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About

Jirong Liu is a leading robotics researcher whose work focuses on enabling robots to achieve human-like dexterity and adaptability in grasping and manipulation. His key research areas include robust grasp perception, reactive grasping for dynamic objects, and the development of generalist robot policies through vision-language-action models. Liu’s most significant contribution is **AnyGrasp**, a groundbreaking framework for grasp perception that operates robustly and efficiently across both spatial and temporal domains, aiming to replicate the prompt, accurate, and continuous grasping abilities of humans. This work has garnered over 210 citations, underscoring its impact on the field. He also co-created **RH20T**, a comprehensive robotic dataset with 55 citations that facilitates one-shot imitation learning and diverse skill acquisition, advancing the goal of generalizable robotic manipulation. Additionally, Liu has pioneered target-referenced reactive grasping for dynamic objects, ensuring semantic consistency in real-time interactions. His recent exploration into generalist robot policies further highlights his commitment to building scalable, versatile robotic systems. Through these achievements, Liu is shaping the future of autonomous manipulation, making robots more capable in open, unstructured environments.