Wenhai Liu
Papers
21
Total Citations
674
H-Index
9
About
Wenhai Liu is a robotics researcher whose work spans robotic grasping, manipulation, and human-robot interaction, with a particular focus on enabling robots to perceive and interact with the physical world as robustly and intuitively as humans. His most recognized contribution, **AnyGrasp** (2023, 210 citations), established a landmark framework for robust and efficient grasp perception across spatial and temporal domains, addressing a long-standing gap in robot manipulation capability. Complementing this, his S-curve trajectory planning work (2019, 205 citations) provided widely adopted solutions for smooth, time-optimal robot motion. Liu has made consistent advances in cluttered-scene robotic picking, developing self-supervised and domain-invariant learning approaches that reduce reliance on costly labeled data. His research extends into articulated object manipulation through **GAMMA** and generalizable robot learning via the **SAGCI-System**, reflecting a commitment to building versatile, adaptive robotic agents. More recently, his **ForceMimic** framework pioneered force-centric imitation learning for contact-rich manipulation, addressing a critical blind spot in current robot learning paradigms. With over 600 cumulative citations, Liu's body of work meaningfully advances the field toward capable, generalizable, and safe robotic systems.
Research Focus
Key Achievements
Top Papers
- 1AnyGrasp: Robust and Efficient Grasp Perception in Spatial and Temporal Domains210 citations · 2023
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