Hyung Jin Chang
Imperial College London, University of Birmingham, Seoul National University
Papers
14
Total Citations
327
H-Index
10
About
Hyung Jin Chang is a leading researcher in assistive robotics and human-robot interaction, with a focus on developing intelligent systems that can perceive, learn from, and adapt to human users. His core contributions span personalised robotic assistance, visuomotor learning, and cognitive architectures for proactive robots. Chang is best known for his pioneering work on robotic dressing assistance, where he developed iterative path optimisation methods that integrate vision and force sensing to personalise care for disabled or elderly users—his 2016 paper on this topic has garnered 80 citations. He also introduced the DAC-h3 cognitive architecture (74 citations), enabling humanoid robots to proactively acquire and express knowledge through mixed-initiative exploration. His research extends to transferring visuomotor learning from simulation to real-world robotics (20 citations) and user modelling using multimodal data (68 citations). More recently, Chang has tackled challenges in 6D object pose tracking and clothes manipulation using RGB-D semantic segmentation. His work on egocentric hand-object pose estimation benchmarks further underscores his impact. With over 300 total citations, Chang’s research is shaping the future of assistive robots that are not only functional but also personalised and socially aware.
Research Focus
Key Achievements
Top Papers
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- 3User modelling for personalised dressing assistance by humanoid robots68 citations · 2015
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- 6Learning Kinematic Structure Correspondences Using Multi-Order Similarities13 citations · 2017
- 7TP-AE: Temporally Primed 6D Object Pose Tracking with Auto-Encoders12 citations · 2022
- 8Emotional head robot with behavior decision model and face recognition11 citations · 2007
- 9Clothes Grasping and Unfolding Based on RGB-D Semantic Segmentation11 citations · 2023
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