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About

Yinjie Lin is a robotics researcher whose work sits at the intersection of motion control, force control, and human-robot interaction, with a particular focus on enabling robots to operate safely and intelligently in complex, real-world environments. His most influential contribution, "Unified Motion/Force/Impedance Control for Manipulators in Unknown Contact Environments" (2021, 77 citations), introduced a robust model-reaching framework that allows robotic manipulators to seamlessly handle both high-speed positioning and compliant contact tasks — a critical capability for automated assembly applications. Complementing this, his research on series elastic actuators (SEA) has advanced decoupled torque control strategies with adaptive robust compensation, earning 51 citations and strengthening the safety foundations of collaborative robotics. Lin has further extended unified control methodologies to task-space frameworks for redundant manipulators, and more recently has explored data-driven approaches, including vision-force-fused curriculum learning for contact-rich assembly tasks. His 2025 work on rigid-body dynamics identification addresses longstanding challenges in precisely characterizing industrial robot parameters. Across his career, Lin's research has collectively garnered nearly 200 citations, establishing him as a notable voice in intelligent robotic manipulation and physical human-robot interaction.