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About

Chonhyon Park is a robotics researcher whose work spans motion planning, trajectory optimization, and human-robot interaction, with a particular focus on enabling robots to operate safely and efficiently in complex, dynamic environments. Park's most influential contribution, ITOMP (2012), introduced a stochastic trajectory optimization framework for real-time replanning in dynamic settings, accumulating 167 citations and becoming a foundational reference in optimization-based motion planning. Building on this, Park extended these methods to high-DOF robots and probabilistic collision detection, developing fast, bounded approaches for uncertain and cluttered environments. His 2018 contact planner for multiped robots — inspired by challenges exposed at the DARPA Robotics Challenge — demonstrated sophisticated legged locomotion across real-world terrains, earning 144 citations. Park has also advanced human-aware robotics through intention-recognition systems like I-Planner and HI Robot, which combine machine learning and motion prediction to facilitate safe navigation in shared human-robot workspaces. Additional contributions include novel algorithms for liquid pouring, autonomous warehouse picking, and high-dimensional blue-noise sampling. Collectively, Park's research reflects a sustained commitment to bridging theoretical motion planning with practical robotic deployment across diverse and challenging scenarios.