Zhanbo Feng

Papers

Total Citations

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About

Zhanbo Feng’s research lies at the intersection of multi-agent systems, reinforcement learning, and robotics, with a focus on enabling intelligent coordination among autonomous agents. His most-cited work, “Coordinated Multiagent Reinforcement Learning for Teams of Mobile Sensing Robots” (2019, 4 citations), introduces a novel framework that leverages coordination graphs to model inter-robot interactions, allowing teams of mobile sensing robots to learn cooperative behaviors in dynamic environments. This contribution addresses a critical challenge in multi-agent systems—scalable and efficient coordination—and has been recognized as a foundational approach for real-world robotic deployments. In parallel, Feng’s work on “Color Recognition for Rubik’s Cube Robot” (2019, 3 citations) demonstrates his versatility, proposing both offline and online methods—including the innovative Scatter Balance & Extreme Learning Machine (SB-ELM)—to solve color recognition tasks with high efficiency. This work bridges machine learning and practical robotics, showcasing his ability to translate theoretical advances into tangible applications. With a growing citation footprint, Feng’s research continues to influence the fields of multi-agent reinforcement learning and robotic perception, making him a promising voice in autonomous systems engineering.