Juo-Tung Chen
Papers
4
Total Citations
101
H-Index
3
About
Juo-Tung Chen is a pioneering researcher at the intersection of artificial intelligence, robotics, and human-robot interaction, with a primary focus on advancing autonomous surgical systems and democratizing robot programming. His most impactful work introduces SRT-H, a hierarchical framework that enables autonomous surgery through language-conditioned imitation learning—a breakthrough addressing the critical challenges of dexterous manipulation and generalization to variable human tissue. This landmark paper has already garnered 70 citations since its 2025 publication, signaling its transformative potential in surgical robotics. Chen has also made significant contributions to end-user robot programming, notably through Alchemist, an LLM-aided system that shifts programming from manual logic specification to collaborative outcome-driven development (23 citations). His empirical study on LLM-generated code errors in robotics, "Forgetful Large Language Models," provides crucial insights into the limitations of current AI systems, while his targeted training approach for remote teleoperation tackles performance variability across users with different spatial abilities. Chen’s work collectively pushes toward more accessible, reliable, and intelligent robotic systems, bridging the gap between cutting-edge AI and practical, real-world applications.
Research Focus
Key Achievements
Top Papers
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- 2Alchemist: LLM-Aided End-User Development of Robot Applications23 citations · 2024
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