Hsueh‐Cheng Wang
Papers
13
Total Citations
279
H-Index
8
About
Hsueh-Cheng Wang is a robotics researcher whose work sits at the intersection of autonomous navigation, assistive technology, and human-robot interaction. He is best known for pioneering deep learning and reinforcement learning approaches that enable robots to serve as intelligent guides for people who are blind and visually impaired (BVI). His 2018 paper on deep trail-following robotic guide dogs, which has garnered 73 citations, demonstrated how virtual-to-real world transfer learning could dramatically improve pedestrian navigation for BVI individuals — a landmark contribution to assistive robotics. Building on this foundation, Wang developed systems incorporating UWB beacons, semantic feedback, and curriculum reinforcement learning to create increasingly robust navigation agents, with subsequent papers attracting 46 and 37 citations respectively. Beyond assistive technology, Wang has made significant contributions to search and rescue robotics, introducing collision-tolerant blimp platforms and heterogeneous robot teams capable of operating in challenging subterranean environments. His research also spans teleoperation via virtual reality, federated learning for robotic grasping, and retail automation. Across these diverse domains, Wang consistently bridges the gap between algorithmic innovation and real-world deployment, making his work particularly valuable for researchers seeking practical, deployable robotic solutions. His cumulative citation record reflects both the breadth and societal relevance of his contributions to modern robotics.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4
- 5
- 6
- 7
- 8Fed-HANet: Federated Visual Grasping Learning for Human Robot Handovers11 citations · 2023
- 9
- 10