Papers
2
Total Citations
34
H-Index
2
About
Xiuye Tao is a rising researcher in robotics and autonomous systems, whose work focuses on path planning and trajectory prediction in complex, uncertain environments. Their key research areas include motion planning under observation uncertainties, multi-agent coordination, and optimization-based prediction for crowded scenes. Tao’s most-cited paper, "Path Planning in Uncertain Environment With Moving Obstacles Using Warm Start Cross Entropy" (2021, 31 citations), introduces a novel framework that leverages the partially observable Markov decision process (POMDP) to navigate robots through grid-based environments with moving obstacles, significantly improving planning reliability. This work has been influential in advancing safe navigation for autonomous systems. In their more recent study, "Fast and Accurate Multi-Agent Trajectory Prediction for Crowded Unknown Scenes" (2024, 3 citations), Tao proposes an energy function optimization-based approach that enables efficient, online prediction of multiple agents’ trajectories in dense, unfamiliar settings. This contribution addresses a critical challenge in real-world robotics, such as autonomous driving and drone swarms. With a growing citation record and a focus on practical, scalable solutions, Xiuye Tao is establishing a reputation for tackling high-stakes problems in dynamic environments, making their research essential for students and engineers developing next-generation autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2