Baihua Li

About

Baihua Li is a researcher at the forefront of autonomous robotics, human-robot interaction, and biomedical signal processing. Her work bridges the gap between intelligent perception and practical assistive technologies. Li’s most impactful contribution is in autonomous underwater exploration, where she developed a context-enhanced anomaly detection system for curiosity-driven navigation—a paper that has garnered 23 citations and represents a novel approach to enabling robots to “discover unknowns” in unstructured environments. In the biomedical domain, she pioneered gesture recognition from surface electromyogram (sEMG) signals using hybrid deep neural networks, offering a non-invasive pathway to prosthetic control that could restore hand function for transradial amputees. Li has also advanced robotic navigation through FELC-SLAM, a feature extraction and loop closure optimized LiDAR SLAM system, and contributed a comprehensive survey on deep learning-based human action recognition from videos. Her work on human-following mobile robots further demonstrates her commitment to safe, intuitive human-robot collaboration. With a research portfolio spanning from underwater autonomy to assistive robotics, Baihua Li is shaping the next generation of intelligent, human-aware autonomous systems.

Research Focus

Key Achievements

Top Papers

1. 1
   Discovering unknowns: Context-enhanced anomaly detection for curiosity-driven autonomous underwater exploration

   23 citations · 2022
2. 2
   Gesture Recognition from Bio-signals Using Hybrid Deep Neural Networks

   9 citations · 2020
3. 3
   FELC-SLAM: feature extraction and loop closure optimized lidar SLAM system

   6 citations · 2024
4. 4
   Current Advances on Deep Learning-based Human Action Recognition from Videos: a Survey

   6 citations · 2021
5. 5
   Human Following for Mobile Robots

   4 citations · 2022

Key Collaborators

Contact & Links