Teawon Han

University of Southern California, Ford Motor Company (United States)

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About

Teawon Han’s research lies at the intersection of modular robotics and safe motion planning, with a focus on enabling robots to adapt dynamically to complex, real-world environments. In his early work on SuperBot—a modular and self-reconfigurable robot—Han demonstrated how versatile gaits could be optimized for sloped terrains, highlighting the platform’s superior adaptability and dexterity compared to conventional robots. This foundational study, published in 2012 and garnering 5 citations, established his commitment to overcoming the limitations of rigid robotic systems. More recently, Han has advanced the field of motion planning with his innovative DaSP-RRT (Data-Driven Safe Performance-Aware RRT) framework, introduced in 2025. This approach leverages data-driven invariant sets to guarantee collision-free paths with optimal performance, addressing critical safety and efficiency challenges in autonomous navigation. Although still early in its impact, DaSP-RRT represents a significant leap toward reliable, performance-aware robotic movement. Han’s work bridges the gap between adaptive hardware and intelligent software, offering practical solutions for robots operating in unpredictable settings—a contribution that promises to influence both modular robotics and autonomous systems for years to come.