This paper examines the emergence of the ‘gravel bike’, a new and successful category of sports bicycles that gained prominence in the global cycling industry in the late 2010s, to advance the understanding of the role of users in the processes of sociotechnical innovation. The study traces the development of gravel cycling and the gravel bike within the framework of science and technology studies (STS), introducing the concept of ‘user betrayal’ to highlight how innovations initially driven by users can later diverge from their original values and needs. The development of the gravel bike represents a case where users’ input played a crucial role in creating an alternative cycling culture that directly supported the introduction of a new, successful bicycle model. However, the commercialization and institutionalization of gravel cycling, driven by industries, institutions and sporting bodies, has led to a significant shift away from the values that motivated early enthusiasts. This case reveals the tensions between user-driven innovation and the forces of commodification, emphasizing how marketing and institutional pressures can undermine the original needs and ideals of user collectives.
Technological understanding is not a singular concept but varies depending on the context. Building on De Jong and De Haro's (2025) notion of technological understanding as the ability to realise an aim by using a technological artefact, this paper further refines the concept as an ability that varies by context and degree. We extend its original specification for a design context by introducing two additional contexts: operation and innovation. Each context represents a distinct way of realising an aim through technology, resulting in three types (specifications) of technological understanding. To further clarify the nature of technological understanding, we propose an assessment framework based on counterfactual reasoning. Each type of understanding is associated with the ability to answer a specific set of what-if questions, addressing changes in an artefact's structure, performance, or appropriateness. Explicitly distinguishing these different types helps to focus efforts to improve technological understanding, clarifies the epistemic requirements for different forms of engagement with technology, and promotes a pluralistic perspective on expertise.
Reinforcement Learning (RL) has become a critical tool for optimization challenges within automation, leading to significant advancements in several areas. This review article examines the current landscape of RL within automation, with a particular focus on its roles in manufacturing, energy systems, and robotics. It discusses state-of-the-art methods, major challenges, and upcoming avenues of research within each sector, highlighting RL's capacity to solve intricate optimization challenges. The paper reviews the advantages and constraints of RL-driven optimization methods in automation. It points out prevalent challenges encountered in RL optimization, including issues related to sample efficiency and scalability; safety and robustness; interpretability and trustworthiness; transfer learning and meta-learning; and real-world deployment and integration. It further explores prospective strategies and future research pathways to navigate these challenges. Additionally, the survey includes a comprehensive list of relevant research papers, making it an indispensable guide for scholars and practitioners keen on exploring this domain.
Chinmay Vilas Samak, Tanmay Vilas Samak, Bing Li
et al.
Simulation-based design, optimization, and validation of autonomous vehicles have proven to be crucial for their improvement over the years. Nevertheless, the ultimate measure of effectiveness is their successful transition from simulation to reality (sim2real). However, existing sim2real transfer methods struggle to address the autonomy-oriented requirements of balancing: (i) conditioned domain adaptation, (ii) robust performance with limited examples, (iii) modularity in handling multiple domain representations, and (iv) real-time performance. To alleviate these pain points, we present a unified framework for learning cross-domain adaptive representations through conditional latent diffusion for sim2real transferable automated driving. Our framework offers options to leverage: (i) alternate foundation models, (ii) a few-shot fine-tuning pipeline, and (iii) textual as well as image prompts for mapping across given source and target domains. It is also capable of generating diverse high-quality samples when diffusing across parameter spaces such as times of day, weather conditions, seasons, and operational design domains. We systematically analyze the presented framework and report our findings in terms of performance benchmarks and ablation studies. Additionally, we demonstrate its serviceability for autonomous driving using behavioral cloning case studies. Our experiments indicate that the proposed framework is capable of bridging the perceptual sim2real gap by over 40%.
Enrico Saccon, Davide De Martini, Matteo Saveriano
et al.
We present a novel framework that integrates Large Language Models (LLMs) with automated planning and formal verification to streamline the creation and use of Markov Decision Processes (MDP). Our system leverages LLMs to extract structured knowledge in the form of a Prolog knowledge base from natural language (NL) descriptions. It then automatically constructs an MDP through reachability analysis, and synthesises optimal policies using the Storm model checker. The resulting policy is exported as a state-action table for execution. We validate the framework in three human-robot interaction scenarios, demonstrating its ability to produce executable policies with minimal manual effort. This work highlights the potential of combining language models with formal methods to enable more accessible and scalable probabilistic planning in robotics.
Caterina Conigliani, Martina Iorio, Salvatore Monni
According to the UN's Sustainable Development Agenda, to effectively achieve sustainable development, strategies for building economic growth should also address social needs, including access to essential services. Sustainable integrated management of water resources for both primary use and energy production is crucial, especially in territories such as the Amazonian State of Pará, where a primary good like fresh water is also the main source of electricity. However, the territorial transformations occurring in Pará over installing new hydroelectric plants have jeopardised local development. This was mainly caused by the top-down approach underlying national strategic projects that have paid little attention to local needs, thus paving the way for detrimental conditions for implementing the UN's 2030 Agenda. This paper aims to analyse the relationship between a municipality's level of development and quality of life and the most relevant key determinants of sustainable development in Pará. To this end, we consider a spatial regression analysis, with particular attention devoted to the role of access to both energy and water. The presence of significant spillover effects implies that providing public services on a geographically broad basis could induce self-reinforcing benefits.
Scientific scandals are catalysts for the evolution process of legal governance. The 2018 CRISPR-babies Incident has essentially triggered China's legal reforms of ethics governance in science and technology. This paper explores the institutional deficiency that led to such a scandal, analyzes its long-term implications for legal governance, and presents China's recent legal progress in response to such an issue. The rapid legislative response to the CRISPR-babies Incident is a double-edged sword, while promoting the improvement of the legal system, it can also cause issues like fragmentation of governance, contradictory rules, and conflict of interest. China should integrate departmental norms and upgrade its level of effectiveness. Strengthening legislation is the implementation path, and improving ethical review, supervision and scientific research integrity systems are the crucial means. In addition, it is necessary to bring the coordinating function of the Central Science and Technology Commission into full play and pay more attention to public engagement and international cooperation.
Public acceptance is critical to the adoption of Shared Autonomous Vehicles (SAVs) in the transport sector. Traditional acceptance models, primarily reliant on Structural Equation Modeling, may not adequately capture the complex, non-linear relationships among factors influencing technology acceptance and often have limited predictive capabilities. This paper introduces a framework that combines Machine Learning techniques with chord diagram visualizations to analyze and predict public acceptance of technologies. Using SAV acceptance as a case study, we applied a Random Forest machine learning approach to model the non-linear relationships among psychological factors influencing acceptance. Chord diagrams were then employed to provide an intuitive visualization of the relative importance and interplay of these factors at both factor and item levels in a single plot. Our findings identified Attitude as the primary predictor of SAV usage intention, followed by Perceived Risk, Perceived Usefulness, Trust, and Perceived Ease of Use. The framework also reveals divergent perceptions between SAV adopters and non-adopters, providing insights for tailored strategies to enhance SAV acceptance. This study contributes a data-driven perspective to the technology acceptance discourse, demonstrating the efficacy of integrating predictive modeling with visual analytics to understand the relative importance of factors in predicting public acceptance of emerging technologies.
In situ robotic automation in construction is challenging due to constantly changing environments, a shortage of robotic experts, and a lack of standardized frameworks bridging robotics and construction practices. This work proposes a holistic framework for construction task specification, optimization of robot morphology, and mission execution using a mobile modular reconfigurable robot. Users can specify and monitor the desired robot behavior through a graphical interface. In contrast to existing, monolithic solutions, we automatically identify a new task-tailored robot for every task by integrating \acf{bim}. Our framework leverages modular robot components that enable the fast adaption of robot hardware to the specific demands of the construction task. Other than previous works on modular robot optimization, we consider multiple competing objectives, which allow us to explicitly model the challenges of real-world transfer, such as calibration errors. We demonstrate our framework in simulation by optimizing robots for drilling and spray painting. Finally, experimental validation demonstrates that our approach robustly enables the autonomous execution of robotic drilling.
For reliable autonomous robot navigation in urban settings, the robot must have the ability to identify semantically traversable terrains in the image based on the semantic understanding of the scene. This reasoning ability is based on semantic traversability, which is frequently achieved using semantic segmentation models fine-tuned on the testing domain. This fine-tuning process often involves manual data collection with the target robot and annotation by human labelers which is prohibitively expensive and unscalable. In this work, we present an effective methodology for training a semantic traversability estimator using egocentric videos and an automated annotation process. Egocentric videos are collected from a camera mounted on a pedestrian's chest. The dataset for training the semantic traversability estimator is then automatically generated by extracting semantically traversable regions in each video frame using a recent foundation model in image segmentation and its prompting technique. Extensive experiments with videos taken across several countries and cities, covering diverse urban scenarios, demonstrate the high scalability and generalizability of the proposed annotation method. Furthermore, performance analysis and real-world deployment for autonomous robot navigation showcase that the trained semantic traversability estimator is highly accurate, able to handle diverse camera viewpoints, computationally light, and real-world applicable. The summary video is available at https://youtu.be/EUVoH-wA-lA.
Enrique González-Plaza, David García, Jesús-Ignacio Prieto
Stirling engines are currently of interest due to their adaptability to a wide range of energy sources. Since simple tools are needed to guide the sizing of prototypes in preliminary studies, this paper proposes two groups of simple models to estimate the maximum power in Stirling engines with a kinematic drive mechanism. The models are based on regression or ANN techniques, using data from 34 engines over a wide range of operating conditions. To facilitate the generalisation and interpretation of results, all models are expressed by dimensionless variables. The first group models use three input variables and 23 data points for correlation construction or training purposes, while another 66 data points are used for testing. Models in the second group use eight inputs and 18 data points for correlation construction or training, while another 36 data points are used for testing. The three-input models provide estimations of the maximum brake power with an acceptable accuracy for feasibility studies. Using eight-input models, the predictions of the maximum indicated power are very accurate, while those of the maximum brake power are less accurate, but acceptable for the preliminary design stage. In general, the best results are achieved with ANN models, although they only employ one hidden layer.
Engineering machinery, tools, and implements, Technological innovations. Automation
Albertina Paula Monteiro, Francisco Barbosa, Amélia Silva
et al.
Based on the legitimacy and stakeholders’ theories, this research aims to analyze the environmental information disclosure of Portuguese companies. Specifically, this study aims to explore the environmental information disclosure level, whether the industry (environmentally sensitive) influences the level of ecological matters disclosure, and whether this impacts the companies' performance/profitability. Using the content analysis technique, we developed two indices to assess the level of environmental disclosures in companies' mandatory and voluntary reporting. In addition, for the relationship between variables analysis, we applied the Process Macro of SPSS software. Study results show that (1) there is a positive evolution in the level of environmental disclosure by Lisbon stock exchange listed companies between the years 2015 and 2017, (2) the industry has no significant relationship with profitability; (3) the environmental disclosure acts as a mediator variable in the relationship between industry and profitability. This research presents differences in the tendency of environmental matters disclosure when prepared under an accounting framework or voluntarily and assesses the mediating role of the environmental disclosure index in the relationship between industry and performance.
In this paper, the growing significance of data analysis in manufacturing environments is exemplified through a review of relevant literature and a generic framework to aid the ease of adoption of regression-based supervised learning in manufacturing environments. To validate the practicality of the framework, several regression learning techniques are applied to an open-source multi-stage continuous-flow manufacturing process data set to typify inference-driven decision-making that informs the selection of regression learning methods for adoption in real-world manufacturing environments. The investigated regression learning techniques are evaluated in terms of their training time, prediction speed, predictive accuracy (R-squared value), and mean squared error. In terms of training time (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>T</mi><mi>T</mi></mrow></semantics></math></inline-formula>), <i>k</i>-NN20 (<i>k</i>-Nearest Neighbour with 20 neighbors) ranks first with average and median values of 4.8 ms and 4.9 ms, and 4.2 ms and 4.3 ms, respectively, for the first stage and second stage of the predictive modeling of the multi-stage continuous-flow manufacturing process, respectively, over 50 independent runs. In terms of prediction speed (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>P</mi><mi>S</mi></mrow></semantics></math></inline-formula>), DTR (decision tree regressor) ranks first with average and median values of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>5.6784</mn><mo>×</mo><msup><mn>10</mn><mn>6</mn></msup></mrow></semantics></math></inline-formula> observations per second (ob/s) and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>4.8691</mn><mo>×</mo><msup><mn>10</mn><mn>6</mn></msup></mrow></semantics></math></inline-formula> observations per second (ob/s), and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>4.9929</mn><mo>×</mo><msup><mn>10</mn><mn>6</mn></msup></mrow></semantics></math></inline-formula> observations per second (ob/s) and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>5.8806</mn><mo>×</mo><msup><mn>10</mn><mn>6</mn></msup></mrow></semantics></math></inline-formula> observations per second (ob/s), respectively, for the first stage and second stage of the predictive modeling of the multi-stage continuous-flow manufacturing process, respectively, over 50 independent runs. In terms of R-squared value (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>R</mi><mn>2</mn></msup></semantics></math></inline-formula>), BR (bagging regressor) ranks first with average and median values of 0.728 and 0.728, respectively, over 50 independent runs, for the first stage of the predictive modeling of the multi-stage continuous-flow manufacturing process, and RFR (random forest regressor) ranks first with average and median values of 0.746 and 0.746, respectively, over 50 independent runs, for the second stage of the predictive modeling of the multi-stage continuous-flow manufacturing process. In terms of mean squared error (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>M</mi><mi>S</mi><mi>E</mi></mrow></semantics></math></inline-formula>), BR (bagging regressor) ranks first with average and median values of 2.7 and 2.7, respectively, over 50 independent runs, for the first stage of the predictive modeling of the multi-stage continuous-flow manufacturing process, and RFR (random forest regressor) ranks first with average and median values of 3.5 and 3.5, respectively, over 50 independent runs, for the second stage of the predictive modeling of the multi-stage continuous-flow manufacturing process. All methods are further ranked inferentially using the statistics of their performance metrics to identify the best method(s) for the first and second stages of the predictive modeling of the multi-stage continuous-flow manufacturing process. A Wilcoxon rank sum test is then used to statistically verify the inference-based rankings. <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>D</mi><mi>T</mi><mi>R</mi></mrow></semantics></math></inline-formula> and <i>k</i>-NN20 have been identified as the most suitable regression learning techniques given the multi-stage continuous-flow manufacturing process data used for experimentation.
Engineering machinery, tools, and implements, Technological innovations. Automation
This survey article provides a synopsis on some of the engineering problems, technological innovations, robotic development and automation efforts encountered in the mining industry -- particularly in the Pilbara iron-ore region of Western Australia. The goal is to paint the technology landscape and highlight issues relevant to an engineering audience to raise awareness of AI and automation trends in mining. It assumes the reader has no prior knowledge of mining and builds context gradually through focused discussion and short summaries of common open-pit mining operations. The principal activities that take place may be categorized in terms of resource development, mine-, rail- and port operations. From mineral exploration to ore shipment, there are roughly nine steps in between. These include: geological assessment, mine planning and development, production drilling and assaying, blasting and excavation, transportation of ore and waste, crush and screen, stockpile and load-out, rail network distribution, and ore-car dumping. The objective is to describe these processes and provide insights on some of the challenges/opportunities from the perspective of a decade-long industry-university R&D partnership.
Elias Greve, Martin Büchner, Niclas Vödisch
et al.
Maps have played an indispensable role in enabling safe and automated driving. Although there have been many advances on different fronts ranging from SLAM to semantics, building an actionable hierarchical semantic representation of urban dynamic scenes and processing information from multiple agents are still challenging problems. In this work, we present Collaborative URBan Scene Graphs (CURB-SG) that enable higher-order reasoning and efficient querying for many functions of automated driving. CURB-SG leverages panoptic LiDAR data from multiple agents to build large-scale maps using an effective graph-based collaborative SLAM approach that detects inter-agent loop closures. To semantically decompose the obtained 3D map, we build a lane graph from the paths of ego agents and their panoptic observations of other vehicles. Based on the connectivity of the lane graph, we segregate the environment into intersecting and non-intersecting road areas. Subsequently, we construct a multi-layered scene graph that includes lane information, the position of static landmarks and their assignment to certain map sections, other vehicles observed by the ego agents, and the pose graph from SLAM including 3D panoptic point clouds. We extensively evaluate CURB-SG in urban scenarios using a photorealistic simulator. We release our code at http://curb.cs.uni-freiburg.de.
In an increasingly automated world -- from warehouse robots to self-driving cars -- streamlining the development and deployment process and operations of robotic applications becomes ever more important. Automated DevOps processes and microservice architectures have already proven successful in other domains such as large-scale customer-oriented web services (e.g., Netflix). We recommend to employ similar microservice architectures for the deployment of small- to large-scale robotic applications in order to accelerate development cycles, loosen functional dependence, and improve resiliency and elasticity. In order to facilitate involved DevOps processes, we present and release a tooling suite for automating the development of microservices for robotic applications based on the Robot Operating System (ROS). Our tooling suite covers the automated minimal containerization of ROS applications, a collection of useful machine learning-enabled base container images, as well as a CLI tool for simplified interaction with container images during the development phase. Within the scope of this paper, we embed our tooling suite into the overall context of streamlined robotics deployment and compare it to alternative solutions. We release our tools as open-source software at https://github.com/ika-rwth-aachen/dorotos.
Vitalii Velychko, Svitlana Voinova, Valery Granyak
et al.
The monograph summarizes and analyzes the current state of development of computer and mathematical simulation and modeling, the automation of management processes, the use of information technologies in education, the design of information systems and software complexes, the development of computer telecommunication networks and technologies most areas that are united by the term Industry 4.0
Caroline Quinn, Ali Zargar Shabestari, Marin Litoiu
et al.
Buildings Automation Systems (BAS) are ubiquitous in contemporary buildings, both monitoring building conditions and managing the building system control points. At present, these controls are prescriptive and pre-determined by the design team, rather than responsive to actual building performance. These are further limited by prescribed logic, possess only rudimentary visualizations, and lack broader system integration capabilities. Advances in machine learning, edge analytics, data management systems, and Facility Management-enabled Building Information Models (FM-BIMs) permit a novel approach: cloud-hosted building management. This paper presents an integration technique for mapping the data from a building Internet of Things (IoT) sensor network to an FM-BIM. The sensor data naming convention and timeseries analysis strategies integrated into the data structure are discussed and presented, including the use of a 3D nested list to permit timeseries data to be mapped to the FM-BIM and readily visualized. The developed approach is presented through a case study of an office living lab consisting of a local sensor network mimicking a BAS, which streams to a cloud server via a virtual private network connection. The resultant data structure and key visualizations are presented to demonstrate the value of this approach, which permits the end-user to select the desired timeframe for visualization and readily step through the spatio-temporal building performance data.