Xiaoyang Jiang

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About

Xiaoyang Jiang is a pioneering researcher at the intersection of robotics, artificial intelligence, and advanced manufacturing. His work centers on developing intelligent control systems for humanoid and legged robots, with a particular focus on bridging the gap between simulation and real-world deployment. Jiang’s most influential contribution is his 2024 paper "Prompt, Plan, Perform: LLM-based Humanoid Control via Quantized Imitation Learning" (28 citations), which introduces a novel framework that leverages large language models to generate high-level motion plans, subsequently executed through quantized imitation learning—a breakthrough that enables more versatile and task-agnostic humanoid control without the need for task-specific reward engineering. In parallel, Jiang has made significant strides in robotic machining, as evidenced by his 2023 work on "Surface defect and chatter monitoring in robotic drilling CFRP composites using acoustic emission technique" (10 citations), which addresses critical quality-control challenges in aerospace manufacturing. His most recent 2025 paper, "Fully Spiking Neural Network for Legged Robots" (6 citations), pushes the boundaries of neuromorphic computing for quadruped locomotion, promising ultra-low-power, biologically inspired control. With a growing citation impact and a trajectory from practical manufacturing solutions to cutting-edge AI-driven robotics, Jiang is establishing himself as a key figure in the next generation of embodied intelligence research.