Yu‐Chen Chang

Papers

1

Total Citations

3

H-Index

1

About

Yu-Chen Chang is a researcher at the forefront of human-robot interaction, specializing in intuitive teleoperation systems that reduce operator cognitive load. Their key research areas include multimodal intent detection, natural language processing for robotics, and gaze-based control interfaces. Chang’s most significant contribution is a novel framework that seamlessly integrates speech commands with natural eye gaze to specify target objects for robotic arm manipulation—a breakthrough that eliminates the need for complex manual controls. This work, published in 2023, has already garnered 3 citations, signaling growing interest in their approach to making robot teleoperation more accessible and efficient. By leveraging instance segmentation on real-time camera feeds, Chang’s system enables operators to interact with robots as naturally as they would with another person. Their research holds promise for applications in assistive technology, manufacturing, and remote surgery, where reducing operator effort is critical. Chang’s innovative fusion of vision, language, and gaze tracking positions them as an emerging leader in creating more human-centric robotic interfaces.

Research Focus

Key Achievements

1
H-Index
1
Papers
3
Total Citations
3
Avg Citations/Paper
🏆 Most Cited Paper
Specifying Target Objects in Robot Teleoperation Using Speech and Natural Eye Gaze
3 citations · 2023
📈 Most Prolific Year: 2023 (1 Papers)
🤝 Key Collaborators: 5

Top Papers

  1. 1

Key Collaborators

Contact & Links

Available for collaboration
Content generated · 13 days ago