Yiting Mao

Papers

Total Citations

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About

Yiting Mao is a rising researcher in artificial intelligence and robotics, with a primary focus on intelligent target recognition and reinforcement learning. Their most cited work, “Research and Design on Intelligent Recognition of Unordered Targets for Robots Based on Reinforcement Learning” (2025), addresses a critical challenge in AI-driven robotics: accurately identifying targets in cluttered, noisy environments where objects are randomly distributed. Mao’s research tackles key obstacles such as environmental complexity, massive data scales, and interference, proposing innovative reinforcement learning frameworks that enable robots to autonomously adapt and improve recognition accuracy. This work has already garnered 8 citations, signaling its early impact on the field. By advancing how machines perceive and interact with disordered real-world settings, Mao’s contributions hold promise for applications in automated manufacturing, logistics, and search-and-rescue operations. Their research bridges the gap between theoretical reinforcement learning algorithms and practical robotic systems, offering scalable solutions for dynamic, unpredictable environments. As a forward-thinking scholar, Yiting Mao is helping to shape the next generation of intelligent, adaptive robots capable of operating beyond structured, controlled settings.