Yichuan Li
Papers
8
Total Citations
176
H-Index
6
About
Yichuan Li is a robotics researcher whose work sits at the intersection of soft robotics, computer vision, and industrial automation. His primary research areas include bio-inspired soft robots with multimodal sensing, sim-to-real transfer learning for robotic manipulation, and reinforcement learning for logistics automation. Li’s most impactful contribution is the development of a modular origami soft robot capable of perceiving both interaction forces and its own body configuration—a breakthrough for human-centered robotic applications (60 citations). He also pioneered S2R-Pick, a robust sim-to-real framework for industrial robotic bin picking that enables fast and accurate object recognition and localization (52 citations), and advanced this line of work with an iterative self-training approach for 6D object pose estimation. His recent work on efficient reinforcement learning for robotic palletization through iterative action masking learning (2024) addresses critical efficiency demands in supply chain management. With over 170 total citations across his publications, Li has established himself as a rising figure in robotic manipulation, consistently bridging the gap between simulation and real-world deployment for industrial applications.
Research Focus
Key Achievements
Top Papers
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- 6Uncertainty-Aware Suction Grasping for Cluttered Scenes7 citations · 2024
- 7DBPF: A Framework for Efficient and Robust Dynamic Bin-Picking5 citations · 2024
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