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About

Kaixian Qu is a robotics researcher whose work lies at the intersection of motion planning, control, and embodied artificial intelligence. His primary research areas include legged and legged-wheeled locomotion, long-horizon motion planning, and the integration of large language models with robotic systems. Qu’s most notable contribution is the development of the Long Short-Term Motion Planning (LSTP) framework, which combines sampling-based planning with numerical optimization to generate robust, efficient motion for diverse ground robot morphologies. This hybrid approach, published in 2023, has already garnered 28 citations for its practical applicability in real-world robotics. More recently, Qu introduced ROS-LLM, a pioneering framework that bridges the Robot Operating System with large language models to enable more intuitive and adaptive embodied AI. Though published in 2025, this work has quickly accumulated 7 citations, signaling its potential to reshape human-robot interaction. Qu’s research is distinguished by its focus on scalable, morphology-agnostic solutions that push the boundaries of autonomous navigation and decision-making in complex environments.