Papers
9
Total Citations
247
H-Index
8
About
Jaeseok Kim is a leading researcher in service robotics, with a primary focus on developing autonomous systems for domestic assistance, particularly for the growing elderly population. His work centers on three key areas: human-robot interaction, learning from demonstration, and deep learning-based control for service robots. Kim's most significant contributions include comprehensive reviews of control strategies for cleaning robots (74 citations) and innovative approaches to gesture recognition during human-robot interaction using combined vision and wearable systems (33 citations). He has pioneered methods for teaching robots complex tasks through kinesthetic demonstrations, enabling autonomous table-cleaning and object manipulation using deep neural networks. His work on image classification using multiple convolutional neural networks on the Fashion-MNIST dataset (49 citations) demonstrates his expertise in computer vision. Notably, Kim has explored knowledge transfer between heterogeneous robots, tactile-based object classification with data augmentation, and cloth manipulation for dressing assistance. His research consistently addresses real-world challenges in service robotics, from recycling automation to daily gesture recognition, with over 200 total citations across his publications.
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