Irvin Haozhe Zhan
Papers
2
Total Citations
69
H-Index
2
About
Irvin Haozhe Zhan is a rising researcher in robotics and artificial intelligence, with a focus on autonomous navigation and 3D perception. His work bridges deep reinforcement learning and sensor fusion to enable intelligent robot behavior in complex environments. In his highly cited 2022 paper, Zhan proposed a novel deep reinforcement learning framework for robot collision avoidance that integrates self-state-attention mechanisms with multi-modal sensor fusion, addressing the limitations of 2D LiDAR by leveraging 3D point cloud data for more robust navigation. This work, garnering 59 citations, has significant implications for autonomous systems ranging from service robots to autonomous vehicles. Zhan also contributed to active object reconstruction through a double branch next-best-view network, introducing an efficient alternative to traditional volumetric methods by using a learning-based approach to determine optimal scanning sequences. His system, which combines this network with a novel robot platform, has been cited 10 times and demonstrates a practical pathway for robots to autonomously reconstruct 3D objects from partial observations. Through these contributions, Zhan is advancing the capabilities of intelligent robots to perceive, navigate, and interact with their surroundings more effectively.
Research Focus
Key Achievements
Top Papers
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- 2