Shibao Yang

Papers

Total Citations

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About

Shibao Yang is a robotics researcher whose work bridges motion planning, learning from demonstration, and underwater autonomous systems. His most cited paper, "Benchmarking of Robot Arm Motion Planning in Cluttered Environments" (2023, 9 citations), tackles a critical gap in robotics: the lack of generalizable benchmarks across different robotic platforms. By systematically evaluating three popular arm systems, Yang provides a standardized framework that enables researchers to compare and transfer motion planning algorithms, accelerating real-world deployment in manufacturing and logistics. In "Enhancing Learning from Demonstration with DLS-IK and ProMPs" (2024), he addresses the practical challenges of transferring human demonstrations to robots—specifically avoiding singularities and respecting joint limits—advancing the field of intuitive robot programming. Yang also contributes to environmental applications through his survey on IoT-based underwater robotics for aquaculture water quality monitoring (2024), demonstrating the breadth of his impact. His work is foundational for students and researchers seeking to make robotic systems more adaptable, reliable, and applicable across industries, from factory floors to underwater ecosystems.