Chengzhong Xu

About

Chengzhong Xu is a prominent researcher whose work sits at the dynamic intersection of cloud robotics, federated learning, autonomous navigation, and cloud computing. His contributions have fundamentally advanced how robots learn, collaborate, and navigate in complex real-world environments. Xu's most influential work introduces federated imitation learning for cloud robotic systems with heterogeneous sensor data (87 citations), enabling robots to acquire new behaviors through observation while preserving data privacy across distributed platforms. This theme of collaborative intelligence continues through FC-SLAM (26 citations) and peer-assisted robotic learning (16 citations), where he pioneered frameworks allowing multiple robots to share knowledge without centralizing sensitive data. Beyond robotics, Xu has made significant contributions to cloud computing through CoScal (62 citations), addressing the performance challenges of microservice scaling using reinforcement learning. His exploration algorithms — including frontier detection with graph-SLAM (36 citations) and the adaptive Ada-Detector (13 citations) — have pushed forward autonomous robot mapping capabilities. His recent NeuPAN framework (12 citations) demonstrates continued innovation in end-to-end robot navigation using model-based deep learning. With research spanning warehouse logistics simulation, edge computing, and collaborative motion planning, Xu's body of work reflects a coherent vision: intelligent, scalable, and cooperative robotic systems capable of operating efficiently in the real world.

Research Focus

Key Achievements

Top Papers

1. 1
   Federated Imitation Learning: A Novel Framework for Cloud Robotic Systems With Heterogeneous Sensor Data

   87 citations · 2020
2. 2
   CoScal: Multifaceted Scaling of Microservices With Reinforcement Learning

   62 citations · 2022
3. 3
   Frontier Detection and Reachability Analysis for Efficient 2D Graph-SLAM Based Active Exploration

   36 citations · 2020
4. 4
   FC-SLAM: Federated Learning Enhanced Distributed Visual-LiDAR SLAM In Cloud Robotic System

   26 citations · 2019
5. 5
   A novel swarm robot simulation platform for warehousing logistics

   19 citations · 2017
6. 6
   Peer-Assisted Robotic Learning: A Data-Driven Collaborative Learning Approach for Cloud Robotic Systems

   16 citations · 2021
7. 7
   Ada-Detector: Adaptive Frontier Detector for Rapid Exploration

   13 citations · 2022
8. 8
   NeuPAN: Direct Point Robot Navigation With End-to-End Model-Based Learning

   12 citations · 2025
9. 9
   Edge Accelerated Robot Navigation With Collaborative Motion Planning

   9 citations · 2024
10. 10
    Concave-Hull Induced Graph-Gain for Fast and Robust Robotic Exploration

    8 citations · 2023

Key Collaborators

Contact & Links