Underwater computer vision plays a vital role in ocean research, enabling autonomous navigation, infrastructure inspections, and marine life monitoring. However, the underwater environment presents unique challenges, including color distortion, limited visibility, and dynamic light conditions, which hinder the performance of traditional image processing methods. Recent advancements in deep learning (DL) have demonstrated remarkable success in overcoming these challenges by enabling robust feature extraction, image enhancement, and object recognition. This review provides a comprehensive analysis of cutting-edge deep learning architectures designed for underwater object detection, segmentation, and tracking. State-of-the-art (SOTA) models, including AGW-YOLOv8, Feature-Adaptive FPN, and Dual-SAM, have shown substantial improvements in addressing occlusions, camouflaging, and small underwater object detection. For tracking tasks, transformer-based models like SiamFCA and FishTrack leverage hierarchical attention mechanisms and convolutional neural networks (CNNs) to achieve high accuracy and robustness in dynamic underwater environments. Beyond optical imaging, this review explores alternative modalities such as sonar, hyperspectral imaging, and event-based vision, which provide complementary data to enhance underwater vision systems. These approaches improve performance under challenging conditions, enabling richer and more informative scene interpretation. Promising future directions are also discussed, emphasizing the need for domain adaptation techniques to improve generalizability, lightweight architectures for real-time performance, and multi-modal data fusion to enhance interpretability and robustness. By critically evaluating current methodologies and highlighting gaps, this review provides insights for advancing underwater computer vision systems to support ocean exploration, ecological conservation, and disaster management.

Supernumerary robotic limbs (SRLs) are wearable robots designed to enhance the sensorimotor abilities of humans. SRLs can be used to compensate for lost functions in patients with motor deficits and, more in general, to augment the sensorimotor capabilities of humans to interact with the environment. The design and control of SRLs present several challenges. SRLs must have high levels of ergonomics and wearability and, depending on the application, they might also require enhanced robustness and strength. Supernumerary robotics differs from collaborative robotics, since SRLs are not collaborative external agents but rather act under the direct command of the operator who, thanks to the use of suitable interfaces, gains the control of some actions of the SRLs. From the control point of view, it is fundamental to find the right trade-off between the degrees of freedom that are under the direct control of the user and the level of robot autonomy. The adoption of feedback interfaces can help the human to better command and use the SRL. In this review, we discuss all these aspects, relating them to the current literature on SRLs. We also present the main applications and the potential of these relatively recent devices, as well as the main neuroscientific questions they rise on the implications of their use on the users’ body schema.

Advances in soft robotics and material science have enabled rapid progress in soft grippers. The ability to 3D print materials with softer, more elastic materials properties is a recent development and a key enabling technology for the rapid development of soft robots. However, obtaining the desired mechanical properties (e.g., stiffness) of the soft joints and information about the parameters to select in 3D printers is often not straightforward. In this article, we propose the use of interpenetrating phase composites (IPCs) materials with mathematically generated topologies based on triply periodic minimal surfaces for the development of soft grippers with desired mechanical properties. The flexible joints of the gripper can be realized through two or more phases that are topologically interconnected such that each phase represents a standalone cellular structure. As a case study, we present the design and development of a two-finger soft gripper as an example to demonstrate the application scenario of our approach. The flexible parts with desired stiffness values are realized by using IPCs materials in which the reinforcement distribution can be regulated on the basis of mathematical models. We characterized the properties of the material through a set of quantitative experiments on IPCs material specimens, and then we realized qualitative grasping tests with the gripper and a set of objects with different shapes and sizes. We showed that by properly regulating the properties of IPCs material it is possible to design modular grippers with the same structure, but different closure motions. Grippers can be customized for different tasks by easily assembling and disassembling fingers.

Through mechanization, automation, and intensification, there has been a substantial increase in agricultural production over time. The efficiency, reliability, and precision of agricultural equipment have improved significantly with automation, leading to a reduced dependency on human intervention. The surge in the adoption of agricultural robotics research and technologies is a response to the growing recognition that robots offer an effective solution to address the shortage of skilled workers in crop production. This paper aims to present a systematic overview of recent advancements in precision delivery technology within agricultural robotics, with a primary focus on the following aspects: (1) the precision agriculture market; (2) the design and development of spray robot technologies, encompassing both terrestrial and aerial platforms; (3) spray technologies and their application mechanisms; (4) various spraying techniques tailored to specific pests and vegetation; and (5) the evolution of sensor technologies for precision spraying. Additionally, this article explores the current state of the art in robotic technologies employed in precision agriculture.

The Fin-Ray principle, inspired by the physiology of fish rays, represents the foundation of a large number of robotic devices. However, despite their popularity, there is not any ad-hoc theoretical model technique for the analysis of this family of fingers. This lack is the main motivation of the presented work, which provides the mathematical modeling, analysis, and prototyping of a closed-chain Fin-Ray finger. In this scenario, the contribution of this article is twofold. At one end, we provide a general discrete Cosserat approach for the modeling of closed-chain soft robots which shares the geometrical structure of the rigid robotics counterpart. On the other end, the approach is employed to explore the family of Fin-Ray effect fingers. Finally, an improved design, which is able to conform to contacting surfaces, while maintaining stiffness out of its grasping plane, is fabricated and its performances are compared to those of a previously proposed prototype.

Tomatoes are a major crop worldwide, and accurately classifying their maturity is important for many agricultural applications, such as harvesting, grading, and quality control. In this paper, the authors propose a novel method for tomato maturity classification using a convolutional transformer. The convolutional transformer is a hybrid architecture that combines the strengths of convolutional neural networks (CNNs) and transformers. Additionally, this study introduces a new tomato dataset named KUTomaData, explicitly designed to train deep-learning models for tomato segmentation and classification. KUTomaData is a compilation of images sourced from a greenhouse in the UAE, with approximately 700 images available for training and testing. The dataset is prepared under various lighting conditions and viewing perspectives and employs different mobile camera sensors, distinguishing it from existing datasets. The contributions of this paper are threefold: firstly, the authors propose a novel method for tomato maturity classification using a modular convolutional transformer. Secondly, the authors introduce a new tomato image dataset that contains images of tomatoes at different maturity levels. Lastly, the authors show that the convolutional transformer outperforms state-of-the-art methods for tomato maturity classification. The effectiveness of the proposed framework in handling cluttered and occluded tomato instances was evaluated using two additional public datasets, Laboro Tomato and Rob2Pheno Annotated Tomato, as benchmarks. The evaluation results across these three datasets demonstrate the exceptional performance of our proposed framework, surpassing the state-of-the-art by 58.14%, 65.42%, and 66.39% in terms of mean average precision scores for KUTomaData, Laboro Tomato, and Rob2Pheno Annotated Tomato, respectively. This work can potentially improve the efficiency and accuracy of tomato harvesting, grading, and quality control processes.