Belinda Teh

Papers

Total Citations

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About

Belinda Teh’s research lies at the intersection of cognitive robotics, machine learning, and multi-agent systems, with a particular focus on the RoboCup Standard Platform League. Her work emphasizes the value of competitive robotics as a driver for integrated, systems-level innovation—an approach that builds complete, functional robotic platforms rather than isolated solutions. As a key contributor to the rUNSWift team, Teh helped advance vision systems, state estimation, locomotion, and layered hybrid architectures for autonomous humanoid robots. Her most cited paper, “Robocup Standard Platform League - rUNSWift 2012 Innovations” (4 citations), documents how large-scale robotic systems are incrementally constructed through competition, offering a developmental methodology that contrasts with traditional isolated problem-solving. A second notable publication, “Standard Platform League” (2 citations), highlights the team’s use of high-level programming languages and machine learning to push the boundaries of real-time robotic cognition. Though her citation counts are modest, Teh’s work represents a foundational contribution to the RoboCup community, demonstrating how undergraduate and master’s students can drive meaningful innovation in embodied AI and autonomous systems.