Papers
10
Total Citations
118
H-Index
5
About
Dong Eui Chang is a robotics researcher whose work spans soft robotics, control theory, and intelligent autonomous systems. His most cited paper (56 citations) introduces a novel *in situ* UV curable 3D printing method for multi-material soft robots, achieving a spider-mimicked tri-legged bot with a dynamic forward gait—a significant advance in soft robot fabrication and locomotion. Chang has made substantial contributions to robot learning and control, including a GRU-Attention based TD3 network for mobile robot navigation (19 citations) and a model-free unsupervised anomaly detection system using stacked LSTMs for fixed-wing UAVs (11 citations). His work on deep reinforcement learning for robot arm manipulation (6 citations) emphasizes efficient training through selective replay buffer updates. In control theory, Chang developed pseudo-energy shaping methods for stabilizing second-order systems (5 citations), extending classical energy shaping approaches. His recent research explores multi-object tracking at low frame rates (FocoTrack), visual inertial odometry using manifold structures, and LLM-based autonomous task planning for abstract commands. With over 100 total citations, Chang’s work bridges fundamental control theory with cutting-edge machine learning, advancing both the theoretical foundations and practical capabilities of robotic systems.
Research Focus
Key Achievements
Top Papers
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- 2GRU-Attention based TD3 Network for Mobile Robot Navigation19 citations · 2022
- 3Experiments of trajectory generation and obstacle avoidance for a UGV13 citations · 2007
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