Haoge Jiang
Papers
6
Total Citations
72
H-Index
4
About
Haoge Jiang is a leading researcher in deep reinforcement learning (DRL) for mobile robot navigation, with a particular focus on dynamic and human-crowded environments. His work addresses the critical challenge of enabling robots to navigate safely and efficiently in complex, real-world settings where traditional path-planning methods fall short. Jiang’s major contributions include the development of novel DRL architectures such as iTD3-CLN and a dueling twin delayed DDPG framework, which enhance collision avoidance and decision-making in dynamic scenes. He has also pioneered the integration of graph convolutional networks (GCNs) with DRL, as demonstrated in his work on gated GCNs for crowd navigation, allowing robots to learn relational features among humans and prioritize influential neighbors. His most cited paper, "A Brief Survey: Deep Reinforcement Learning in Mobile Robot Navigation" (30 citations), provides a foundational overview of the field. Jiang’s research has accumulated over 70 citations, reflecting its growing impact on autonomous robotics. His innovative use of GCNs for feature aggregation in crowd navigation represents a notable achievement, pushing the boundaries of socially-aware robot motion planning.
Research Focus
Key Achievements
Top Papers
- 1A Brief Survey: Deep Reinforcement Learning in Mobile Robot Navigation30 citations · 2020
- 2
- 3
- 4A Dueling Twin Delayed DDPG Architecture for mobile robot navigation5 citations · 2022
- 5Imitation of human motion for humanoid robot in lift and carry event3 citations · 2019
- 6