An Thai Le

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

• Le, A. T.; Hansel, K.; Peters, J.; Chalvatzaki, G. (2023). Hierarchical Policy Blending As Optimal Transport, 5th Annual Learning for Dynamics & Control Conference (L4DC), PMLR.   Download Article [PDF]   BibTeX Reference [BibTex] Website

Imitation Learning

1. 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
2. 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

1. 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