Papers
5
Total Citations
50
H-Index
4
About
Minh Tuan Tran is a robotics researcher whose work bridges the gap between human motor control and robotic systems. His primary research focuses on humanoid robotics, human-robot interaction, and advanced motion planning. Tran's most significant contribution lies in developing computational approaches for generating human-like reaching movements in humanoid robots, as demonstrated in his highly cited 2012 paper (27 citations) and his foundational 2010 work on movement primitives. He pioneered methods that allow robots to replicate natural human reaching motions using small sets of movement primitives, drawing from biological motor control principles and dynamic modeling. Tran has also advanced the field of motion planning by introducing neural network approaches for predicting sample collisions, addressing the computational challenges of high-dimensional robotic systems. More recently, he has explored human-centred interfaces, including mixed reality and virtual reality applications for industrial and collaborative robot control, contributing to the fourth industrial revolution. His work on VR-based teleoperation systems demonstrates his commitment to creating intuitive, human-friendly robotic interfaces. With a career spanning over a decade, Tran continues to shape how robots understand and replicate human movement.
Research Focus
Key Achievements
Top Papers
- 1
- 2Humanoid human-like reaching control based on movement primitives12 citations · 2010
- 3Predicting Sample Collision with Neural Networks5 citations · 2020
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- 5