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About

Daniel Schilberg is a robotics and automation researcher whose work spans industrial robotics, human-robot collaboration, cyber-physical systems, and the emerging frontier of assistive and agricultural robotics. Over the course of his career, he has made meaningful contributions to how intelligent robotic systems are designed, planned, and safely deployed in both industrial and rehabilitative contexts. His most-cited work, a 2023 comprehensive review of wearable assistive robotic devices for head and neck rehabilitation (36 citations), highlights his growing engagement with healthcare robotics and musculoskeletal recovery technologies. Earlier contributions, including research on efficient collision avoidance for industrial manipulators (2014) and self-optimized assembly planning for ROS-based robot cells (2012), established his reputation in practical, safety-conscious industrial automation. His work on interoperability in smart automation of cyber-physical systems (2016) further reflects his interest in integrated, intelligent manufacturing environments. Schilberg has also demonstrated a commitment to robotics education, co-developing problem-based learning approaches using LEGO Mindstorms platforms. More recently, his attention has turned toward AI-driven agricultural automation and humanoid robotics in farming. With over 115 total citations across diverse domains, his body of work reflects a versatile and forward-looking perspective on robotics research and its real-world applications.