Yuanzhou Xue
Papers
2
Total Citations
4
H-Index
2
About
Yuanzhou Xue investigates the intersection of robotics, task planning, and artificial intelligence, with a focus on enabling robots to operate robustly in open, uncertain environments. His work addresses the critical challenge of maintaining plan validity when robots face partial observability and dynamic state changes. Xue’s major contributions center on developing novel planning frameworks that integrate sensing and acting in an interleaved, adjoint manner. In his 2021 paper “Towards Adjoint Sensing and Acting Schemes and Interleaving Task Planning for Robust Robot Plan,” he proposes a paradigm where robots dynamically adjust their plans based on real-time environmental observations, thereby enhancing task success under uncertainty. Similarly, his work “Towards a Hybrid-ASP Planning Approach With Adjoint Observation for Incomplete Task-Relevant Information” introduces a hybrid Answer Set Programming (ASP) approach that allows robots to reason about and compensate for missing information during execution. While his most-cited papers have garnered 2 citations each, reflecting the emerging nature of this research area, Xue’s contributions are notable for their forward-looking approach to integrating perception and action in autonomous systems. His work lays important groundwork for more resilient robot planning in real-world applications, from service robotics to autonomous exploration.
Research Focus
Key Achievements
Top Papers
- 1
- 2