Papers
11
Total Citations
353
H-Index
7
About
Shih-Yuan Liu is a leading researcher in multi-robot systems, decision-making under uncertainty, and autonomous navigation. His most impactful work introduces Gaussian processes for learning and control, providing a foundational tutorial that has garnered 116 citations and shaped how robots adapt in uncertain environments. Liu has made major contributions to decentralized multi-robot planning, developing novel approaches to solve Decentralized Partially Observable Markov Decision Processes (Dec-POMDPs) using belief space macro-actions and graph-based cross-entropy methods—enabling teams of robots to coordinate effectively even with partial information. His work on SLAM with objects using nonparametric pose graphs (82 citations) advances how robots map unknown spaces by treating objects as unique, identifiable landmarks. Liu also pioneered measurable augmented reality for prototyping cyberphysical systems, bridging hardware and algorithm testing. His research spans motion planning with diffusion maps, autonomous UAV systems for sensing and tracking, and bio-inspired information gathering. With over 350 total citations across his top papers, Liu’s work is essential reading for anyone tackling real-world challenges in multi-robot coordination, adaptive control, and autonomous perception.
Research Focus
Key Achievements
Top Papers
- 1Gaussian Processes for Learning and Control: A Tutorial with Examples116 citations · 2018
- 2SLAM with objects using a nonparametric pose graph82 citations · 2016
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- 4Motion planning with diffusion maps28 citations · 2016
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- 7An Autonomous Unmanned Aerial Vehicle System for Sensing and Tracking16 citations · 2011
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- 10SLAM with Objects using a Nonparametric Pose Graph2 citations · 2017