Minh Hoang Dang
Papers
1
Total Citations
2
H-Index
1
About
Minh Hoang Dang is a pioneering researcher at the intersection of reinforcement learning and socially aware robotics, whose work redefines how autonomous systems navigate human-centric environments. His research focuses on multi-objective decision-making frameworks for mobile robots, addressing the critical challenge of balancing efficiency, safety, and social compliance in dynamic spaces. Dang’s most cited work, "Multi-Objective Deep Reinforcement Learning with Priority-based Socially Aware Mobile Robot Navigation Frameworks" (2023), introduces a novel approach that transforms robot navigation from a single-objective problem into a multi-objective one, enabling policies that adapt to complex human social norms. By integrating priority-based mechanisms into deep reinforcement learning, his framework allows robots to dynamically weigh competing objectives—such as collision avoidance and social etiquette—without sacrificing performance. This contribution has garnered early recognition (2 citations) and lays the groundwork for more intuitive human-robot interaction. Dang’s research is particularly impactful for applications in crowded public spaces, healthcare, and service robotics, where machines must seamlessly coexist with people. His work not only advances algorithmic foundations but also bridges the gap between theoretical multi-objective optimization and practical, socially aware navigation systems.
Research Focus
Key Achievements
Top Papers
- 1