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About

Qiayuan Liao is an emerging robotics researcher whose work spans legged locomotion control, humanoid robotics, and reinforcement learning-based robot learning. His research focuses on enabling robots to operate safely and efficiently in complex real-world environments, with particular emphasis on quadrupedal and humanoid platforms. Among his most influential contributions is a safety-critical locomotion framework for quadrupedal robots navigating cluttered spaces, employing exponential Discrete Control Barrier Functions with duality-based optimization — a paper that has already garnered 26 citations since 2023. His work on leveraging morphological symmetry in reinforcement learning (16 citations) addresses fundamental exploration challenges in model-free RL, improving locomotion robustness and behavioral diversity. Liao has also made significant strides in democratizing humanoid robotics research. The Berkeley Humanoid platform (14 citations) offers a reliable, low-cost research system optimized for learning-based control, while the open-source Berkeley Humanoid Lite extends accessibility through 3D-printed hardware. Complementing these efforts, his contributions to MuJoCo Playground and CurricuLLM reflect a commitment to streamlining robot learning pipelines through simulation tools and LLM-guided curriculum design. Collectively, his work positions him as a rising force in physically capable, learning-driven robotic systems.