Papers
22
Total Citations
189
H-Index
6
About
Nanfeng Xiao is a researcher whose work spans robotics, human-computer interaction, and deep learning, with particular expertise in intelligent robot systems and neural network applications. Over more than two decades, Xiao has made meaningful contributions to fields ranging from keyword spotting and facial expression recognition to robotic grasping, reinforcement learning, and sensor motion planning. His most impactful work, "Effective Combination of DenseNet and BiLSTM for Keyword Spotting" (2019, 83 citations), demonstrates his ability to bridge advanced deep learning architectures with practical, resource-constrained applications in smart devices and service robots. His 2018 deep learning-based grasp detection method for a five-fingered industrial robot hand (25 citations) further highlights his talent for translating complex neural network models into real-world robotic systems. Xiao's earlier research established a strong foundation in neural network-based robot control, multi-agent assembly systems, and reinforcement learning for unknown environments, reflecting a career-long commitment to making robots more adaptive and intelligent. His work on gesture recognition and humanoid robot navigation further underscores his sustained focus on seamless human-robot interaction. Collectively, his publications reveal a researcher dedicated to advancing intelligent, autonomous robotic systems capable of operating effectively in dynamic, real-world settings.
Research Focus
Key Achievements
Top Papers
- 1Effective Combination of DenseNet and BiLSTM for Keyword Spotting83 citations · 2019
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- 5Plane extraction for navigation of humanoid robot6 citations · 2011
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- 8Multi-agent model for robotic assembly system5 citations · 2003
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- 10Neural Network-Based Learning Impedance Control for a Robot.4 citations · 2001