Linh Pham
Papers
1
Total Citations
2
H-Index
1
About
Linh Pham is a researcher focused on the intersection of computer vision and reinforcement learning, with a particular emphasis on memory-driven navigation for autonomous systems. Their most-cited work, "Vision Memory for Target Object Navigation Using Deep Reinforcement Learning: An Empirical Study" (2018), explores how deep neural networks can be integrated with reinforcement learning to enable agents to navigate complex environments by remembering and locating target objects. This study highlights Pham’s contribution to addressing the challenge of high-dimensional sensory data in robotic navigation, leveraging neural networks for feature extraction and reinforcement learning for experiential decision-making. While early in their career, with this paper accruing 2 citations, Pham’s work lays a foundational empirical framework for combining memory mechanisms with deep RL—a growing area in embodied AI. Their research is particularly relevant for students and researchers interested in how agents can learn to generalize navigation tasks from visual input, bridging gaps between perception, memory, and action. Pham’s approach offers practical insights for developing more intelligent, adaptive robots capable of operating in unstructured real-world settings.
Research Focus
Key Achievements
Top Papers
- 1