Water potential explains water transport in the soil–plant–atmosphere continuum (SPAC), and is gaining interest as connecting variable between ‘pedo-, bio- and atmosphere’. It is primarily used to simulate hydraulics in the SPAC, and is thus essential for studying drought effects. Recent implementations of hydraulics in large-scale terrestrial biosphere models (TBMs) improved their performance under water-limited conditions, while hydraulic features of recent detailed functional–structural plant models (FSPMs) open new possibilities for dissecting complex traits for drought tolerance. These developments in models across scales deserve a critical appraisal to evaluate its potential for wider use in FSPMs, but also in crop systems models (CSMs), where hydraulics are currently still absent. After refreshing the physical basis, we first address models where water potential is primarily used for describing water transport along the transpiration pathway from the soil to the leaves, through the roots, the xylem and the leaf mesophyll. Then, we highlight models for three ecophysiological processes, which have well-recognized links to water potential: phloem transport, stomatal conductance and organ growth. We identify water potential as the bridge between soil, root and shoot models, as the physiological variable integrating below- and above-ground abiotic drivers, but also as the link between water status and growth. Models making these connections enable identifying crucial traits for ecosystem resilience to drought and for breeding towards improved drought tolerance in crops. Including hydraulics often increases model complexity, and thus requires experimental data on soil and plant hydraulics. Nevertheless, modelling hydraulics is insightful at different scales (FSPMs, CSMs and TBMs).

Assistive robots should be able to wash, fold or iron clothes. However, due to the variety, deformability and self-occlusions of clothes, creating robot systems for cloth manipulation is challenging. Synthetic data is a promising direction to improve generalization, but the sim-to-real gap limits its effectiveness. To advance the use of synthetic data for cloth manipulation tasks such as robotic folding, we present a synthetic data pipeline to train keypoint detectors for almost-flattened cloth items. To evaluate its performance, we have also collected a real-world dataset. We train detectors for both T-shirts, towels and shorts and obtain an average precision of 64% and an average keypoint distance of 18 pixels. Fine-tuning on real-world data improves performance to 74% mAP and an average distance of only 9 pixels. Furthermore, we describe failure modes of the keypoint detectors and compare different approaches to obtain cloth meshes and materials. We also quantify the remaining sim-to-real gap and argue that further improvements to the fidelity of cloth assets will be required to further reduce this gap. The code, dataset and trained models are available here.

General-purpose clothes-folding robots do not yet exist owing to the deformable nature of textiles, making it hard to engineer manipulation pipelines or learn this task. In order to accelerate research for the learning of the robotic clothes-folding task, we introduce a video dataset of human folding demonstrations. In total, we provide 8.5 hours of demonstrations from multiple perspectives leading to 1,000 folding samples of different types of textiles. The demonstrations are recorded in multiple public places, in different conditions with a diverse set of people. Our dataset consists of anonymized RGB images, depth frames, skeleton keypoint trajectories, and object labels. In this article, we describe our recording setup, the data format, and utility scripts, which can be accessed at https://adverley.github.io/folding-demonstrations .

We present a diverse dataset of industrial metal objects with unique characteristics such as symmetry, texturelessness, and high reflectiveness. These features introduce challenging conditions that are not captured in existing datasets. Our dataset comprises both real-world and synthetic multi-view RGB images with 6D object pose labels. Real-world data were obtained by recording multi-view images of scenes with varying object shapes, materials, carriers, compositions, and lighting conditions. This resulted in over 30,000 real-world images. We introduce a new public tool that enables the quick annotation of 6D object pose labels in multi-view images. This tool was used to provide 6D object pose labels for all real-world images. Synthetic data were generated by carefully simulating real-world conditions and varying them in a controlled and realistic way. This resulted in over 500,000 synthetic images. The close correspondence between synthetic and real-world data and controlled variations will facilitate sim-to-real research. Our focus on industrial conditions and objects will facilitate research on computer vision tasks, such as 6D object pose estimation, which are relevant for many industrial applications, such as machine tending. The dataset and accompanying resources are available on the project website.

Vocational Education and Training (VET) faces significant challenges in equipping individuals for modern workplaces, which increasingly require digital literacy and Computational Thinking (CT) skills. This paper addresses the imperative of integrating CT into VET programs and outlines key research questions. Our methodology primarily involves a systematic literature review, resulting in the identification of 29 relevant papers. Through qualitative content analysis, we develop a CT integration framework that connects CT practices and integration elements to the engineering design process, while highlighting the VET context. Arguably, the innovative aspect of this framework lies in its core dimensions of harnessing computational power for enhanced efficiency. Raising the question of whether computers can optimize the efficiency and effectiveness of specific tasks is paramount for addressing challenges in technology-rich environments. Therefore, this inquiry merits unwavering attention at every stage of the process. The proposed framework provides educators with a structured approach to identify integration opportunities and help prepare students for multifaceted vocational careers. Furthermore, other key findings underscore the inherently interdisciplinary nature of VET, the growing demand for STEM competencies, and the transformative potential of CT integration. Implications emphasize the need for further research, supportive policies, and practical CT integration. Despite limitations, this study strongly advocates for CT integration, empowering VET students for success in the contemporary workforce.