Pipelines play a vital role in transporting energy resources that are essential for America’s economy and the well-being of the American people. A significant challenge facing the domestic pipeline infrastructure is the gradual deterioration of materials and structural components, which can result in serious safety risks. While regular in-line inspection (ILI) of pipelines is crucial for risk management, existing tools are often labor-intensive, expensive, and hard to adapt to different pipeline geometries. This Leading Engineering for America's Prosperity, Health, and Infrastructure (LEAP-HI) award advances fundamental and interdisciplinary research to enable safe, efficient, and automated inspection of gas transport pipelines for accurate risk assessment. The research integrates new knowledge in robotics, non-destructive evaluation (NDE), risk engineering, artificial intelligence (AI), and policy analysis to iteratively develop Learning-based Autonomous Risk Assessment Systems (LARAS). The project will include lab and field testing at different scales, create partnerships with industry and collect stakeholder feedback throughout the planned activities. The broader impacts of the project include the development of new curriculum and undergraduate research opportunities, outreach to local communities and professional societies, and online modules for workforce development. This interdisciplinary project will advance the science and engineering of automated pipeline i