Reinforcement Learning, Motion Planning, Optimal Control, Variational Inference, Optimal Transport
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An Thai Le
TU Darmstadt, FG IAS,
Hochschulstr. 10, 64289 Darmstadt
Office.
Room E225, Building S2|02
+49-6151-16-20073
an.le@tu-darmstadt.de
an@robot-learning.de
Before his Ph.D., An Thai Le has worked on motion optimization and reactive motion generation methods applied in robotics manipulation and human-robot collaboration settings. His thesis entitled Learning Task-Parameterized Riemannian Motion Policies ( Code link) was written under the supervision of Dr. Jim Mainprice from Universitaet Stuttgart and Dr. Meng Guo from BCAI. The thesis aims to learn movement primitives that are context-dependent for adaptation under different task conditions and composable with other behavior skills such as collision avoidance.
3. Le, A. T. (2021). Learning Task-Parameterized Riemannian Motion Policies, Master Thesis. Download Article Code
Year | Type | Together with | Student(s) | Topic | Document |
---|---|---|---|---|---|
2022 | Integrated Project | Junning Huang | Lu Liu, Yuheng Ouyang, Jiahui Shi | Benchmark Neural Lyapunov Control Algorithms on Pendulum | |
2022 | Integrated Project | Junning Huang | Chao Jin, Liyuan Xiang, Peng Yan | Hybrid Motion-Force Optimization | |
2022 | Master Thesis | Ali Younes, Georgia Chalvatzaki | Daljeet Nandha | Leveraging Large Language Models For Autonomous Task Planning | |
2023 | Master Thesis | Joao Carvalho, Julen Urain De Jesus | Mark Baierl | Score-based Planning Networks for Robot Motion Planning |