Pengteng Li
Papers
2
Total Citations
14
H-Index
2
About
Pengteng Li is a robotics researcher whose work sits at the intersection of human-robot interaction, motion prediction, and language-driven autonomy. Li’s most influential contribution, “Robot Trajectron: Trajectory Prediction-based Shared Control for Robot Manipulation” (2024, 12 citations), pioneers a novel framework that predicts a user’s arm-reaching trajectory from just the motion’s onset, then uses that prediction to provide real-time shared control assistance. This reduces operator cognitive load and marks a significant advance in intuitive, assistive manipulation. In parallel, Li’s “A Language-Driven Navigation Strategy Integrating Semantic Maps and Large Language Models” (2024, 2 citations) addresses a core challenge in embodied AI: enabling robots to accurately perceive and map semantic-spatial information for natural language-driven navigation. By integrating visual-language models with LLMs, this work pushes toward more generalizable and context-aware robotic guidance. Together, these contributions demonstrate Li’s focus on making robots more responsive and intelligent partners—whether through anticipating human intent or understanding natural language commands. With a clear trajectory toward smarter, more collaborative systems, Li is a rising voice in modern robotics.
Research Focus
Key Achievements
Top Papers
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