Sucheng Qian

Papers

Total Citations

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About

Sucheng Qian is a researcher whose work bridges computer vision, robotics, and embodied AI, with a focus on articulated object understanding. Qian’s most notable contribution is the creation of **AKB-48**, a large-scale, real-world articulated object knowledge base that provides comprehensive annotations of appearance, structure, physical properties, and semantics. This resource addresses a critical gap in the field, where most prior work relied on synthetic datasets that fail to capture the complexity of real-world objects. By enabling more robust perception and interaction with articulated objects—such as cabinets, drawers, and doors—Qian’s work has direct implications for robotic manipulation and scene understanding. The primary paper on AKB-48 has garnered **64 citations**, reflecting its growing influence among researchers tackling embodied AI challenges. Qian’s efforts stand out for their emphasis on real-world data collection and multi-modal understanding, offering a foundational tool that supports downstream tasks in grasping, affordance learning, and interactive perception. This work marks a significant step toward bridging the gap between synthetic training and real-world deployment in articulated object reasoning.