Gyeong-Moon Park
Papers
1
Total Citations
4
H-Index
1
About
Gyeong-Moon Park is a researcher advancing the frontiers of episodic memory and continual learning for intelligent robotic systems. His work focuses on enabling robots to retrieve sequences of events from past experiences, allowing them to provide personalized, context-aware services. Park’s most cited paper, “SR-EM: Episodic Memory Aware of Semantic Relations Based on Hierarchical Clustering Resonance Network” (2021), introduces a novel architecture that integrates semantic relations into episodic memory, enabling robots to learn new tasks incrementally without catastrophic forgetting. This contribution addresses a critical challenge in lifelong learning for autonomous agents. With 4 citations, this work has laid a foundation for more adaptive and memory-efficient AI systems. Park’s research sits at the intersection of cognitive robotics, continual learning, and hierarchical clustering, offering practical pathways for robots to build and recall structured experiences. His achievements highlight a commitment to bridging human-like memory mechanisms with machine learning, making his work essential reading for students and researchers interested in building robots that truly learn from and adapt to their environments.
Research Focus
Key Achievements
Top Papers
- 1