Hsuan–Ming Feng
Papers
7
Total Citations
73
H-Index
4
About
Hsuan–Ming Feng is a robotics researcher whose work centers on autonomous navigation, human-robot interaction, and intelligent manipulation for real-world service robots. His major contributions lie in integrating deep reinforcement learning with multi-sensor fusion for simultaneous localization and mapping (SLAM), as demonstrated in his highly cited paper on multi-sensor fusion SLAM (19 citations), which combines LiDAR and RGB-D data with adaptive estimation. He also pioneered voice interaction recognition for mobile robots (35 citations), designing deep neural network-based systems that allow users to control service robots through natural spoken commands in real-life scenarios. Feng’s research extends to multi-agent path-finding using hybrid centralized training and decentralized execution reinforcement learning, and to object pick-and-place systems that leverage rapidly-exploring random trees for collision-free manipulation. His work on 3D LiDAR SLAM for object detection and navigation further showcases his commitment to practical, deployable robotics. With a growing citation record and a focus on cyber-physical systems, Feng is advancing the frontier of autonomous service robots that can perceive, navigate, and interact seamlessly in human environments.
Research Focus
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Top Papers
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