Jiuhong Xiao

About

Jiuhong Xiao is a leading researcher at the intersection of multi-robot systems, computer vision, and deep learning, with a focus on enabling intelligent, collaborative autonomy. Their work is defined by tackling the core challenges of perception and coordination in distributed robotic teams. Xiao’s most impactful contribution is the development of "Multi-Robot Collaborative Perception With Graph Neural Networks" (2022, 98 citations), which provides a foundational framework for aerial robot swarms to share and process environmental data, dramatically enhancing situational awareness and decision-making. This work is complemented by pioneering research into bio-inspired systems, such as "Toward Coordination Control of Multiple Fish-Like Robots" (2021, 20 citations), where Xiao addressed the complex problem of real-time, multi-joint pose estimation and tracking using deep neural networks. More recently, Xiao has advanced the field of visual geo-localization with the "VG-SSL" benchmark (2023), a critical resource for developing self-supervised learning methods that allow autonomous vehicles and robots to identify their location from visual inputs alone. Through these contributions, Xiao is shaping the future of robust, scalable, and perceptive multi-agent systems.

Research Focus

Key Achievements

Top Papers

1. 1
   Multi-Robot Collaborative Perception With Graph Neural Networks

   98 citations · 2022
2. 2
   Toward Coordination Control of Multiple Fish-Like Robots: Real-Time Vision-Based Pose Estimation and Tracking via Deep Neural Networks

   20 citations · 2021
3. 3
   VG-SSL: Benchmarking Self-supervised Representation Learning Approaches for Visual Geo-localization

   2 citations · 2023

Key Collaborators

Contact & Links