Papers
1
Total Citations
11
H-Index
1
About
Juntao Tan is a researcher whose work lies at the intersection of robotics, machine learning, and planning under uncertainty. His key contributions center on developing efficient belief-space planning algorithms that enable robots to make robust decisions with incomplete or noisy information—a critical challenge for real-world autonomous systems. Tan is perhaps best known for his work on "Belief-Space Planning Using Learned Models with Application to Underactuated Hands" (2022, 11 citations), where he introduced a novel framework that integrates learned dynamics models with belief-space planning to improve manipulation tasks for complex, underactuated robotic hands. This approach not only enhances dexterous manipulation but also offers a scalable solution for robots operating in uncertain environments. His research has been recognized for bridging the gap between model-based planning and data-driven learning, offering practical pathways for more adaptive and reliable robotic systems. With a growing citation footprint, Tan’s work is increasingly influential in the robotics community, particularly for researchers focused on planning, control, and robotic manipulation.
Research Focus
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Top Papers
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