Ao Xi
Papers
3
Total Citations
67
H-Index
3
About
Ao Xi is a robotics researcher specializing in reinforcement learning-based control systems for bipedal locomotion and dynamic stability. His work centers on developing intelligent control frameworks that enable humanoid robots to maintain balance and walk across challenging, unpredictable environments — a problem of significant practical importance for real-world robotics deployment. Xi's most influential contribution, "Balance Control of a Biped Robot on a Rotating Platform Based on Efficient Reinforcement Learning" (2019, 39 citations), introduced a hybrid approach combining model-based and model-free reinforcement learning to stabilize a NAO humanoid robot on a rotating platform with unknown angular velocity. This elegant fusion of learning paradigms addressed a critical limitation in existing methods, demonstrating robust performance under genuine environmental uncertainty. Building on this foundation, his subsequent works extended the framework to handle walking on both static and dynamic platforms (2020, 19 citations) and multi-degree-of-freedom oscillating surfaces (2020, 9 citations), progressively tackling greater complexity. Collectively, Xi's research has garnered over 67 citations, establishing him as a meaningful contributor to the intersection of deep reinforcement learning and humanoid robot control. His work offers promising pathways toward robots capable of operating reliably in real-world, dynamically unstable conditions.
Research Focus
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Top Papers
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