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About

Ying Jin is a versatile robotics and intelligent systems researcher whose work spans motion planning, rehabilitation robotics, and autonomous task allocation. With a career bridging human-assistive technologies and advanced AI-driven control systems, Jin has made meaningful contributions to both clinical and computational domains of robotics engineering. Jin's most influential work, garnering 25 citations, introduced an improved Deep Deterministic Policy Gradient (DDPG) algorithm for six-degree-of-freedom robotic arm motion planning, demonstrating how reinforcement learning can be effectively applied to complex manipulator control using the widely adopted UR5 platform. Earlier in their career, Jin contributed significantly to upper-limb rehabilitation robotics through the development of the "Hybrid-PLEMO" system — a novel active/passive force feedback rehabilitation platform designed to expand therapeutic motion capabilities beyond the limitations of conventional two-DOF systems. This work, published across multiple studies between 2008 and 2009 and accumulating over 19 citations collectively, highlighted Jin's commitment to translating robotics research into meaningful clinical applications for stroke patients and elderly users. More recently, Jin has explored intelligent task allocation for wargame simulation environments, reflecting a broadening interest in multi-agent systems and heuristic optimization. Jin's body of work exemplifies a thoughtful integration of human-centered design and cutting-edge machine intelligence.