Aaron Hao Tan
Papers
11
Total Citations
202
H-Index
6
About
Aaron Hao Tan is a robotics researcher specializing in autonomous mobile robot navigation, deep reinforcement learning, and human-robot interaction. His work sits at the intersection of simulation-to-real transfer, multi-robot systems, and vision-language models, addressing some of the most pressing challenges in deploying intelligent robots in complex, real-world environments. Tan's most influential contribution, "A Sim-to-Real Pipeline for Deep Reinforcement Learning for Autonomous Robot Navigation in Cluttered Rough Terrain" (2021, 84 citations), demonstrated how robots could be trained in simulation and successfully transferred to navigate demanding physical terrains — a critical bottleneck in practical robotics deployment. His follow-up work on decentralized multi-robot exploration using macro actions (2022, 46 citations) tackled coordination under communication failures, advancing the frontier of cooperative autonomous systems. More recently, Tan has embraced transformer architectures and large language models to push robot navigation further, with NavFormer (2024, 33 citations) enabling target-driven navigation in dynamic environments, and newer work incorporating vision-language models for social navigation and hand-drawn map interpretation. His early foundations in visual servoing round out a research trajectory that has grown steadily in ambition and impact, accumulating over 200 citations across a focused, coherent body of work that meaningfully advances autonomous robotics.
Research Focus
Key Achievements
Top Papers
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- 6Mobile Robot Regulation with Position Based Visual Servoing7 citations · 2018
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- 8Mobile Robot Regulation With Image Based Visual Servoing3 citations · 2018
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- 10A Novel Autonomous Scaled Electric Combat Vehicle2 citations · 2019