Haowen Liu
Papers
1
Total Citations
14
H-Index
1
About
Haowen Liu is a rising researcher in the field of autonomous underwater robotics, with a focus on perception-driven navigation in challenging, unstructured environments. Their work centers on enabling safe, collision-free movement for underwater vehicles using minimal, low-cost sensor suites—specifically monocular cameras and single-beam sonar. In their most-cited paper (2023), Liu introduced an innovative framework that leverages domain randomization to bridge the sim-to-real gap, allowing neural networks trained in simulation to robustly generalize to real-world underwater conditions. This approach has garnered 14 citations in just a short time, signaling its relevance to the growing demand for reliable, cost-effective autonomous underwater systems. Liu’s contributions are particularly notable for addressing the dual challenges of sparse sensor data and unpredictable underwater dynamics, offering a practical path toward scalable marine robotics. Their work stands out for its pragmatic integration of simulation-based learning with real-world deployment, making it a valuable reference for researchers developing navigation systems for environmental monitoring, underwater inspection, and autonomous exploration.
Research Focus
Key Achievements
Top Papers
- 1