Claire chatted to Agata Suwala from the Manufacturing Technology Centre about leveraging robotics to make manufacturing systems more sustainable. Agata Suwala is a Technology Manager at the Manufacturing Technology Centre, where she leads cutting-edge work in automation and robotics. With over a decade of experience in R&D, Agata specialises in developing and implementing advanced manufacturing systems--particularly for the aerospace sector--transforming complex, skill-intensive processes through automation. Her recent focus is on enabling the transition to a circular economy by leveraging automation and robotics to create sustainable, scalable technologies. Robot Talk is a weekly podcast that explores the exciting world of robotics, artificial intelligence and autonomous machines.

ICINCO 2025 (22nd International Conference on Informatics in Control, Automation and Robotics) received 158 paper submissions from 42 countries. To evaluate each submission, a double-blind paper review was performed by the Program Committee. After a stringent selection process, 43 papers were published and presented as full papers, i.e. completed work (12 pages/25' oral presentation), 86 papers were accepted as short papers (51 as oral presentation). The organizing committee included the ICINCO Conference Chair: Dimitar Filev, Ford Research, United States, and the ICINCO 2025 Program Chairs: Giuseppina Carla Gini, Politecnico di Milano, Italy, and Radu-Emil Precup, Politehnica University of Timisoara, Romania. At the closing session, the conference acknowledged a few papers that were considered excellent in their class, presenting a "Best Paper Award", "Best Student Paper Award", "Best Poster Award", and "Best Industrial Paper Award" for the conference.

Human-robot collaboration (HRC) in the construction industry depends on precise and prompt recognition of human motion intentions and actions by robots to maximize safety and workflow efficiency. There is a research gap in comparing data modalities, specifically signals and videos, for motion intention recognition. To address this, the study leverages deep learning to assess two different modalities in recognizing workers' motion intention at the early stage of movement in drywall installation tasks. The Convolutional Neural Network - Long Short-Term Memory (CNN-LSTM) model utilizing surface electromyography (sEMG) data achieved an accuracy of around 87% with an average time of 0.04 seconds to perform prediction on a sample input. Meanwhile, the pre-trained Video Swin Transformer combined with transfer learning harnessed video sequences as input to recognize motion intention and attained an accuracy of 94% but with a longer average time of 0.15 seconds for a similar prediction. This study emphasizes the unique strengths and trade-offs of both data formats, directing their systematic deployments to enhance HRC in real-world construction projects.

The general public often encounters complex texts but does not have the time or expertise to fully understand them, leading to the spread of misinformation. Automatic Text Simplification (ATS) helps make information more accessible, but its evaluation methods have not kept up with advances in text generation, especially with Large Language Models (LLMs). In particular, recent studies have shown that current ATS metrics do not correlate with the presence of errors. Manual inspections have further revealed a variety of errors, underscoring the need for a more nuanced evaluation framework, which is currently lacking. This resource paper addresses this gap by introducing a test collection for detecting and classifying errors in simplified texts. First, we propose a taxonomy of errors, with a formal focus on information distortion. Next, we introduce a parallel dataset of automatically simplified scientific texts. This dataset has been human-annotated with labels based on our proposed taxonomy. Finally, we analyze the quality of the dataset, and we study the performance of existing models to detect and classify errors from that taxonomy. These contributions give researchers the tools to better evaluate errors in ATS, develop more reliable models, and ultimately improve the quality of automatically simplified texts.

While 3D LiDAR sensor technology is becoming more advanced and cheaper every day, the growth of digitalization in the AEC industry contributes to the fact that 3D building information models (BIM models) are now available for a large part of the built environment. These two facts open the question of how 3D models can support 3D LiDAR long-term SLAM in indoor, GPS-denied environments. This paper proposes a methodology that leverages BIM models to create an updated map of indoor environments with sequential LiDAR measurements. Session data (pose graph-based map and descriptors) are initially generated from BIM models. Then, real-world data is aligned with the session data from the model using multi-session anchoring while minimizing the drift on the real-world data. Finally, the new elements not present in the BIM model are identified, grouped, and reconstructed in a surface representation, allowing a better visualization next to the BIM model. The framework enables the creation of a coherent map aligned with the BIM model that does not require prior knowledge of the initial pose of the robot, and it does not need to be inside the map.

Harnessing human movements to command an Unmanned Aerial Vehicle (UAV) holds the potential to revolutionize their deployment, rendering it more intuitive and user-centric. In this research, we introduce a novel methodology adept at classifying three-dimensional human actions, leveraging them to coordinate on-field with a UAV. Utilizing a stereo camera, we derive both RGB and depth data, subsequently extracting three-dimensional human poses from the continuous video feed. This data is then processed through our proposed k-nearest neighbour classifier, the results of which dictate the behaviour of the UAV. It also includes mechanisms ensuring the robot perpetually maintains the human within its visual purview, adeptly tracking user movements. We subjected our approach to rigorous testing involving multiple tests with real robots. The ensuing results, coupled with comprehensive analysis, underscore the efficacy and inherent advantages of our proposed methodology.

Recent developments in Artificial Intelligence (AI) provide unprecedented automation opportunities in the Architecture, Engineering, and Construction (AEC) industry. However, despite the enthusiasm regarding the use of AI, 85% of current big data projects fail. One of the main reasons for AI project failures in the AEC industry is the disconnect between those who plan or decide to use AI and those who implement it. AEC practitioners often lack a clear understanding of the capabilities and limitations of AI, leading to a failure to distinguish between what AI should solve, what it can solve, and what it will solve, treating these categories as if they are interchangeable. This lack of understanding results in the disconnect between AI planning and implementation because the planning is based on a vision of what AI should solve without considering if it can or will solve it. To address this challenge, this work introduces the LeanAI method. The method has been developed using data from several ongoing longitudinal studies analyzing AI implementations in the AEC industry, which involved 50+ hours of interview data. The LeanAI method delineates what AI should solve, what it can solve, and what it will solve, forcing practitioners to clearly articulate these components early in the planning process itself by involving the relevant stakeholders. By utilizing the method, practitioners can effectively plan AI implementations, thus increasing the likelihood of success and ultimately speeding up the adoption of AI. A case example illustrates the usefulness of the method.

Mining is an important sector in India, contributing to the country's economic growth. The Indian mining industry encompasses exploration, extraction, and processing of minerals and metallic and non-metallic minerals. With increasing demand for minerals, the mining industry in India has seen significant growth in recent years. In this report, we will analyze the current state of mining technology in India and its impact on the mining industry. The mining industry in India has made significant advancements in recent years, with the introduction of modern technology.

The use of computers and control systems in every industry has become very important in the last two decades. This is because computers are the backbone of the development of an industry. Information technology (computers, control systems) is used to handle all types of industrial methods; it also controls the processes of the planted machinery, increases efficiency, manually replaces the industry's workers, and enhances the speed and quality of that industry. All of these uses are called Industrial automation and robotics. Industrial automation and robotics cover a wide range of control systems from any production methods assembly lines, medical and aircraft etc.

According to research by the National Restaurant Association Research and Knowledge Group, the restaurant industry will be drastically different by the year 2030. WIthin a decade, it could be possible for an individual to approach a drive-through in an autonomous vehicle, order through an AI-powered voice ordering assistant, and eat food that was prepared by robots. One of the most startling aspects of this interaction is that there is not a single human involved besides the consumer. These changes will cause massive implications for every aspect of the sector, most importantly for the workers and consumers. Restaurants and food joints, once reliable venues for human interaction, will be important for the implementation of artificial intelligence (AI) technology.