Sanghun Cheong
Papers
1
Total Citations
7
H-Index
1
About
Sanghun Cheong is a robotics researcher whose work focuses on intelligent manipulation in cluttered environments, particularly through the integration of deep reinforcement learning and motion planning. His most cited paper, "Obstacle rearrangement for robotic manipulation in clutter using a deep Q-network" (2021, 7 citations), introduces a novel approach that enables robots to autonomously reorganize obstacles to access target objects—a critical challenge in real-world settings like warehouses or homes. This work demonstrates how deep Q-networks can learn effective rearrangement policies, bridging the gap between perception and action in complex, unstructured spaces. Cheong’s contributions are significant for advancing robotic autonomy, offering practical solutions for tasks requiring spatial reasoning and adaptive decision-making. By combining algorithmic innovation with real-world applicability, his research has implications for logistics, assistive robotics, and manufacturing. With a growing citation impact, Cheong is establishing himself as a promising voice in the field of robotic manipulation, where his work continues to inspire further exploration into learning-based methods for physical interaction.
Research Focus
Key Achievements
Top Papers
- 1