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About

Yuyang Tu is a robotics researcher whose work focuses on the intersection of robotic manipulation, perception, and agricultural automation. Their key research areas include object-in-hand pose estimation, tool affordance recognition, and damage-less grasping for fragile objects. Tu’s most notable contribution is **PoseFusion**, a framework that uses SelectLSTM to robustly estimate the relative pose between an object and a robot hand—a critical capability for dexterous manipulation tasks. This work, published in 2023, has already garnered 12 citations, reflecting its relevance in addressing the challenge of occlusion in hand-object interactions. Tu also authored **ToolEENet**, which tackles 6D pose estimation for tools grasped by dexterous hands, and a comprehensive systematic review on damage-less robotic grasping of fragile fruit, published in 2025. Additionally, Tu has explored magnet-actuated tethered capsule robots, learning friction models to improve position control for diagnostic applications. With a growing citation record and a focus on practical, real-world challenges—from agricultural harvesting to medical robotics—Yuyang Tu is establishing a reputation for advancing robot perception and manipulation in complex, contact-rich environments.