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This paper describes a novel approach to adaptive manufacturing in thecontext of small batch production and customization. It focuses on integratingtask-level planning and reasoning with reinforcement learning (RL) in theSkiROS2 skill-based robot control platform. This integration enhances theefficiency and adaptability of robotic systems in manufacturing, enabling themto adjust to task variations and learn from interaction data. The paperhighlights the architecture of SkiROS2, particularly its world model, skilllibraries, and task management. It demonstrates how combining RL with roboticmanipulators can learn and improve the execution of industrial tasks. Itadvocates a multi-objective learning model that eases the learning problemdesign. The approach can incorporate user priors or previous experiences toaccelerate learning and increase safety.  Spotlight video: https://youtu.be/H5PmZl2rRbs?si=8wmZ-gbwuSJRxe3S&t=1422  SkiROS2 code: https://github.com/RVMI/skiros2  SkiROS2 talk at ROSCon: https://vimeo.com/879001825/2a0e9d5412  SkiREIL code: https://github.com/matthias-mayr/SkiREIL

Flexible and Adaptive Manufacturing by Complementing Knowledge Representation, Reasoning and Planning with Reinforcement Learning