Yong Jun Lee
Papers
1
Total Citations
2
H-Index
1
About
Yong Jun Lee is a robotics researcher whose work focuses on the intersection of large language models (LLMs) and robotic manipulation, particularly in complex, real-world tasks. His key research areas include reinforcement learning, action-reward generation, and autonomous manipulation for service and industrial robots. Lee’s most notable contribution is the development of TARG (Tree of Action-reward Generation), a novel framework that leverages LLMs to generate hierarchical action-reward structures for robotic cabinet opening—a challenging task requiring precise force control and sequential reasoning. This work, published in 2025, has already garnered 2 citations, signaling early impact in the field. By bridging LLM-based planning with physical manipulation, Lee addresses critical gaps in robot autonomy, enabling machines to handle unstructured environments without extensive manual programming. His approach exemplifies a shift toward more intuitive, language-driven robot control, making him a promising figure in embodied AI. Lee’s research holds practical implications for assistive robotics, manufacturing, and home automation, where adaptive manipulation is essential. As his work gains traction, it is poised to influence how robots learn and execute complex tasks through natural language interaction.
Research Focus
Key Achievements
Top Papers
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