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About

Lingyun Chen is a robotics researcher whose work spans motion planning, manipulation, and human-robot interaction. Their key contributions include developing the Force Direction Informed Trees (FDIT*) algorithm, a sampling-based planner that enhances pathfinding speed and cost-effectiveness in high-dimensional spaces—a significant advance for autonomous navigation. Chen also pioneered a self-adaptable tactile insertion framework using Behavior Trees operating at 1 kHz, enabling robots to dynamically adjust insertion strategies in real time, a critical skill for manufacturing. Their passivity-based approach to relocating high-frequency robot controllers to the edge cloud addresses computational bottlenecks, reducing onboard energy consumption while maintaining stability. Chen’s work on learning barrier-certified polynomial dynamical systems further improves obstacle avoidance in robot learning from demonstrations. With over 41 citations across their most-cited papers, their research has practical impact, including contributions to the RoboCup Small Size League, where their team ZJUNlict won the 2018 championship through optimized mechanical and dribbling systems. Chen’s interdisciplinary work—bridging theory and application—positions them as a rising figure in robotics, with clear implications for industrial automation and autonomous systems.