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About

Simon Storms is a robotics and automation researcher whose work spans industrial robot programming, machining, dynamics modeling, and intelligent production systems. His research addresses some of the most persistent challenges in deploying industrial robots at scale — particularly their limited stiffness and positional accuracy in demanding manufacturing contexts like milling operations. Storms has made notable contributions to robot-based machining, developing model-driven process planning and feed-forward control strategies to compensate for process forces during milling — work that has collectively attracted over 20 citations and represents a meaningful step toward making robotic machining industrially viable. His interest in flexible, reconfigurable production systems is reflected in simulation frameworks for virtual robot programming and approaches to simplifying collaborative workstation commissioning. More recently, Storms has pushed into data-driven territory, applying deep learning — including transformer networks — to robot dynamics modeling, and exploring sim-to-real transfer in reinforcement learning for compliant assembly tasks. His 2023 work on friction modeling for structured learning signals a growing focus on combining physics-informed and machine learning approaches. With a publication record spanning foundational automation concepts to cutting-edge AI applications, Storms represents a bridge between classical robotics engineering and modern intelligent manufacturing research.