Papers
1
Total Citations
6
H-Index
1
About
Haiyue Ma is a researcher advancing the frontier of autonomous mobile robotics through deep reinforcement learning. Their primary focus lies in developing map-less, end-to-end navigation systems that enable robots to operate intelligently in dynamic, unstructured environments. Ma’s most cited work introduces a novel deep reinforcement learning model that integrates a unique long-term memory capability, leveraging recurrent neural networks to process continuous historical states as input. This breakthrough allows robots to navigate without pre-existing maps, adapting to real-time changes with enhanced decision-making. The paper, published in 2023, has already garnered 6 citations, signaling its growing influence in the field. By addressing the critical challenge of robust navigation in unpredictable settings, Ma’s contributions are paving the way for more resilient and autonomous robotic systems. Their work stands as a key reference for researchers and students exploring the intersection of reinforcement learning and robotics, offering a practical pathway toward truly self-reliant mobile agents.
Research Focus
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Top Papers
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