Tan-Hanh Pham
Papers
2
Total Citations
22
H-Index
2
About
Tan-Hanh Pham is a rising roboticist whose work bridges bio-inspired design and intelligent control. His research centers on two key areas: developing high-performance robotic platforms and creating resilient control systems for hardware failures. Pham’s most cited work, "A robotic fish capable of fast underwater swimming and water leaping with high Froude number" (2022, 17 citations), showcases his ability to engineer novel, agile robots that push the boundaries of aquatic locomotion—a contribution that has drawn attention for its potential in environmental monitoring and exploration. More recently, his 2024 paper, "Adaptive Compensation for Robotic Joint Failures Using Partially Observable Reinforcement Learning," tackles a critical challenge in industrial robotics: enabling manipulators to complete tasks despite unexpected joint malfunctions. By framing failure compensation as a partially observable reinforcement learning problem, Pham offers a practical path toward more resilient automation. Though early in his career, his work demonstrates a clear trajectory from innovative platform design to sophisticated, fault-tolerant control—a combination that promises significant impact on both field robotics and manufacturing.
Research Focus
Key Achievements
Top Papers
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