Xingpeng Sun
Papers
1
Total Citations
8
H-Index
1
About
Xingpeng Sun is a rising researcher at the forefront of human-robot interaction, with a primary focus on making autonomous navigation systems more trustworthy and robust. His work centers on integrating large language models (LLMs) with robotic platforms, particularly addressing the critical challenge of handling uncertainty in spoken instructions. In his highly cited 2024 paper, "TrustNavGPT: Modeling Uncertainty to Improve Trustworthiness of Audio-Guided LLM-Based Robot Navigation," Sun introduces a novel framework that enables robots to recognize and reason about ambiguous or uncertain commands from human speech—a fundamental step toward safer, more reliable real-world deployment. This contribution has already garnered 8 citations, signaling strong early impact in the rapidly evolving field of LLM-guided robotics. By bridging the gap between natural language understanding and physical navigation, Sun’s research directly tackles the "last mile" problem of human-robot communication: ensuring that robots can ask for clarification when needed, rather than blindly following flawed instructions. His work promises to make future robotic assistants not only more capable, but also more intuitive and trustworthy partners for everyday tasks.
Research Focus
Key Achievements
Top Papers
- 1