Frontiers in Robotics and AI最新文献

{"title":"On realizing autonomous transport services in multi story buildings with doors and elevators.","authors":"Paul Robert Schulze, Steffen Müller, Tristan Müller, Horst-Michael Gross","doi":"10.3389/frobt.2025.1546894","DOIUrl":"10.3389/frobt.2025.1546894","url":null,"abstract":"<p><p>Mobile service robots for transportation tasks are usually restricted to a barrier-free environment where they can navigate freely. To enable the use of such assistive robots in existing buildings, the robot should be able to overcome closed doors independently and operate elevators with the interface designed for humans while being polite to passers-by. The integration of these required capabilities in an autonomous mobile service robot is explained using the example of a SCITOS G5 robot equipped with differential drive and a Kinova Gen II arm with 7 DoF. This robot also defines the framework conditions with certain limitations in terms of maneuverability and perceptual abilities. Results of field tests with that robot in an elderly care facility as well as in a university office building are shown, where it performed transportation and messaging tasks. We also report on the success rates achieved and highlight the main problems we have encountered and dicsuss open issues.</p>","PeriodicalId":47597,"journal":{"name":"Frontiers in Robotics and AI","volume":"12 ","pages":"1546894"},"PeriodicalIF":2.9,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11894313/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143606335","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unsupervised semantic label generation in agricultural fields.","authors":"Gianmarco Roggiolani, Julius Rückin, Marija Popović, Jens Behley, Cyrill Stachniss","doi":"10.3389/frobt.2025.1548143","DOIUrl":"10.3389/frobt.2025.1548143","url":null,"abstract":"<p><p>Robust perception systems allow farm robots to recognize weeds and vegetation, enabling the selective application of fertilizers and herbicides to mitigate the environmental impact of traditional agricultural practices. Today's perception systems typically rely on deep learning to interpret sensor data for tasks such as distinguishing soil, crops, and weeds. These approaches usually require substantial amounts of manually labeled training data, which is often time-consuming and requires domain expertise. This paper aims to reduce this limitation and propose an automated labeling pipeline for crop-weed semantic image segmentation in managed agricultural fields. It allows the training of deep learning models without or with only limited manual labeling of images. Our system uses RGB images recorded with unmanned aerial or ground robots operating in the field to produce semantic labels exploiting the field row structure for spatially consistent labeling. We use the rows previously detected to identify multiple crop rows, reducing labeling errors and improving consistency. We further reduce labeling errors by assigning an \"unknown\" class to challenging-to-segment vegetation. We use evidential deep learning because it provides predictions uncertainty estimates that we use to refine and improve our predictions. In this way, the evidential deep learning assigns high uncertainty to the weed class, as it is often less represented in the training data, allowing us to use the uncertainty to correct the semantic predictions. Experimental results suggest that our approach outperforms general-purpose labeling methods applied to crop fields by a large margin and domain-specific approaches on multiple fields and crop species. Using our generated labels to train deep learning models boosts our prediction performance on previously unseen fields with respect to unseen crop species, growth stages, or different lighting conditions. We obtain an IoU of 88.6% on crops, and 22.7% on weeds for a managed field of sugarbeets, where fully supervised methods have 83.4% on crops and 33.5% on weeds and other unsupervised domain-specific methods get 54.6% on crops and 11.2% on weeds. Finally, our method allows fine-tuning models trained in a fully supervised fashion to improve their performance in unseen field conditions up to +17.6% in mean IoU without additional manual labeling.</p>","PeriodicalId":47597,"journal":{"name":"Frontiers in Robotics and AI","volume":"12 ","pages":"1548143"},"PeriodicalIF":2.9,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11893429/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143606340","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Editorial: AI-powered musical and entertainment robotics.","authors":"Huijiang Wang, Josie Hughes, Tetsushi Nonaka, Arsen Abdulali, Thilina Dulantha Lalitharatne, Fumiya Iida","doi":"10.3389/frobt.2025.1572828","DOIUrl":"https://doi.org/10.3389/frobt.2025.1572828","url":null,"abstract":"","PeriodicalId":47597,"journal":{"name":"Frontiers in Robotics and AI","volume":"12 ","pages":"1572828"},"PeriodicalIF":2.9,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11893408/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143606222","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CARE: towards customized assistive robot-based education.","authors":"Nafisa Maaz, Jinane Mounsef, Noel Maalouf","doi":"10.3389/frobt.2025.1474741","DOIUrl":"10.3389/frobt.2025.1474741","url":null,"abstract":"&lt;p&gt;&lt;p&gt;This study proposes a novel approach to enhancing the learning experience of elementary school students by integrating Artificial Intelligence (AI) and robotics in education, focusing on personalized and adaptive learning. Unlike existing adaptive and intelligent tutoring systems, which primarily rely on digital platforms, our approach employs a personalized tutor robot to interact with students directly, combining cognitive and emotional assessment to deliver tailored educational experiences. This work extends the current research landscape by integrating real-time facial expression analysis, subjective feedback, and performance metrics to classify students into three categories: Proficient Students (Prof.S), Meeting-Expectations Students (MES), and Developing Students (DVS). These classifications are used to deliver customized learning content, motivational messages, and constructive feedback. The primary research question guiding this study is: Does personalization enhance the effectiveness of a robotic tutor in fostering improved learning outcomes? To address this, the study explores two key aspects: (1) how personalization contributes to a robotic tutor's ability to adapt to individual student needs, thereby enhancing engagement and academic performance, and (2) how the effectiveness of a personalized robotic tutor compares to a human teacher, which serves as a benchmark for evaluating the system's impact. Our study contrasts the personalized robot with a human teacher to highlight the potential of personalization in robotic tutoring within a real-world educational context. While a comparison with a generic, unpersonalized robot could further isolate the impact of personalization, our choice of comparison with a human teacher underscores the broader objective of positioning personalized robotic tutors as viable and impactful educational tools. The robot's AI-powered system, employing the XGBoost algorithm, predicts the student's proficiency level with high accuracy (100%), leveraging factors such as test scores, task completion time, and emotional engagement. Challenges and learning materials are dynamically adjusted to suit each student's needs, with DVS receiving supportive exercises and Prof. S receiving advanced tasks. Our methodology goes beyond existing literature by embedding a fully autonomous robotic system within a classroom setting to assess and enhance learning outcomes. Evaluation through post-diagnostic exams demonstrated that the experimental group of students using the AI-robot system showed a significant improvement rate (approximately 8%) over the control group. These findings highlight the unique contribution of this study to the field of Human-Robot Interaction (HRI) and educational robotics, showcasing how integrating AI and robotics in a real-world learning environment can engage students and improve educational outcomes. By situating our work within the broader context of intelligent tutoring systems and addressi","PeriodicalId":47597,"journal":{"name":"Frontiers in Robotics and AI","volume":"12 ","pages":"1474741"},"PeriodicalIF":2.9,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11885127/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143587587","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From traditional robotic deployments towards assisted robotic deployments in nuclear decommissioning.","authors":"Erwin Jose Lopez Pulgarin, Dave Hopper, Jon Montgomerie, James Kell, Joaquin Carrasco, Guido Herrmann, Alexander Lanzon, Barry Lennox","doi":"10.3389/frobt.2025.1432845","DOIUrl":"10.3389/frobt.2025.1432845","url":null,"abstract":"<p><p>The history around teleoperation and deployment of robotic systems in constrained and dangerous environments such as nuclear is a long and successful one. From the 1940s, robotic manipulators have been used to manipulate dangerous substances and enable work in environments either too dangerous or impossible to be operated by human operators. Through the decades, technical and scientific advances have improved the capabilities of these devices, whilst allowing for more tasks to be performed. In the case of nuclear decommissioning, using such devices for remote inspection and remote handling has become the only solution to work and survey some areas. Such applications deal with challenging environments due to space constrains, lack of up-to-date structural knowledge of the environment and poor visibility, requiring much training and planning to succeed. There is a growing need to speed these deployment processes and to increase the number of decommissioning activities whilst maintaining high levels of safety and performance. Considering the large number of research and innovation being done around improving robotic capabilities, numerous potential benefits could be made by translating them to the nuclear decommissioning use cases. We believe such innovations, in particular improved feedback mechanisms from the environment during training and deployments (i.e., Haptic Digital Twins) and higher modes of assisted or supervised control (i.e., Semi-autonomous operation) can play a large role. We list some of the best practices currently being followed in the industry around teleoperation and robotic deployments and the potential benefits of implementing the aforementioned innovations.</p>","PeriodicalId":47597,"journal":{"name":"Frontiers in Robotics and AI","volume":"12 ","pages":"1432845"},"PeriodicalIF":2.9,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11893983/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143606334","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Italian version of the unified theory of acceptance and use of technology questionnaire: a pilot validation study.","authors":"Alfonsina D'Iorio, Federica Garramone, Silvia Rossi, Chiara Baiano, Gabriella Santangelo","doi":"10.3389/frobt.2025.1371583","DOIUrl":"10.3389/frobt.2025.1371583","url":null,"abstract":"<p><strong>Background: </strong>The Unified Theory of Acceptance and Use of Technology is a self-rated questionnaire to assess twelve constructs related to the level of acceptance of a robot, consisting of 41 items rated on a 5-point Likert scale. The aim of the study was to conduct a preliminary evaluation of the psychometric properties of the Italian version of the UTAUT (I-UTAUT) in a sample of Italian healthy subjects (HCs).</p><p><strong>Materials and methods: </strong>30 HCs underwent the I-UTAUT to assess its comprehensibility. Reliability and divergent validity of the I-UTAUT were evaluated in a sample of 121 HCs, who also underwent the Montreal Cognitive Assessment (MoCA).</p><p><strong>Results: </strong>The final I-UTAUT version was easily comprehensible. There were no missing data, no floor and ceiling effects. Contrarily to the original version, the Principal Components Analysis suggested a seven-component structure; Cronbach's alpha was 0.94. The I-UTAUT score did not correlate with MoCA.</p><p><strong>Conclusion: </strong>The I-UTAUT represented a reliable and valid questionnaire to identify the level of acceptance of robotics technology in Italian healthy sample.</p>","PeriodicalId":47597,"journal":{"name":"Frontiers in Robotics and AI","volume":"12 ","pages":"1371583"},"PeriodicalIF":2.9,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11872730/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143544123","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial social intelligence in teamwork: how team traits influence human-AI dynamics in complex tasks.","authors":"Rhyse Bendell, Jessica Williams, Stephen M Fiore, Florian Jentsch","doi":"10.3389/frobt.2025.1487883","DOIUrl":"10.3389/frobt.2025.1487883","url":null,"abstract":"<p><p>This study examines the integration of Artificial Social Intelligence (ASI) into human teams, focusing on how ASI can enhance teamwork processes in complex tasks. Teams of three participants collaborated with ASI advisors designed to exhibit Artificial Theory of Mind (AToM) while engaged in an interdependent task. A profiling model was used to categorize teams based on their taskwork and teamwork potential and study how these influenced perceptions of team processes and ASI advisors. Results indicated that teams with higher taskwork or teamwork potential had more positive perceptions of their team processes, with those high in both dimensions showing the most favorable views. However, team performance significantly mediated these perceptions, suggesting that objective outcomes strongly influence subjective impressions of teammates. Notably, perceptions of the ASI advisors were not significantly affected by team performance but were positively correlated with higher taskwork and teamwork potential. The study highlights the need for ASI systems to be adaptable and responsive to the specific traits of human teams to be perceived as effective teammates.</p>","PeriodicalId":47597,"journal":{"name":"Frontiers in Robotics and AI","volume":"12 ","pages":"1487883"},"PeriodicalIF":2.9,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11873349/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143544033","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A comprehensive perspective on electric vehicles as evolutionary robots.","authors":"Haoyang Che, Shaolin Wang, Lei Yao, Ying Gu","doi":"10.3389/frobt.2025.1499215","DOIUrl":"https://doi.org/10.3389/frobt.2025.1499215","url":null,"abstract":"<p><p>Multi-robot systems exhibit different application forms in human life, among these, electric vehicles (EVs) at rest and in motion can be perceived as a specialized category of multi-robot systems with increasingly sophisticated vehicle functions and a certain degree of flexibility, and most notably, the ability to iteratively evolve. However, for EVs to evolve into the next-generation of multi-robot systems, more complex technical and operational mechanisms shall be fully cultivated in EVs to develop their evolutionary capabilities, including, but not limited to multimodal environmental sensing techniques, advanced telematics communication protocols such as 5G, Over-The-Air (OTA) upgrade functions, real-time backend data lake analytics, and user-centric marketing initiatives. As it stands, these mechanisms are evidently insufficient for realizing genuine evolutionary robots (ER), especially in unstructured environments. The overarching perspective of conceptualizing EV as ER is not always prominently featured in academic literature. This manuscript provides a succinct overview of the ongoing transition from Software-Defined Vehicles (SDV) to Artificial Intelligence-Defined Vehicles (AIDV), and examines the ongoing research focused on the utilization of electric vehicles as mobile edge computing platforms. Furthermore, it discusses the fundamental evolutionary competencies that define modern electric vehicles, establishing the core tenets upon which our analysis is predicated. To transcend the <i>status quo</i>, we underscore the imperative and pressing need for profound transformations across a spectrum of pivotal domains within the field. Furthermore, this endeavor aims to amplify the reach and influence of research on EVs as ERs, potentially catalyzing the emergence of several niche research areas.</p>","PeriodicalId":47597,"journal":{"name":"Frontiers in Robotics and AI","volume":"12 ","pages":"1499215"},"PeriodicalIF":2.9,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11872702/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143544029","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adaptive formation learning control for cooperative AUVs under complete uncertainty.","authors":"Emadodin Jandaghi, Mingxi Zhou, Paolo Stegagno, Chengzhi Yuan","doi":"10.3389/frobt.2024.1491907","DOIUrl":"https://doi.org/10.3389/frobt.2024.1491907","url":null,"abstract":"<p><strong>Introduction: </strong>This paper addresses the critical need for adaptive formation control in Autonomous Underwater Vehicles (AUVs) without requiring knowledge of system dynamics or environmental data. Current methods, often assuming partial knowledge like known mass matrices, limit adaptability in varied settings.</p><p><strong>Methods: </strong>We proposed two-layer framework treats all system dynamics, including the mass matrix, as entirely unknown, achieving configuration-agnostic control applicable to multiple underwater scenarios. The first layer features a cooperative estimator for inter-agent communication independent of global data, while the second employs a decentralized deterministic learning (DDL) controller using local feedback for precise trajectory control. The framework's radial basis function neural networks (RBFNN) store dynamic information, eliminating the need for relearning after system restarts.</p><p><strong>Results: </strong>This robust approach addresses uncertainties from unknown parametric values and unmodeled interactions internally, as well as external disturbances such as varying water currents and pressures, enhancing adaptability across diverse environments.</p><p><strong>Discussion: </strong>Comprehensive and rigorous mathematical proofs are provided to confirm the stability of the proposed controller, while simulation results validate each agent's control accuracy and signal boundedness, confirming the framework's stability and resilience in complex scenarios.</p>","PeriodicalId":47597,"journal":{"name":"Frontiers in Robotics and AI","volume":"11 ","pages":"1491907"},"PeriodicalIF":2.9,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11868763/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143544027","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A mini-review on mobile manipulators with Variable Autonomy.","authors":"Cesar Alan Contreras, Alireza Rastegarpanah, Manolis Chiou, Rustam Stolkin","doi":"10.3389/frobt.2025.1540476","DOIUrl":"https://doi.org/10.3389/frobt.2025.1540476","url":null,"abstract":"<p><p>This paper presents a mini-review of the current state of research in mobile manipulators with variable levels of autonomy, emphasizing their associated challenges and application environments. The need for mobile manipulators in different environments, especially hazardous ones such as decommissioning and search and rescue, is evident due to the unique challenges and risks each presents. Many systems deployed in these environments are not fully autonomous, requiring human-robot teaming to ensure safe and reliable operations under uncertainties. Through this analysis, we identify gaps and challenges in the literature on Variable Autonomy, including cognitive workload and communication delays, and propose future directions, including whole-body Variable Autonomy for mobile manipulators, virtual reality frameworks, and large language models to reduce operators' complexity and cognitive load in some challenging and uncertain scenarios.</p>","PeriodicalId":47597,"journal":{"name":"Frontiers in Robotics and AI","volume":"12 ","pages":"1540476"},"PeriodicalIF":2.9,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11867941/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143544031","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}