Xusheng Luo
Papers
10
Total Citations
169
H-Index
6
About
Xusheng Luo is a leading researcher in multi-robot systems, formal methods, and human-robot interaction, with a focus on making complex temporal logic planning both scalable and accessible. His most impactful contribution is the development of abstraction-free methods for multi-robot optimal control synthesis under Linear Temporal Logic (LTL) specifications, a breakthrough that eliminates the computational bottleneck of discrete product automata construction—his 2021 paper on this topic has garnered 64 citations. Luo also pioneered hierarchical decomposition approaches for task allocation in heterogeneous multi-robot teams, enabling scalable solutions for complex, multi-type task specifications (56 citations). His work extends into socially-aware robotics, where he uses bandit feedback to learn human comfort preferences for collision-free navigation (12 citations), and into formal verification of neural network-controlled stochastic systems. Notably, his recent research bridges natural language understanding and formal task specification, allowing non-experts to command multi-robot teams through intuitive language. With over 170 total citations and a rapidly growing portfolio, Luo’s work is shaping the future of safe, scalable, and human-friendly autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2Temporal Logic Task Allocation in Heterogeneous Multirobot Systems56 citations · 2022
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- 4Socially-Aware Robot Planning via Bandit Human Feedback12 citations · 2020
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- 6
- 7Human-in-the-Loop Robot Planning with Non-Contextual Bandit Feedback5 citations · 2021
- 8Temporal Logic Task Allocation in Heterogeneous Multi-Robot Systems4 citations · 2021
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