Suckers are significant for robots in picking, transferring, manipulation and locomotion on diverse surfaces. However, most of the existing suckers lack high-fidelity perceptual and tactile sensing, which impedes them from resolving the fine-grained geometric features and interaction status of the target surface. This limits their robust performance with irregular objects and in complex, unstructured environments. Inspired by the adaptive structure and high-performance sensory capabilities of cephalopod suckers, in this paper, we propose a novel, intelligent sucker, named SuckTac, that integrates a camera-based tactile sensor directly within its optimized structure to provide high-density perception and robust suction. Specifically, through joint structure design and optimization and based on a multi-material integrated casting technique, a camera and light source are embedded into the sucker, which enables in-situ, high-density perception of fine details like surface shape, texture and roughness. To further enhance robustness and adaptability, the sucker's mechanical design is also optimized by refining its profile, adding a compliant lip, and incorporating surface microstructure. Extensive experiments, including challenging tasks such as robotic cloth manipulation and soft mobile robot inspection, demonstrate the superior performance and broad applicability of the proposed system.

The primary objective of this study was to design the grabbing technique used to determine the vacuum suction gripper and its design parameters for the pocket welting operation in apparel manufacturing. It presents the application of vacuum suction in grabbing technology, a technique that has revolutionized the handling and manipulation to grasp the various fabric materials in a range of garment industries. Vacuum suction, being non-intrusive and non-invasive, offers several advantages compared to traditional grabbing methods. It is particularly useful in scenarios where soft woven fabric and air-impermeable fabric items need to be handled with utmost care. The paper delves into the working principles of vacuum suction, its various components, and the underlying physics involved. Furthermore, it explores the various applications of vacuum suction in the garment industry into the automation exploration. The paper also highlights the challenges and limitations of vacuum suction technology and suggests potential areas for further research and development.

Continuum robots with variable stiffness have gained wide popularity in the last decade. Layer jamming (LJ) has emerged as a simple and efficient technique to achieve tunable stiffness for continuum robots. Despite its merits, the development of a control-oriented dynamical model tailored for this specific class of robots remains an open problem in the literature. This paper aims to present the first solution, to the best of our knowledge, to close the gap. We propose an energy-based model that is integrated with the LuGre frictional model for LJ-based continuum robots. Then, we take a comprehensive theoretical analysis for this model, focusing on two fundamental characteristics of LJ-based continuum robots: shape locking and adjustable stiffness. To validate the modeling approach and theoretical results, a series of experiments using our \textit{OctRobot-I} continuum robotic platform was conducted. The results show that the proposed model is capable of interpreting and predicting the dynamical behaviors in LJ-based continuum robots.

The extent to which advanced waveform analysis of non-invasive physiological signals can diagnose levels of hypovolemia remains insufficiently explored. The present study explores the discriminative ability of a deep learning (DL) framework to classify levels of ongoing hypovolemia, simulated via novel dynamic lower body negative pressure (LBNP) model among healthy volunteers. We used a dynamic LBNP protocol as opposed to the traditional model, where LBNP is applied in a predictable step-wise, progressively descending manner. This dynamic LBNP version assists in circumventing the problem posed in terms of time dependency, as in real-life pre-hospital settings, intravascular blood volume may fluctuate due to volume resuscitation. A supervised DL-based framework for ternary classification was realized by segmenting the underlying noninvasive signal and labeling segments with corresponding LBNP target levels. The proposed DL model with two inputs was trained with respective time-frequency representations extracted on waveform segments to classify each of them into blood volume loss: Class 1 (mild); Class 2 (moderate); or Class 3 (severe). At the outset, the latent space derived at the end of the DL model via late fusion among both inputs assists in enhanced classification performance. When evaluated in a 3-fold cross-validation setup with stratified subjects, the experimental findings demonstrated PPG to be a potential surrogate for variations in blood volume with average classification performance, AUROC: 0.8861, AUPRC: 0.8141, $F1$-score:72.16%, Sensitivity:79.06 %, and Specificity:89.21 %. Our proposed DL algorithm on PPG signal demonstrates the possibility of capturing the complex interplay in physiological responses related to both bleeding and fluid resuscitation using this challenging LBNP setup.

The ability to adapt and conform to angular and uneven surfaces improves the suction cup's performance in grasping and manipulation. However, in most cases, the adaptation costs lack of required stiffness for manipulation after surface attachment; thus, the ideal scenario is to have compliance during adaptation and stiffness after attachment to the surface. Nevertheless, most stiffness modulation techniques in suction cups require additional actuation. This article presents a new stiffness tunable suction cup that adapts to steep angular surfaces. Using granular jamming as a vacuum driven stiffness modulation provides a sensorless for activating the mechanism. Thus, the design is composed of a conventional active suction pad connected to a granular stalk, emulating a hinge behavior that is compliant during adaptation and has high stiffness after attachment is ensured. During the experiment, the suction cup can adapt to angles up to 85 degrees with force lower than 0.5 N. We also investigated the effect of granular stalk's length on the adaptation and how this design performs compared to passive adaptation without stiffness modulation.

This paper presents a compact system, OpenPneu, to support the pneumatic actuation for multi-chambers on soft robots. Micro-pumps are employed in the system to generate airflow and therefore no extra input as compressed air is needed. Our system conducts modular design to provide good scalability, which has been demonstrated on a prototype with ten air channels. Each air channel of OpenPneu is equipped with both the inflation and the deflation functions to provide a full range pressure supply from positive to negative with a maximal flow rate at 1.7 L/min. High precision closed-loop control of pressures has been built into our system to achieve stable and efficient dynamic performance in actuation. An open-source control interface and API in Python are provided. We also demonstrate the functionality of OpenPneu on three soft robotic systems with up to 10 chambers.