Kuozhan Wang

Carleton University, Zhongyuan University of Technology

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About

Kuozhan Wang is a robotics researcher whose work focuses on advancing human-robot interaction, autonomous navigation, and multi-modal control systems. His research spans speech-driven robot control, semantic environmental understanding, and intelligent grasping—core areas that bridge the gap between raw sensor data and meaningful robotic behavior. Wang’s most influential work, “Voice based robot control” (2006, 26 citations), established a foundational approach for using hidden Markov models to recognize oral commands and translate them into mobile robot actions, a system that has informed subsequent voice-controlled robotics research. More recently, he has explored the integration of RGB-D cameras, voice systems, and adaptive control on the Pepper robot platform (2024), targeting flexible, real-world applications in homes and public spaces. His latest contribution (2025) introduces an adaptive-optimized semantic point cloud mapping framework using YOLO11-seg, enabling robots to move beyond geometric SLAM toward richer, semantically-aware navigation and path planning. By combining speech recognition, semantic mapping, and multi-modal interaction, Wang’s work continues to push the boundaries of how robots perceive, understand, and respond to complex human environments.