Mining Tan
Papers
2
Total Citations
10
H-Index
2
About
Mining Tan is an emerging researcher at the intersection of artificial intelligence, robotics, and natural language processing, with a focused specialization in embodied AI and large language model (LLM)-driven autonomous systems. His most notable contribution is the development of **RoboGPT**, an intelligent embodied agent framework that leverages the reasoning capabilities of large language models to enable robots to perform long-term sequential decision-making for everyday instruction-following tasks. This work addresses a critical challenge in robotics: bridging the gap between an LLM's generative power and the practical demands of grounded, real-world task execution guided by natural language. Tan's research has evolved iteratively, with an early version of RoboGPT introduced in 2023 and a refined, more widely recognized iteration published in 2025, accumulating a combined 10 citations across both works. His focus on common-sense reasoning, multi-step planning, and the translation of human instructions into executable robotic behavior places him at a frontier that is increasingly central to the future of human-robot interaction. As LLM-based robotics continues to expand rapidly, Tan's contributions offer a meaningful foundation for students and researchers exploring intelligent embodied agents.
Research Focus
Key Achievements
Top Papers
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- 2