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About

Wenqi Shao is a leading researcher at the forefront of Embodied AI, specializing in bridging the gap between high-level reasoning and low-level robotic control. Her primary research areas center on multimodal code generation and task planning for open-world manipulation, where she develops frameworks that enable robots to translate complex, free-form instructions into precise physical actions. Shao’s most impactful contributions include pioneering the use of large language and multimodal models for robotic behavior synthesis, as demonstrated in her highly cited works "RoboScript" and "RoboCodeX" (both 2024, with 3 citations each). These studies address a critical bottleneck in Embodied AI: converting multimodal inputs—such as visual scenes and natural language commands—into executable code for real-world and simulated robots. By focusing on code generation for free-form manipulation tasks, Shao’s research has advanced the practical deployment of intelligent agents capable of general common sense reasoning and adaptive planning. Her work is notable for its emphasis on cross-platform validation, ensuring that theoretical models translate effectively from simulation to reality. Through these achievements, Wenqi Shao is shaping the next generation of autonomous robotic systems, making her a key figure to watch in the evolving landscape of Embodied AI.