Simge Nur Aslan
Papers
8
Total Citations
56
H-Index
4
About
Simge Nur Aslan is a robotics and artificial intelligence researcher whose work focuses on enabling humanoid robots to perceive, navigate, and interact with their environments through deep learning. Her research centers on object recognition, semantic segmentation, and learning from demonstration, with the goal of making humanoid robots capable assistants in homes, hospitals, hotels, and industrial settings. Aslan’s most impactful contribution is a novel CNN and hybrid CNN-LSTM model for learning object manipulation from demonstration (26 citations), which allows robots to acquire manipulation skills by observing human actions. She has also developed fast, lightweight deep learning architectures for real-time object recognition on resource-constrained humanoid platforms (8 citations) and created an algorithm that combines semantic segmentation with Deep Q-Networks to enable robots to walk to targets while avoiding obstacles (6 citations). Her work on deep wavelet pyramid networks for indoor scene perception (4 citations) further advances robots’ ability to understand complex environments. Aslan’s research is notable for its practical focus on end-to-end learning pipelines that integrate perception, planning, and control, directly addressing the challenge of deploying humanoid robots in unstructured, real-world settings.
Research Focus
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Top Papers
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