Jiaming Chen

Northeastern University, Zhengzhou University

Papers

Total Citations

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About

Jiaming Chen is a robotics researcher whose work focuses on bridging the gap between human demonstration and robot skill acquisition, particularly in complex and bionic systems. His primary research areas include learning from demonstration (LfD), reinforcement learning for robotic control, and the application of smart materials in robotics. Chen’s most cited work, “Learning complex assembly skills from kinect based human robot interaction” (2017, 8 citations), addresses the fundamental challenge of transferring human assembly knowledge to robots by using Kinect-based motion capture, converting human movements into robot-executable skills. This contribution is critical for advancing intuitive human-robot collaboration in manufacturing. He further explores adaptive control in “Reinforcement Learning Control of a Shape Memory Alloy-based Bionic Robotic Hand” (2019, 7 citations), where he tackles the hysteresis and dynamic modeling of SMA actuators to create a low-noise, high-power-to-weight ratio robotic hand. Additionally, his work on learning quasi-periodic motions (2019) extends LfD to rhythmic tasks. Chen’s research, though early in impact, lays foundational methods for making robots more adaptable and human-like, with potential applications in prosthetics and automated assembly.