Papers
16
Total Citations
104
H-Index
7
About
Kuo-Shih Tseng is a robotics researcher whose work lies at the intersection of spatial search, human-robot interaction, and autonomous exploration. His primary contributions focus on solving NP-hard search and coverage problems by leveraging submodularity and Fourier sparsity, enabling near-optimal solutions for robotic systems. Tseng’s most cited work, “Near-optimal probabilistic search using spatial Fourier sparse set” (2017, 19 citations), established a foundational framework for efficient search in unknown environments. He has also made significant strides in understanding human performance in telerobotic search, as seen in his 2020 paper analyzing coordination patterns between gaze and control (13 citations). His research on 3D map exploration via submodular functions in the Fourier domain (2020, 11 citations) has advanced autonomous UAV navigation, allowing drones to explore complex environments efficiently. Tseng’s work has been supported by ONR and NSF grants, underscoring its practical relevance for search and rescue operations. With a total of over 90 citations across his top papers, Tseng’s integration of theoretical optimization with real-world robotic applications continues to shape how autonomous systems and humans collaborate in critical search missions.
Research Focus
Key Achievements
Top Papers
- 1Near-optimal probabilistic search using spatial Fourier sparse set19 citations · 2017
- 2
- 33D Map Exploration via Learning Submodular Functions in the Fourier Domain11 citations · 2020
- 4
- 5Spatial Search via Adaptive Submodularity and Deep Learning8 citations · 2019
- 6
- 7A Novel Telerobotic Search System using an Unmanned Aerial Vehicle7 citations · 2020
- 8
- 9
- 103D Map Exploration Using Topological Fourier Sparse Set5 citations · 2022