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About

Yuyang Li is an emerging robotics researcher whose work sits at the intersection of dexterous manipulation, tactile sensing, and generalizable robot learning. His research addresses some of the most persistent challenges in robotic grasping and manipulation, with a particular focus on enabling robots to interact with the physical world with human-like dexterity and adaptability. Li's most influential contribution, **GenDexGrasp** (2023, 59 citations), tackled the critical problem of generalizing dexterous grasping across different robot hand morphologies — a capability most prior systems lacked entirely. This work established him as a serious voice in cross-embodiment robotic learning. He subsequently extended this vision through research on multi-object grasping, bimanual manipulation transfer, and affordance-based pre-grasping strategies, collectively pushing the frontier of what robotic hands can achieve in unstructured environments. Notably, his 2025 work embedding high-resolution touch sensing across robotic hands (36 citations despite its recency) signals a growing commitment to tactile-informed intelligence. His tactile manipulation work (**Tac-Man**, 17 citations) further demonstrates how touch feedback can replace reliance on prior object knowledge during complex articulated-object interactions. With contributions spanning simulation platforms (**RoboVerse**), agent-agnostic learning (**Ag2Manip**), and scalable data generation, Li represents a researcher systematically building the infrastructure for truly generalizable robotic manipulation.