Papers

14

Total Citations

142

H-Index

6

About

Lawrence Yunliang Chen is a robotics researcher whose work lies at the intersection of deformable object manipulation, robot learning, and surgical assistance. His most significant contributions center on enabling robots to handle complex, non-rigid materials—from cables and plastic bags to garments and surgical shunts. Chen pioneered the task of Planar Robot Casting (PRC), where a robot arm uses a single planar motion to slide a cable’s free end to a target, effectively extending the robot’s reach beyond its workspace. His Real2Sim2Real framework for self-supervised learning of this skill has garnered 39 citations. He also introduced AutoBag, a system that learns to open thin, specular plastic bags and insert objects—a notoriously difficult perception and manipulation challenge—earning 29 citations. Beyond these, Chen has advanced garment smoothing via single-arm fling motions, in-context imitation learning with the In-Context Robot Transformer (ICRT), and robot-assisted vascular shunt insertion using the da Vinci Research Kit, addressing scenarios where a supervising surgeon is local, remote, or unavailable. His work on Fleet-DAgger further tackles scalable human supervision for robot fleets. With over 130 total citations across his top papers, Chen is shaping how robots learn to handle the physical world’s most challenging materials.

Research Focus

Key Achievements

6
H-Index
14
Papers
142
Total Citations
10
Avg Citations/Paper
🏆 Most Cited Paper
Real2Sim2Real: Self-Supervised Learning of Physical Single-Step Dynamic Actions for Planar Robot Casting
39 citations · 2022
📈 Most Prolific Year: 2023 (6 Papers)
🤝 Key Collaborators: 137
🏛 Institutions: University of California, Berkeley, Berkeley Systems (United States), Berkeley College

Top Papers

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Key Collaborators

Contact & Links

Available for collaboration
Content generated · 15 days ago