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About

Chengzhen Yan is a rising researcher in robotics, specializing in safe and resilient autonomous navigation and legged locomotion. His work addresses critical challenges in deploying robots in unstructured and adversarial environments. Yan’s most impactful contributions lie in mapless navigation, where he pioneered safety-enhanced imitation learning to train robots to explore unknown spaces without pre-built maps, guaranteeing collision avoidance during training—a key advance over prior learning-based methods. His 2022 paper on this topic has already garnered 32 citations, reflecting its influence. He further advanced the field with an efficient deep reinforcement learning algorithm that uses a gap-guided switching strategy for smoother, more reliable navigation. Beyond ground robots, Yan has tackled the problem of resilient state estimation for mobile sensor networks under cyber-attacks, introducing an invariant extended Kalman filter to maintain accuracy despite deception. Most recently, he has addressed the practical challenge of slip detection and recovery for quadruped robots, developing an orthogonal decomposition method that enables stable locomotion on slippery surfaces. With a growing citation record and work spanning from theoretical estimation to real-world robot control, Yan is establishing himself as a versatile engineer advancing the safety and robustness of autonomous systems.