Automated vehicles need to be aware of the capabilities they currently possess. Skill graphs are directed acylic graphs in which a vehicle's capabilities and the dependencies between these capabilities are modeled. The skills a vehicle requires depend on the behaviors the vehicle has to perform and the operational design domain (ODD) of the vehicle. Skill graphs were originally proposed for online monitoring of the current capabilities of an automated vehicle. They have also been shown to be useful during other parts of the development process, e.g. system design, system verification. Skill graph construction is an iterative, expert-based, manual process with little to no guidelines. This process is, thus, prone to errors and inconsistencies especially regarding the propagation of changes in the vehicle's intended ODD into the skill graphs. In order to circumnavigate this problem, we propose to formalize expert knowledge regarding skill graph construction into a knowledge base and automate the construction process. Thus, all changes in the vehicle's ODD are reflected in the skill graphs automatically leading to a reduction in inconsistencies and errors in the constructed skill graphs.
Unsupervised Skill Discovery for Robotic Manipulation through Automatic Task Generation
NAVER LABS Europe（NAVER欧洲实验室）；TU Darmstadt（达姆施塔特工业大学）；EPITA Research Laboratory (LRE)（EPITA研究实验室）；German Research Center for AI (DFKI)（德国人工智能研究中心）；Center for Cognitive Science, TU Darmstadt（达姆施塔特工业大学认知科学中心）；Hessian Center for Artificial Intelligence (Hessian.ai)（黑森州人工智能中心）
Skill Drift Is Contract Violation: Proactive Maintenance for LLM Agent Skill Libraries
Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学人工智能学院 Gallagher 学院）；King Abdullah University of Science and Technology（国王 Abdullah 科学与技术大学）
Explainable AI-Powered Framework for Video-Based Skill Assessment in Cataract Surgery
K.N. Toosi University of Technology（霍加托西大学）；Faculties of Electrical and Computer Engineering（电气与计算机工程学院）；Applied Robotics and AI Solutions (ARAS)（应用机器人与人工智能解决方案（ARAS））
SKILLFOUNDRY: Building Self-Evolving Agent Skill Libraries from Heterogeneous Scientific Resources
Ray and Stephanie Lane Computational Biology Department, School of Computer Science, Carnegie Mellon University（卡内基梅隆大学计算机科学学院Ray and Stephanie Lane计算生物学系）；Machine Learning Department, School of Computer Science, Carnegie Mellon University（卡内基梅隆大学计算机科学学院机器学习系）
Automatic Coral Detection with YOLO: A Deep Learning Approach for Efficient and Accurate Coral Reef Monitoring
Cadi Ayyad University（卡迪·阿雅德大学）；University of La Réunion（留尼汪大学）；IRD（法国国家可持续发展研究所）；IFREMER（法国海洋开发研究院）；CNRS（法国国家科学研究中心）；University of New Caledonia（新喀里多尼亚大学）；Institut de Recherche pour le Développement（法国国家可持续发展研究所）；MNHN（法国国家自然历史博物馆）；EPHE（巴黎高等研究实践学院）
