Plastic pollution in water bodies threatens and disrupts aquatic life, requiring effective cleanup solutions. This paper proposes a strategy for plastic cleanup using a fleet of autonomous surface vehicles in a multitask scenario, with a focus on both exploration and cleaning tasks. The mission is decoupled into two phases: an exploration phase for locating trash and a cleaning phase for collection. A Multitask Deep Q-Network with two heads estimates Q-values for each task, and all ASVs share the same policy through an egocentric state formulation to enhance scalability. A multiobjective learning approach is applied, resulting in distinct policies that balance the duration of the exploration and cleaning phases, leading to the construction of a Pareto front, which provides a visual representation of trade-offs between task priorities. The framework adapts to various environmental conditions, demonstrated in both the larger Malaga Port and the smaller Alamillo Lake. The study also highlights the importance of a dedicated exploration phase for larger areas, while minimal exploration is sufficient for smaller spaces. Compared to the decomposition weighting sum strategy, the approach consistently produces superior Pareto-optimal policies, ensuring broader and more effective exploration of the objective space.
The urinary bladder is considered a highly complex organ, capable not only of storing urine but also of sensing intra-vesical volume and dynamically expanding and contracting. Consequently, fully replicating its functions following radical cystectomy remains a significant technological challenge. Hereinafter, an implantable robotic bladder is presented that can change shape and expand its internal volume up to 400 mL, based on the amount of urine collected from kidneys, and monitor the volume in real-time. It can apply on-demand mechanical compression to assist urination, by means of an origami-designed enclosure, coupled to miniaturized mechatronic components. In vitro characterization in a human phantom is demonstrated, and volume monitoring is validated following a realistic filling routine. The tests demonstrate successful expansions for collecting urine, with an average volume reconstruction error of 8.4 ± 6.1 mL, and then 99% of the volume is voided in less than 2 min. The work paves the way for developing active robotic solutions and reproducing bladder functions in patients with cancer and organ removal or impairment.
Self-adaptive, easy-to-control, and low-cost gripper devices are indispensable in manufacturing and agriculture. However, the existing soft grippers cannot provide high response speed and firm grasping. Inspired by the natural, active entanglement behaviors of animals and plants, a rapid, cilia-like soft gripper design is proposed for grasping various objects via envelopes formed by the self-entanglement of multiple hollowed silicone tubes. The basic entanglement unit comprises a hollow, soft silicone tube with an actuation wire inside, which leads to compression and entanglement by fixing the front of the tube and drawing the actuation wire. Using multiple entanglement units enables sufficient mechanical interlocking between deformed tubes and grasped objects, avoiding the reliance on contact force control. Experimental results demonstrate that the developed soft gripper, with a cost lower than one dollar, can complete adaptive grasping within 1 s. The grasping success rate can reach 100% in grasping common irregular-shaped daily objects within the effective grasping range of the entanglement units. The design paves the way for harnessing the potential of embodied intelligence in soft robots, enabling fast and universal grasping.
Interventionalist catheterization actions are essential for assessing tool navigation quality and procedural competence during interventions. Traditional assessment methods are subjective, lack immediate feedback, and limit timely performance improvement. To address these limitations, this study introduces a deep-learning framework designed to systematically analyze catheterization action data, address inherent class imbalances, and enable real-time action recognition. First, the proposed framework leverages advanced generative models to augment minority action classes, thus enhancing data representation and ensuring accurate recognition of catheterization actions. The six generative models utilized in this study undergo rigorous evaluation, achieving high fidelity with average precision and F1-scores exceeding 94% across all models except CTGAN. Second, a convolutional neural network (IAR-Net) tailored to recognize seven distinct catheterization actions is developed. Evaluated using the augmented dataset, IAR-Net achieves an accuracy of 98.9%, surpassing current benchmarks. Comparative analysis with state-of-the-art machine learning and transformer-based models designed for tabular data confirms IAR-Net's performance and robustness in recognizing catheterization actions. Lastly, interpretability methods are incorporated to elucidate the model's decision-making process, improving understanding and increasing the trustworthiness of predictions. These outcomes offer a promising avenue for enhancing trainee assessment and training protocols, thereby accelerating the acceptance and integration of robot-assisted endovascular systems into clinical practice.
This study presents a new method for measuring the effective stiffness of curved actuators. Actuators are loaded into tension, and analytical mechanical equilibrium formulations are used to determine the stress along the actuator. A new mechanical metric, Shape Actuation Modulus (SAM), defines the effective stiffness of the actuator during loading as the ratio of stress change to radius of curvature change. Conductive polylactic-acid shape-memory actuators are produced to benchmark this novel methodology. These actuators display a linear behavior between 25 and 50 mm radius of curvature with SAM of 3.8±0.9 MPa at 50 mm. The interval on which the radius of curvature to stress relationship is linear can be controlled by choosing the radius of curvature of the hinge. For instance, SAM calculation with R2 > 0.97 was achieved in ranges of [22.7;79.6] mm and [16.4;51.5]mm for starting radius of curvature of 23.5±0.7 mm and 17.2±0.6 mm, respectively. Hence, the new technique proposed provides guidelines to design actuators. Finally, a comparison of bio-composite actuators made of the same material was conducted. The hygromnemic actuators tested displayed a stiffness more than one order of magnitude larger than the hygromorphic ones for the range of radius of curvature [20;100]mm.
This study investigates the application of Riemannian geometry-based methods for brain decoding using invasive electrophysiological recordings. While Riemannian geometry has been successfully applied in noninvasive settings, its utility for invasive datasets, which are typically smaller and scarcer, remains less explored. Herein, a minimum distance to mean (MDM) classifier is proposed using a Riemannian geometry approach based on covariance matrices extracted from intracortical local field potential (LFP) recordings across various regions during different brain state dynamics. For benchmarking, the performance of the approach is evaluated against convolutional neural networks (CNNs) and Euclidean MDM classifiers. The results indicate that the Riemannian geometry-based classification not only achieves a superior mean F1 macro-averaged score across different channel configurations but also requires up to two orders of magnitude less computational training time. Additionally, the geometric framework reveals distinct spatial contributions of brain regions across varying brain states, suggesting a state-dependent organization that traditional time series-based methods often fail to capture. The findings align with previous studies supporting the efficacy of geometry-based methods and extend their application to invasive brain recordings, highlighting their potential for broader clinical use, such as brain-computer interface applications.
Neuromorphic devices, inspired by the human brain's efficiency and adaptability, hold great potential for artificial intelligence (AI) hardware to overcome the limitations of traditional von Neumann architecture. As a subclass, multimodal and multifunctional neuromorphic devices have recently gained a lot of attention due to their advantages in in-sensor computing and sophisticated behaviors. In this review, recent advances in materials, device structures, and applications in this field are systematically presented. It includes optical, electrical, mechanical, and chemical sensing in multimodal neuromorphic device, which enable in-sensor computing to minimize energy consumption and enhance real-time decision-making. The materials applied in this field such as phase-change, 2D materials, and ferroelectrics are summarized for their roles in achieving synaptic plasticity, nonvolatile memory for multifunctional neuromorphic devices. Structural innovations, including reconfigurable, multi-terminal, and 3D-integrated designs, further optimize parallel processing and multifunctional integration. Besides, application scenarios of multimodal and multifunctional neuromorphic devices and their advantages for improving the efficiency of AI are reviewed. Finally, challenges in material stability and commercialization are discussed, it emphasizes the need for interdisciplinary efforts to bridge the gap. This review provides critical insights and future directions for developing brain-inspired, energy-efficient AI hardware.
Bacterial cellulose (BC) is an emerging smart material, synthesized through microbial fermentation of environmentally friendly substrates, including organic waste. When functionalized with ionic liquids (ILs) and coated with conductive polymers, BC forms soft, sustainable, and electroactive composites, making it suitable for sensors in soft robotics, wearable, biomedical, and environmental monitoring applications. However, modeling frameworks for BC–IL sensors are still lacking, hindering their integration into real-world applications. To bridge this gap and support smart material design, we propose a novel first-principle white-box modeling framework is proposed that couples a 2D finite element method (FEM) for mechanical deformation with 1D FEM sub-models for ion transport and voltage generation. Specifically, this work introduces the first dual-carrier multiphysics model for mechanoelectric transduction in BC–IL sensors. The model, experimentally calibrated and validated, resolves the spatio-temporal dynamics of mechanical deformation and dual-ion transport, including diffusion, electromigration, and advection. By explicitly incorporating the transport and interaction of both cations and anions, previously neglected in smart-sensors modeling, the proposed strategy provides a foundational simulation framework for the scalable, rapid, and intelligent design of next-generation biodegradable and multifunctional smart sensors, advancing the integration of green materials into intelligent systems.
Sensory feedback systems allow soft robots to interact and respond to their environment through embedded or external sensors. These sensors often rely on electronic components for signal interpretation and processing, which increases system complexity, reduces robustness under hazardous conditions, and limits the adaptability of the robots. Reducing complexity and improving adaptability in soft robots requires the development of electronics-free control systems. A 3D-printed, electronics-free sensory system is integrated into a six-legged soft robot, increasing its adaptability by enabling obstacle detection and directional change of locomotion using pneumatic logic gates. Pneumatic systems enable smooth, nature-like movements and can operate safely in environments where electronics might fail. The results show rapid sensor response times (1.41–1.52 s), low required input forces (1.07–4.62 N) of the sensor, and walking speeds up to 0.17 body lengths per second. Operating at 225 kPa with 13.64 ln min−1 of compressed air in tethered mode, the robot also functions autonomously with a CO2 cartridge. Integrated pneumatic grippers enhance their utility for object retrieval. The design achieves a new level of autonomy and versatility, advancing electronics-free control systems, while maintaining cost efficiency. These findings lay the foundation for future innovations in increasingly autonomous electronic-free soft robots.
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