Papers
4
Total Citations
229
H-Index
3
About
Zichen Yan is a robotics researcher whose work centers on the control, safety, and human-robot interaction of complex manipulator systems. Yan’s major contributions lie in developing neural-learning-based control strategies for constrained robotic manipulators with flexible joints, addressing the significant challenge of system uncertainties—a foundational paper that has garnered 175 citations. Expanding into human-robot collaboration, Yan has pioneered bidirectional bimanual handover systems for large, planar objects, enabling more natural and efficient human-to-cobot interactions, as demonstrated in works with 29 and 22 citations. More recently, Yan has advanced risk-aware policy learning through dual-agent reinforcement learning, introducing a safety correction framework that balances exploration and performance in unstructured tasks. This work, though newer with 3 citations, points toward a critical direction for deploying robots safely in dynamic environments. Yan’s research is notable for bridging theoretical control methods with practical, real-world robotic applications, making significant strides in making robots more capable and safer partners for humans.
Research Focus
Key Achievements
Top Papers
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