An Thai Le
Quick Info
Research Interests
Reinforcement Learning, Motion Planning, Optimal Control, Variational Inference, Optimal Transport
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Contact Information
An Thai Le
TU Darmstadt, FG IAS,
Hochschulstr. 10, 64289 Darmstadt
Office.
Room E225, Building S2|02
work+49-6151-16-20073
emailan.le@tu-darmstadt.de
emailan@robot-learning.de

An Thai Le joined the Intelligent Autonomous System lab on November 1st, 2021, as a Ph.D. student. He is currently working on applying Optimal Transport methods for the correspondence problem of Imitation Learning and also Motion Planning problems. He aims to explore fascinating viewpoints from Computational Geometry and apply these perspectives to Robotics and Machine Learning.
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.
Key References
Optimal Transport In Robotics
Imitation Learning
- Urain, J.; Le, A.T.; Lambert, A.; Chalvatzaki, G.; Boots, B.; Peters, J. (2022). Learning Implicit Priors for Motion Optimization, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
Download Article [PDF] BibTeX Reference [BibTex] Website
- Le, A.T.; Guo M.; Duijkeren, N.; Rozo, L.; Krug, R.; Kupcsik, A.G.; Bürger M. (2021). Learning forceful manipulation skills from multi-modal human demonstrations, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
Download Article [PDF] BibTeX Reference [BibTex] Video
3. Le, A. T. (2021). Learning Task-Parameterized Riemannian Motion Policies, Master Thesis. Download Article Code
TAMP For Human-Robot Collaboration
- Le, A.T.; Kratzer, P.; Hagenmayer, S.; Toussaint, M.; Mainprice, J. (2021). Hierarchical Human-Motion Prediction and Logic-Geometric Programming for Minimal Interference Human-Robot Tasks, 30th IEEE International Conference on Robot & Human Interactive Communication (RO-MAN).
Download Article [PDF] BibTeX Reference [BibTex] Video
Other Activities
- Served as Reviewer for IROS, ICRA, CoRL, AAAI and RA-L.
Teaching Assistant
- Reinforcement Learning (SS 2022)
- Statistical Machine Learning (SS 2023)
Supervised Theses and Projects
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 | pdf |
2023 | Master Thesis | Joao Carvalho, Julen Urain De Jesus | Mark Baierl | Score-based Planning Networks for Robot Motion Planning | pdf |