Daniel Ruan, Salma Mozaffari, Sigrid Adriaenssens
et al.
Industrial robots are increasingly deployed in contact-rich construction and manufacturing tasks that involve uncertainty and long-horizon execution. While learning-based visuomotor policies offer a promising alternative to open-loop control, their deployment on industrial platforms is challenged by a large observation-execution gap caused by sensing, inference, and control latency. This gap is significantly greater than on low-latency research robots due to high-level interfaces and slower closed-loop dynamics, making execution timing a critical system-level issue. This paper presents a latency-aware framework for deploying and evaluating visuomotor policies on industrial robotic arms under realistic timing constraints. The framework integrates calibrated multimodal sensing, temporally consistent synchronization, a unified communication pipeline, and a teleoperation interface for demonstration collection. Within this framework, we introduce a latency-aware execution strategy that schedules finite-horizon, policy-predicted action sequences based on temporal feasibility, enabling asynchronous inference and execution without modifying policy architectures or training. We evaluate the framework on a contact-rich industrial assembly task while systematically varying inference latency. Using identical policies and sensing pipelines, we compare latency-aware execution with blocking and naive asynchronous baselines. Results show that latency-aware execution maintains smooth motion, compliant contact behavior, and consistent task progression across a wide range of latencies while reducing idle time and avoiding instability observed in baseline methods. These findings highlight the importance of explicitly handling latency for reliable closed-loop deployment of visuomotor policies on industrial robots.
This report describes a dataset for industrial smoke segmentation, published on a figshare repository (https://doi.org/10.21942/uva.31847188). The dataset is licensed under CC BY 4.0.
Rachel Poonsiriwong, Chayapatr Archiwaranguprok, Pat Pataranutaporn
Millions of users form emotional attachments to AI companions like Character AI, Replika, and ChatGPT. When these relationships end through model updates, safety interventions, or platform shutdowns, users receive no closure, reporting grief comparable to human loss. As regulations mandate protections for vulnerable users, discontinuation events will accelerate, yet no platform has implemented deliberate end-of-"life" design. Through grounded theory analysis of AI companion communities, we find that discontinuation is a sense-making process shaped by how users attribute agency, perceive finality, and anthropomorphize their companions. Strong anthropomorphization co-occurs with intense grief; users who perceive change as reversible become trapped in fixing cycles; while user-initiated endings demonstrate greater closure. Synthesizing grief psychology with Self-Determination Theory, we develop four design principles and artifacts demonstrating how platforms might provide closure and orient users toward human connection. We contribute the first framework for designing psychologically safe AI companion discontinuation.
With the rapid development of industrial intelligence and unmanned inspection, reliable perception and safety assessment for AI systems in complex and dynamic industrial sites has become a key bottleneck for deploying predictive maintenance and autonomous inspection. Most public datasets remain limited by simulated data sources, single-modality sensing, or the absence of fine-grained object-level annotations, which prevents robust scene understanding and multimodal safety reasoning for industrial foundation models. To address these limitations, InspecSafe-V1 is released as the first multimodal benchmark dataset for industrial inspection safety assessment that is collected from routine operations of real inspection robots in real-world environments. InspecSafe-V1 covers five representative industrial scenarios, including tunnels, power facilities, sintering equipment, oil and gas petrochemical plants, and coal conveyor trestles. The dataset is constructed from 41 wheeled and rail-mounted inspection robots operating at 2,239 valid inspection sites, yielding 5,013 inspection instances. For each instance, pixel-level segmentation annotations are provided for key objects in visible-spectrum images. In addition, a semantic scene description and a corresponding safety level label are provided according to practical inspection tasks. Seven synchronized sensing modalities are further included, including infrared video, audio, depth point clouds, radar point clouds, gas measurements, temperature, and humidity, to support multimodal anomaly recognition, cross-modal fusion, and comprehensive safety assessment in industrial environments.
Background: Young people are adversely affected by the lack of access to mental health support. Digital interventions have demonstrated efficacy in randomised trials and pose one possible solution to increase access to mental health care. Yet, purposeful implementation efforts seldom occur, and effective interventions are rarely evaluated in real-world contexts. Methods: The Preferred Reporting Items for Systematic Reviews and Meta-Analyses guided this systematic literature review. Relevant databases were systematically searched for studies reporting on disseminated digital mental health interventions for child and adolescent anxiety and/or depression that reported implementation outcome data. Reference lists of included studies and articles citing the included studies were also searched. Intervention and implementation outcome data was extracted. Results: Nine peer-reviewed articles, pertaining to seven different digital interventions, were identified as meeting inclusion criteria. There was significant heterogeneity in methods and dissemination efforts. The use of implementation science was inconsistent across studies, and the use of implementation frameworks and models was minimal. Digital interventions were effective for and adopted by community users, though dropout rates were high. Results indicate that young people and parents find digital mental health interventions to be acceptable and beneficial. Conclusions: This review highlights the inconsistencies in the application of implementation science for scaling up digital interventions for children and adolescents. However, results of included scale-up efforts support the notion of community dissemination of child and adolescent digital interventions. Future research should focus on planning and testing implementation strategies for the dissemination of such interventions.The review protocol was registered with PROSPERO (Registration ID CRD42024552703).
We propose an approach to generate synthetic data to train computer vision (CV) models for industrial wear and tear detection. Wear and tear detection is an important CV problem for predictive maintenance tasks in any industry. However, data curation for training such models is expensive and time-consuming due to the unavailability of datasets for different wear and tear scenarios. Our approach employs a vision language model along with a 3D simulation and rendering engine to generate synthetic data for varying rust conditions. We evaluate our approach by training a CV model for rust detection using the generated dataset and tested the trained model on real images of rusted industrial objects. The model trained with the synthetic data generated by our approach, outperforms the other approaches with a mAP50 score of 0.87. The approach is customizable and can be easily extended to other industrial wear and tear detection scenarios
This research explores consumer attitudes and behavior in a metaverse retailing environment, mainly focusing on how perceptions of scarcity and rarity influence consumers’ views of purchasing virtual wearables. Our findings diverge from preconceived notions about scarcity in physical/online retail, opening the door to a new understanding of how metaverse citizens may perceive scarcity of products. While it may appear simple to assume that physical-world strategies can seemingly be exported to virtual worlds, we uncovered a more complex story. The influence of the supply (availability) information on consumer attitudes in the metaverse is mediated by consumers’ need for uniqueness. Specifically, seeing the virtual offerings as relatively abundant increased consumers’ need for uniqueness, which improved the likelihood of purchase, a puzzling result. The mystery is better understood when considering how all items in exclusive collections in the metaverse can preserve their rare status, thereby fully separating scarcity and rarity. Unlike in physical retail environments, our findings indicate an interaction: high product availability (low scarcity) increases the likelihood of purchasing only when product rarity is high. These surprising results provide novel insights for academics and practitioners to consider the combinatorial effects of availability information and product rarity, as well as the virtual customers’ characteristics, particularly their need for uniqueness as a mediator to their attitudes toward virtual products.
Condition monitoring is essential for ensuring the safety, reliability, and efficiency of modern industrial systems. With the increasing complexity of industrial processes, artificial intelligence (AI) has emerged as a powerful tool for fault detection and diagnosis, attracting growing interest from both academia and industry. This paper provides a comprehensive overview of intelligent condition monitoring methods, with a particular emphasis on chemical plants and the widely used Tennessee Eastman Process (TEP) benchmark. State-of-the-art machine learning (ML) and deep learning (DL) algorithms are reviewed, highlighting their strengths, limitations, and applicability to industrial fault detection and diagnosis. Special attention is given to key challenges, including imbalanced and unlabeled data, and to strategies by which models can address these issues. Furthermore, comparative analyses of algorithm performance are presented to guide method selection in practical scenarios. This survey is intended to benefit both newcomers and experienced researchers by consolidating fundamental concepts, summarizing recent advances, and outlining open challenges and promising directions for intelligent condition monitoring in industrial plants.
This paper is about effectively utilizing synthetic data for training deep neural networks for industrial parts classification, in particular, by taking into account the domain gap against real-world images. To this end, we introduce a synthetic dataset that may serve as a preliminary testbed for the Sim-to-Real challenge; it contains 17 objects of six industrial use cases, including isolated and assembled parts. A few subsets of objects exhibit large similarities in shape and albedo for reflecting challenging cases of industrial parts. All the sample images come with and without random backgrounds and post-processing for evaluating the importance of domain randomization. We call it Synthetic Industrial Parts dataset (SIP-17). We study the usefulness of SIP-17 through benchmarking the performance of five state-of-the-art deep network models, supervised and self-supervised, trained only on the synthetic data while testing them on real data. By analyzing the results, we deduce some insights on the feasibility and challenges of using synthetic data for industrial parts classification and for further developing larger-scale synthetic datasets. Our dataset and code are publicly available.
Federated Learning (FL) is the most widely adopted collaborative learning approach for training decentralized Machine Learning (ML) models by exchanging learning between clients without sharing the data and compromising privacy. However, since great data similarity or homogeneity is taken for granted in all FL tasks, FL is still not specifically designed for the industrial setting. Rarely this is the case in industrial data because there are differences in machine type, firmware version, operational conditions, environmental factors, and hence, data distribution. Albeit its popularity, it has been observed that FL performance degrades if the clients have heterogeneous data distributions. Therefore, we propose a Lightweight Industrial Cohorted FL (LICFL) algorithm that uses model parameters for cohorting without any additional on-edge (clientlevel) computations and communications than standard FL and mitigates the shortcomings from data heterogeneity in industrial applications. Our approach enhances client-level model performance by allowing them to collaborate with similar clients and train more specialized or personalized models. Also, we propose an adaptive aggregation algorithm that extends the LICFL to Adaptive LICFL (ALICFL) for further improving the global model performance and speeding up the convergence. Through numerical experiments on real-time data, we demonstrate the efficacy of the proposed algorithms and compare the performance with existing approaches.
Aimira Baitieva, David Hurych, Victor Besnier
et al.
Automating visual inspection in industrial production lines is essential for increasing product quality across various industries. Anomaly detection (AD) methods serve as robust tools for this purpose. However, existing public datasets primarily consist of images without anomalies, limiting the practical application of AD methods in production settings. To address this challenge, we present (1) the Valeo Anomaly Dataset (VAD), a novel real-world industrial dataset comprising 5000 images, including 2000 instances of challenging real defects across more than 20 subclasses. Acknowledging that traditional AD methods struggle with this dataset, we introduce (2) Segmentation-based Anomaly Detector (SegAD). First, SegAD leverages anomaly maps as well as segmentation maps to compute local statistics. Next, SegAD uses these statistics and an optional supervised classifier score as input features for a Boosted Random Forest (BRF) classifier, yielding the final anomaly score. Our SegAD achieves state-of-the-art performance on both VAD (+2.1% AUROC) and the VisA dataset (+0.4% AUROC). The code and the models are publicly available.
Rohit Konda, Jordan Prescott, Vikas Chandan
et al.
The widespread use of industrial refrigeration systems across various sectors contribute significantly to global energy consumption, highlighting substantial opportunities for energy conservation through intelligent control design. As such, this work focuses on control algorithm design in industrial refrigeration that minimize operational costs and provide efficient heat extraction. By adopting tools from inventory control, we characterize the structure of these optimal control policies, exploring the impact of different energy cost-rate structures such as time-of-use (TOU) pricing and peak pricing. While classical threshold policies are optimal under TOU costs, introducing peak pricing challenges their optimality, emphasizing the need for carefully designed control strategies in the presence of significant peak costs. We provide theoretical findings and simulation studies on this phenomenon, offering insights for more efficient industrial refrigeration management.
Zhaoxian Li,1 Wei Bao,1 Yao Wang,1 Shangsong Yan,1 Hong Zheng,2 Junlong Luo1,3 1Psychology College, Shanghai Normal University, Shanghai, People’s Republic of China; 2Changning Mental Health Center Affiliated with East China Normal University, Shanghai, People’s Republic of China; 3Lab for Educational Big Data and Policymaking, Ministry of Education, Shanghai Normal University, Shanghai, People’s Republic of ChinaCorrespondence: Hong Zheng; Junlong Luo, Email zhhmm2@163.com; luo831023@vip.163.comIntroduction: The impact of emotions on intuitive and analytical thinking has been widely studied. Most research suggests that negative emotions enhance analytical processing. However, there are studies indicating that the sense of certainty associated with disgust can stimulate intuitive processing. Despite these findings, the neuroelectrophysiological evidence supporting the role of disgust in promoting intuitive processing remains unexplored.Methods: This study aimed to investigate the neuroelectrophysiological mechanisms by which disgust promotes intuitive processing. A total of 54 participants were recruited and randomly assigned to specific emotion groups. Emotional states were induced by exposing participants to disgust and fear videos designed to evoke specific dimensions of certainty and uncertainty. Event-related potentials (ERP) and the Cognitive Reflection Test (CRT) were utilized as experimental materials to measure participants’ responses.Results: The results demonstrated that disgust facilitated intuitive thinking, as evidenced by the lowest accuracy in behavioral outcomes. ERP findings showed that disgust led to smaller N2 and larger P3b amplitudes under conditions of conflict. These results suggest that disgust reduces individuals’ conflict-detection ability, resulting in a stronger sense of certainty in intuitive but incorrect answers.Conclusion: This study provides neuroelectrophysiological evidence that disgust enhances intuitive thinking. The findings offer a new perspective on the influence of emotions on dual-process thinking, highlighting the role of disgust in shaping intuitive and analytical thought processes.Keywords: certain, uncertain, disgust, fear, dual-process theory, N2, P3b
Giovanni Piccininno, Nicola Laurieri, Alessandro Anselmo
et al.
We describe an innovative case study focusing on a social robot able to help healthcare professionals compute criticality scores for patients hosted in a Geriatric Sub-Intensive Care Unit. The aim is to establish the feasibility of a scenario in which the robot modulates the frequency of its visits to the room of bedridden patients, based on the criticality scores it has computed.
The process of industrial box-packing, which involves the accurate placement of multiple objects, requires high-accuracy positioning and sequential actions. When a robot is tasked with placing an object at a specific location with high accuracy, it is important not only to have information about the location of the object to be placed, but also the posture of the object grasped by the robotic hand. Often, industrial box-packing requires the sequential placement of identically shaped objects into a single box. The robot's action should be determined by the same learned model. In factories, new kinds of products often appear and there is a need for a model that can easily adapt to them. Therefore, it should be easy to collect data to train the model. In this study, we designed a robotic system to automate real-world industrial tasks, employing a vision-based learning control model. We propose in-hand-view-sensitive Newtonian variational autoencoder (ihVS-NVAE), which employs an RGB camera to obtain in-hand postures of objects. We demonstrate that our model, trained for a single object-placement task, can handle sequential tasks without additional training. To evaluate efficacy of the proposed model, we employed a real robot to perform sequential industrial box-packing of multiple objects. Results showed that the proposed model achieved a 100% success rate in industrial box-packing tasks, thereby outperforming the state-of-the-art and conventional approaches, underscoring its superior effectiveness and potential in industrial tasks.
In line with the development of Industry 4.0, surface defect detection/anomaly detection becomes a topical subject in the industry field. Improving efficiency as well as saving labor costs has steadily become a matter of great concern in practice, where deep learning-based algorithms perform better than traditional vision inspection methods in recent years. While existing deep learning-based algorithms are biased towards supervised learning, which not only necessitates a huge amount of labeled data and human labor, but also brings about inefficiency and limitations. In contrast, recent research shows that unsupervised learning has great potential in tackling the above disadvantages for visual industrial anomaly detection. In this survey, we summarize current challenges and provide a thorough overview of recently proposed unsupervised algorithms for visual industrial anomaly detection covering five categories, whose innovation points and frameworks are described in detail. Meanwhile, publicly available datasets for industrial anomaly detection are introduced. By comparing different classes of methods, the advantages and disadvantages of anomaly detection algorithms are summarized. Based on the current research framework, we point out the core issue that remains to be resolved and provide further improvement directions. Meanwhile, based on the latest technological trends, we offer insights into future research directions. It is expected to assist both the research community and industry in developing a broader and cross-domain perspective.
Jian ming Wang,1 Yong qiang Li2 1School of Business Administration, Zhejiang University of Finance & Economics, Hangzhou, Zhejiang Province, 310018, People’s Republic of China; 2China Institute of Regulation Research, Zhejiang University of Finance & Economics, Hangzhou, Zhejiang Province, 310018, People’s Republic of ChinaCorrespondence: Yong qiang Li, Email 1836514269@qq.comPurpose: To explore the effects of “soft” behavioral intervention policies (eg, green emotions, social norms) and “soft” economic incentive policies (eg, high-intensity subsidies, low-intensity subsidies) and their combinations on the public’s green product purchasing behavior.Participants and Methods: An online questionnaire experiment was conducted on Chinese users using Credamo online questionnaire platform to explore the effects of different “soft” intervention policies on consumers’ green purchasing behavior, and the sample data were examined using multiple regression. In Study 1, a total of 460 valid samples were collected to explore the differences in the effects of single intervention policies; in Study 2, a total of 556 valid samples were collected to explore the effects of a combination of soft policies.Results: In the area of green product purchasing, both behavioral interventions and economic incentives alone can promote green consumption behavior; economic incentives have a more positive impact on guiding consumers to green consumption; the combination of “soft” behavioral interventions and “soft” economic incentives has a positive impact on green consumption. The combination of “soft” behavioral intervention policies and “soft” economic incentive policies is more effective than the individual policies.Conclusion: The experimental results of Study 1 show that the policy effects of both behavioral intervention policies and economic incentive intervention policies are evident for goods with different value attributes. Meanwhile, comparing the two types of soft intervention policies, we find that the effect of economic incentive intervention policies is stronger than that of soft behavioral intervention policies. In Study 2, the empirical analysis of the policy mix shows that the policy mix is more effective. The combination of “soft” economic incentive policies and “soft” behavioral intervention policies can effectively increase the salience of policy instruments, and the effect of policy combinations is greater than that of single policies.Keywords: green consumption, intervention policy, policy mix
Victor Aguirregabiria, Allan Collard-Wexler, Stephen P. Ryan
This survey is organized around three main topics: models, econometrics, and empirical applications. Section 2 presents the theoretical framework, introduces the concept of Markov Perfect Nash Equilibrium, discusses existence and multiplicity, and describes the representation of this equilibrium in terms of conditional choice probabilities. We also discuss extensions of the basic framework, including models in continuous time, the concepts of oblivious equilibrium and experience-based equilibrium, and dynamic games where firms have non-equilibrium beliefs. In section 3, we first provide an overview of the types of data used in this literature, before turning to a discussion of identification issues and results, and estimation methods. We review different methods to deal with multiple equilibria and large state spaces. We also describe recent developments for estimating games in continuous time and incorporating serially correlated unobservables, and discuss the use of machine learning methods to solving and estimating dynamic games. Section 4 discusses empirical applications of dynamic games in IO. We start describing the first empirical applications in this literature during the early 2000s. Then, we review recent applications dealing with innovation, antitrust and mergers, dynamic pricing, regulation, product repositioning, advertising, uncertainty and investment, airline network competition, dynamic matching, and natural resources. We conclude with our view of the progress made in this literature and the remaining challenges.
The latest Industrial revolution has helped industries in achieving very high rates of productivity and efficiency. It has introduced data aggregation and cyber-physical systems to optimize planning and scheduling. Although, uncertainty in the environment and the imprecise nature of human operators are not accurately considered for into the decision making process. This leads to delays in consignments and imprecise budget estimations. This widespread practice in the industrial models is flawed and requires rectification. Various other articles have approached to solve this problem through stochastic or fuzzy set model methods. This paper presents a comprehensive method to logically and realistically quantify the non-deterministic uncertainty through probabilistic uncertainty modelling. This method is applicable on virtually all Industrial data sets, as the model is self adjusting and uses epsilon-contamination to cater to limited or incomplete data sets. The results are numerically validated through an Industrial data set in Flanders, Belgium. The data driven results achieved through this robust scheduling method illustrate the improvement in performance.