Jeongeun Park
Papers
2
Total Citations
10
H-Index
2
About
Jeongeun Park is a robotics researcher whose work focuses on the intersection of machine learning, robot perception, and human-robot interaction. Her primary research areas include kinematic structure learning, motion embedding, and teleoperation systems for manipulation tasks. Park's most notable contribution is the development of a learning framework that uses graph neural networks (GNNs) to find compact, low-dimensional representations of a robot's kinematic structure and motion embedding spaces—a key step toward understanding and generalizing complex robot behaviors. This work, published in 2021, has garnered 6 citations and lays the foundation for more efficient robot learning. More recently, Park introduced SPOTS (Stable Placement of Objects with Reasoning in Semi-Autonomous Teleoperation Systems, 2024, 4 citations), which addresses the underexplored "place" task in pick-and-place operations. By focusing on stable object placement within a teleoperation framework, Park's research enhances the reliability and autonomy of semi-autonomous systems. Her work is particularly valuable for advancing robot manipulation in real-world, unstructured environments, bridging the gap between learning and practical deployment.
Research Focus
Key Achievements
Top Papers
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- 2