Jingjin Yu
University of Illinois System, Rutgers, The State University of New Jersey, University of Illinois Urbana-Champaign, Massachusetts Institute of Technology, Tel Aviv University, Boston University, Rutgers Sexual and Reproductive Health and Rights, Mitsubishi Electric (United States), Corvallis Environmental Center
Papers
86
Total Citations
2,435
H-Index
22
About
Jingjin Yu is a robotics and algorithms researcher whose work sits at the intersection of computational complexity, multi-robot systems, and robot manipulation. He is best known for his foundational contributions to multi-robot path planning (MPP), where he has rigorously characterized the computational hardness of optimally coordinating multiple robots on graphs. His landmark 2013 paper establishing the NP-hardness of key MPP objectives has garnered over 400 citations, while his subsequent work extending these intractability results to planar graphs and developing complete, efficient algorithms has collectively shaped how the field approaches large-scale robot coordination. Beyond theoretical analysis, Yu has built practical, high-performance planners — such as the DDM algorithm for warehouse-like settings — bridging complexity theory and real-world deployment. His research further extends into persistent monitoring, where he has modeled stochastic event coverage and introduced the correlated orienteering problem for informative mission planning. His work on object rearrangement and deep learning-based interaction prediction demonstrates versatility across manipulation and planning domains. With over 1,300 combined citations across his most influential papers, Yu has established himself as a leading voice in algorithmic robotics, consistently producing work that is both theoretically rigorous and practically motivated.
Research Focus
Key Achievements
Top Papers
- 1Structure and Intractability of Optimal Multi-Robot Path Planning on Graphs414 citations · 2013
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- 3Planning optimal paths for multiple robots on graphs192 citations · 2013
- 4Intractability of Optimal Multirobot Path Planning on Planar Graphs93 citations · 2015
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- 9Motion Planning for Unlabeled Discs with Optimality Guarantees62 citations · 2015
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