Neural Dynamic Movement Primitives -- a survey

One of the most important challenges in robotics is producing accurate trajectories and controlling their dynamic parameters so that the robots can perform different tasks. The ability to provide such motion control is closely related to how such movements are encoded. Advances on deep learning have had a strong repercussion in the development of novel approaches for Dynamic Movement Primitives. In this work, we survey scientific literature related to Neural Dynamic Movement Primitives, to complement existing surveys on Dynamic Movement Primitives

Predicting Operators Fatigue in a Human in the AI Loop for Defect Detection in Manufacturing

Quality inspection, typically performed manually by workers in the past, is now rapidly switching to automated solutions, using artificial intelligence (AI)-driven methods. This elevates the job function of the quality inspection team from the physical inspection tasks to tasks related to managing workflows in synergy with AI agents, for example, interpreting inspection outcomes or labeling inspection image data for the AI models. In this context, we have studied how defect inspection can be enhanced, providing defect hints to the operator to ease defect identification. Furthermore, we developed machine learning models to recognize and predict operators’ fatigue. By doing so, we can proactively take mitigation actions to enhance the workers’ well-being and ensure the highest defect inspection quality standards. We consider such processes to empower human and non-human actors in manufacturing and the sociotechnical production system. The paper first outlines the conceptual approach for integrating the operator in the AI-driven quality inspection process while implementing a fatigue monitoring system to enhance work conditions. Furthermore, it describes how this was implemented by leveraging data and experiments performed for a real-world manufacturing use case

Human in the AI loop via xAI and Active Learning for Visual Inspection

Industrial revolutions have historically disrupted manufacturing by introducing automation into production. Increasing automation reshapes the role of the human worker. Advances in robotics and artificial intelligence open new frontiers of human-machine collaboration. Such collaboration can be realized considering two sub-fields of artificial intelligence: active learning and explainable artificial intelligence. Active learning aims to devise strategies that help obtain data that allows machine learning algorithms to learn better. On the other hand, explainable artificial intelligence aims to make the machine learning models intelligible to the human person. The present work first describes Industry 5.0, human-machine collaboration, and state-of-the-art regarding quality inspection, emphasizing visual inspection. Then it outlines how human-machine collaboration could be realized and enhanced in visual inspection. Finally, some of the results obtained in the EU H2020 STAR project regarding visual inspection are shared, considering artificial intelligence, human digital twins, and cybersecurity

Human in the AI loop via xAI and Active Learning for Visual Inspection

Industrial revolutions have historically disrupted manufacturing by introducing automation into production. Increasing automation reshapes the role of the human worker. Advances in robotics and artificial intelligence open new frontiers of human-machine collaboration. Such collaboration can be realized considering two sub-fields of artificial intelligence: active learning and explainable artificial intelligence. Active learning aims to devise strategies that help obtain data that allows machine learning algorithms to learn better. On the other hand, explainable artificial intelligence aims to make the machine learning models intelligible to the human person. The present work first describes Industry 5.0, human-machine collaboration, and state-of-the-art regarding quality inspection, emphasizing visual inspection. Then it outlines how human-machine collaboration could be realized and enhanced in visual inspection. Finally, some of the results obtained in the EU H2020 STAR project regarding visual inspection are shared, considering artificial intelligence, human digital twins, and cybersecurity

Reframing Demand Forecasting: A Two-Fold Approach for Lumpy and Intermittent Demand

Demand forecasting is a crucial component of demand management. While shortening the forecasting horizon allows for more recent data and less uncertainty, this frequently means lower data aggregation levels and a more significant data sparsity. Furthermore, sparse demand data usually result in lumpy or intermittent demand patterns with irregular demand intervals. The usual statistical and machine learning models fail to provide good forecasts in such scenarios. Our research confirms that competitive demand forecasts can be obtained through two models: predicting the demand occurrence and estimating the demand size. We analyze the usage of local and global machine learning models for both cases and compare the results against baseline methods. Finally, we propose a novel evaluation criterion for the performance of lumpy and intermittent demand forecasting models. Our research shows that global classification models are the best choice when predicting demand event occurrence. We achieved the best results using the simple exponential smoothing forecast to predict demand sizes. We tested our approach on real-world data made up of 516 time series corresponding to the daily demand, over three years, of a European original automotive equipment manufacturer

Reframing Demand Forecasting: A Two-Fold Approach for Lumpy and Intermittent Demand

Demand forecasting is a crucial component of demand management. While shortening the forecasting horizon allows for more recent data and less uncertainty, this frequently means lower data aggregation levels and a more significant data sparsity. Furthermore, sparse demand data usually result in lumpy or intermittent demand patterns with irregular demand intervals. The usual statistical and machine learning models fail to provide good forecasts in such scenarios. Our research confirms that competitive demand forecasts can be obtained through two models: predicting the demand occurrence and estimating the demand size. We analyze the usage of local and global machine learning models for both cases and compare the results against baseline methods. Finally, we propose a novel evaluation criterion for the performance of lumpy and intermittent demand forecasting models. Our research shows that global classification models are the best choice when predicting demand event occurrence. We achieved the best results using the simple exponential smoothing forecast to predict demand sizes. We tested our approach on real-world data made up of 516 time series corresponding to the daily demand, over three years, of a European original automotive equipment manufacturer