Xiaowen Su
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1
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About
Xiaowen Su is a robotics researcher whose work focuses on enhancing the safety and operational efficiency of industrial automation systems, particularly Delta robots. Su’s most significant contribution addresses a critical challenge in human-robot collaboration: collision detection and response. In their highly cited 2024 paper, Su introduced a novel generalized momentum extended state observer method to accurately estimate external torque without requiring additional sensors. This approach enables robots to detect unexpected collisions in real-time and respond with appropriate strategies, dramatically improving safety in high-speed pick-and-place applications. While the field is still emerging, Su’s work has already garnered attention for its practical, implementable solution to a pressing industrial problem. The research bridges theoretical control systems with real-world robotic safety requirements, offering manufacturers a cost-effective way to deploy Delta robots in shared workspaces. Su’s methodology stands out for its elegance—leveraging existing robot dynamics rather than expensive hardware—making it accessible for widespread adoption. As collaborative robotics continues to expand, Su’s contributions to collision detection and safe automation are poised to become foundational references for engineers designing the next generation of human-safe industrial robots.
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