Tae‐Seong Kim
Papers
5
Total Citations
86
H-Index
4
About
Tae-Seong Kim is a leading researcher at the intersection of robotics and artificial intelligence, specializing in wearable exoskeletons, anthropomorphic robotic hands, and deep reinforcement learning (DRL). His work focuses on enabling robots to perform complex, human-like manipulations—from real-time activity recognition in assistive exoskeletons to bimanual long-horizon tasks. Kim’s most cited paper, “Real-Time Human Activity Recognition with IMU and Encoder Sensors in Wearable Exoskeleton Robot via Deep Learning Networks” (2022, 52 citations), demonstrates how deep learning can decode human intent to enhance robotic control assistance for daily tasks. He has also pioneered dexterous object manipulation using natural hand pose transformers and DRL, achieving autonomous grasping and manipulation with anthropomorphic soft robot hands. His recent work on bimanual manipulation via Temporal-Context Transformer RL (2024) tackles the challenge of long-sequence, multi-agent coordination, pushing the boundaries of robot intelligence. With contributions spanning healthcare, smart homes, and smart factories, Kim’s research is pivotal for developing robots that seamlessly collaborate with humans, making him a key figure in advancing embodied AI and assistive robotics.
Research Focus
Key Achievements
Top Papers
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- 3Bimanual Long-Horizon Manipulation Via Temporal-Context Transformer RL8 citations · 2024
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