Xinquan Chen

Shenzhen Institutes of Advanced Technology

Papers

Total Citations

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About

Xinquan Chen is a pioneering researcher at the intersection of robotics, cloud computing, and collaborative artificial intelligence. His primary research areas include cloud robotic systems, multi-agent path planning, and data-driven collaborative learning. Chen’s most impactful contribution is the development of “Peer-Assisted Robotic Learning,” a novel framework that addresses the critical challenge of data isolation in robotic systems. By enabling robots to share and learn from distributed datasets via the cloud, his work overcomes the labor-intensive process of building individual datasets, allowing for more efficient and scalable robotic intelligence. This landmark paper has garnered 16 citations, reflecting its significant influence on the field. Additionally, Chen has advanced cloud-based path planning for dynamic environments, tackling the complex problem of navigating robots through crowded, safety-critical spaces with moving obstacles and other agents. His research not only enhances robotic autonomy but also promotes collaborative learning, breaking down data silos to foster more robust and adaptable systems. Chen’s work is essential reading for students and researchers interested in the future of connected, intelligent robotics.