Lu Shi

University of California, Riverside

Papers

5

Total Citations

101

H-Index

5

About

Lu Shi is a leading researcher in data-driven robotics, with a focus on leveraging Koopman operator theory to model and control complex, nonlinear systems. His work bridges the gap between theoretical advances and practical applications, particularly in soft robotics and mobile robotic systems. Shi’s major contributions include the development of ACD-EDMD, a method for analytically constructing dictionaries of lifting functions—a critical step for accurate Koopman-based modeling—and the introduction of data-driven hierarchical control structures that enhance system performance under uncertainty. His research has been widely recognized, with his most-cited paper, “ACD-EDMD,” garnering 39 citations, and his comprehensive review on Koopman operators for soft robotics earning 28 citations. Shi has also advanced the robustness and online control of soft grippers and non-holonomic mobile robots, demonstrating the practical viability of data-driven approaches in real-world robotic tasks. His work is essential reading for students and researchers interested in the intersection of machine learning, control theory, and robotics, offering both foundational insights and actionable frameworks for next-generation autonomous systems.

Research Focus

Key Achievements

5
H-Index
5
Papers
101
Total Citations
20
Avg Citations/Paper
🏆 Most Cited Paper
ACD-EDMD: Analytical Construction for Dictionaries of Lifting Functions in Koopman Operator-Based Nonlinear Robotic Systems
39 citations · 2021
📈 Most Prolific Year: 2021 (2 Papers)
🤝 Key Collaborators: 5
🏛 Institutions: University of California, Riverside

Top Papers

  1. 1
  2. 2
  3. 3
  4. 4
  5. 5

Key Collaborators

Contact & Links

Available for collaboration
Content generated · 13 days ago