Wenwu Wang

About

Wenwu Wang is a leading researcher at the intersection of robotics, signal processing, and human-robot interaction. His work primarily focuses on decentralized multirobot systems, acoustic scene understanding, and multimodal perception for intelligent machines. Wang’s most cited paper, “Adaptive Recursive Decentralized Cooperative Localization for Multirobot Systems With Time-Varying Measurement Accuracy” (2021, 47 citations), introduces a groundbreaking method for robots to accurately estimate their positions in dynamic environments without relying on fixed noise models—a critical advance for autonomous teams operating in GPS-denied settings. He has also made notable contributions to acoustic scene classification by modeling the cooperative relationships between scenes and events (2023, 14 citations), and to multimodal object recognition using transformers that integrate visual, haptic, and kinesthetic data (2023). Wang’s work on audio-visual speaker tracking (2017) and sound-based touch gesture and emotion recognition for human-robot interaction (2025) further demonstrates his commitment to creating more intuitive, context-aware robots. His research is widely cited for its practical impact on autonomous navigation, social robotics, and assistive technologies, making him a key figure in advancing how machines perceive and interact with the world.

Research Focus

Key Achievements

Top Papers

1. 1
   Adaptive Recursive Decentralized Cooperative Localization for Multirobot Systems With Time-Varying Measurement Accuracy

   47 citations · 2021
2. 2
   Cooperative Scene-Event Modelling for Acoustic Scene Classification

   14 citations · 2023
3. 3
   Visual-Haptic-Kinesthetic Object Recognition with Multimodal Transformer

   5 citations · 2023
4. 4
   Audio‐Visual Speaker Tracking

   3 citations · 2017
5. 5
   Sound-Based Recognition of Touch Gestures and Emotions for Enhanced Human-Robot Interaction

   2 citations · 2025

Key Collaborators

Contact & Links