Papers
4
Total Citations
50
H-Index
3
About
Ci Chen is a leading researcher in multi-robot systems and legged robot locomotion, with a focus on integrating advanced control theory with reinforcement learning. Their work spans two critical frontiers: distributed coordination and fault-tolerant robotics. Chen’s most cited paper (2022, 27 citations) introduces a neural-network-optimized distributed model predictive control strategy for nonholonomic multi-robot formation tracking, addressing the complex challenge of consensus under motion constraints. More recently, Chen has pioneered research in quadruped robot resilience, notably developing a meta-reinforcement learning framework that enables locomotion policies to adapt when motors become stuck—a significant contribution to fault-tolerant control, an area with limited prior work. Chen also explores co-optimization of morphology and gait for small-scale legged robots using deep reinforcement learning (2023, 10 citations), reducing actuator counts without sacrificing mobility. Their 2024 work on real-time motion and foothold planning for discrete terrain further advances autonomous legged navigation. With a growing citation impact and a portfolio addressing both theoretical and practical challenges, Chen is shaping the future of resilient, adaptive robotic systems.
Research Focus
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