenTact Toolbox, a computational pipeline for creating versatile whole-body tactile skins tailored to both robot shape and application domain. Our pipeline includes procedural mesh generation for conforming to a robot's topology, task-driven simulation to refine sensor distribution, and multi-material 3D printing for shape-agnostic fabrication. This work represents a shift from "one-size-fits-all" tactile sensors toward context-driven, highly adaptable designs that can be customized for a wide range of robotic systems and applications. Whole-body tactile skins are sensors designed to give a robot the sense of touch over the full integration levels because it requires manual assembly and surface of its body.