Zhutian Yang

Papers

Total Citations

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About

Zhutian Yang is a rising researcher at the intersection of robotics, artificial intelligence, and task planning. Her work focuses on bridging the gap between high-level reasoning and low-level physical feasibility in autonomous systems. Yang’s most cited paper, “Guiding Long-Horizon Task and Motion Planning with Vision Language Models” (2025, 16 citations), introduces a novel framework that leverages Vision-Language Models (VLMs) to generate plausible high-level plans from visual scenes and natural language goals. Her key contribution lies in identifying and addressing a critical limitation: while VLMs excel at semantic reasoning, they cannot guarantee that generated actions are geometrically or kinematically feasible for a specific robot. This insight has opened new avenues for integrating symbolic planning with motion constraints, making her work essential for researchers developing robust, real-world robotic systems. With a growing citation record and a focus on long-horizon planning, Yang is establishing herself as a key voice in embodied AI, where her research promises to enable more reliable and autonomous robots in complex environments.