Chuheng Zhang
Papers
1
Total Citations
4
H-Index
1
About
Chuheng Zhang is a rising researcher at the intersection of artificial intelligence and robotics, with a primary focus on grounding large language models (LLMs) in physical-world interactions. His most cited work, "Empowering Large Language Models on Robotic Manipulation with Affordance Prompting" (2024), addresses a critical limitation of LLMs: their inability to generate effective control sequences for real-world tasks due to a lack of physical grounding. Zhang introduces affordance prompting, a novel framework that bridges this gap by embedding actionable, object-specific cues into LLM reasoning, enabling more robust robotic manipulation. Though early in its trajectory, this work has already garnered 4 citations, signaling growing interest from the AI and robotics communities. Zhang’s contributions are particularly notable for tackling the fundamental challenge of embodied AI—how to make language models not just talk, but act in the physical world. His approach offers a scalable path toward integrating semantic knowledge with sensorimotor control, a key step for autonomous systems. As a young scholar, Zhang is poised to influence how future robots leverage language models for adaptive, real-time tasks, making his research essential reading for students and researchers exploring the convergence of NLP and robotics.
Research Focus
Key Achievements
Top Papers
- 1