An Thai Le

I am now Assistant Professor at VinUniversity, and Director of Foundation AI at VinRobotics. I remain affiliated with TU Darmstadt as a Visiting Professor. Check out my new website at https://anindex.github.io/

An Thai Le

An Thai Le joined the Intelligent Autonomous Systems lab on November 1st, 2021, and defended his Ph.D. in 2025 under Prof. Jan Peters, with the thesis Tensor Search Methods for Vectorizing Motion Planning. He now leads a research group on efficient robot learning & planning algorithms at VinUniversity.

His research scales motion planning and policy learning to long-horizon, high-dimensional, and multimodal problems - primarily through tensor search (GPU-batched search and trajectory optimization over plan tensors) and by pairing algorithmic structure with generative models such as diffusion and flow matching. Batched planners are useful because they cover many homotopy classes at once, providing robustness to local minima and serving as oracles for data collection or distillation. Most current work targets humanoid loco-manipulation. He has also applied entropic optimal transport to other ML problems, including molecular conformer aggregation and CLIP prompt alignment.

Before his Ph.D., An Thai Le worked on forceful imitation learning during the internship at Bosch AI. He also worked on task and motion planning for human-robot collaboration settings. During his master's research, he was fortunate to be advised by Dr. Meng Guo and Dr. Jim Mainprice.

IAS Publications

Optimal Transport In Robotics

1. •       Bib
     Le, A. T.; Chalvatzaki, G.; Biess, A.; Peters, J. (2023). Accelerating Motion Planning via Optimal Transport, Advances in Neural Information Processing Systems (NIPS / NeurIPS).
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     Le, A. T.; Chalvatzaki, G.; Biess, A.; Peters, J. (2023). Accelerating Motion Planning via Optimal Transport, IROS 2023 Workshop on Differentiable Probabilistic Robotics: Emerging Perspectives on Robot Learning, [Oral].
   •     Bib
     Le, A. T.; Chalvatzaki, G.; Biess, A.; Peters, J. (2023). Accelerating Motion Planning via Optimal Transport, NeurIPS 2023 Workshop Optimal Transport and Machine Learning, [Oral].
2. •       Bib
     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.

Optimal Transport In ML

1. •     Bib
     Nguyen, D.M.H.; Lukashina, N.; Nguyen, N.; Le, A.T.; Nguyen, T.T.; Ho, N.; Peters, J.; Sonntag, D.; Zaverkin, V.; Niepert, M. (2024). Structure-Aware E(3)-Invariant Molecular Conformer Aggregation Networks, Proceedings of the International Conference on Machine Learning (ICML).
2. •     Bib
     Nguyen, D.H.M.*; Le, A.T.*; Nguyen, T.Q.; Nghiem, T.D.; Duong-Tran, D. ; Peters, J.; Li, S.; Niepert, M.; Sonntag, D. (2024). Dude: Dual Distribution-Aware Context Prompt Learning For Large Vision-Language Model, Asian Conference on Machine Learning (ACML).

Generative Modeling For Imitation Learning And Motion Planning

1. •       Bib
     Carvalho, J.; Le, A.T.; Kicki, P. ; Koert, D.; Peters, J. (2025). Motion Planning Diffusion: Learning and Adapting Robot Motion Planning with Diffusion Models, IEEE Transactions on Robotics (T-Ro), 41, pp.4881-4901.
2. •     Bib
     Nguyen, K.; Le, A. T.; Pham, T.; Manfred, H.; Peters, J.; Vu, M.N. (2025). FlowMP: Learning Motion Fields for Robot Planning with Conditional Flow Matching, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
3. •       Bib
     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).
   •   Bib
     Le, A. T.; Urain, J.; Lambert, A.; Chalvatzaki, G.; Boots, B.; Peters, J. (2022). Learning Implicit Priors for Motion Optimization, RSS 2022 Workshop on Implicit Representations for Robotic Manipulation.
4. •       Bib
     Le, A. T.; Guo M.; Duijkeren, N.; Rozo, L.; Krug, R.; Kupcsik, A.G.; Buerger, M. (2021). Learning forceful manipulation skills from multi-modal human demonstrations, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

Vectorization For Planning

1. •       Bib
     Le, A. T.; Nguyen, K.; Vu, M.N.; Carvalho, J.; Peters, J. (2025). Model Tensor Planning, Transactions on Machine Learning Research (TMLR).
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     Le, A. T.; Nguyen, K.; Vu, M.N.; Carvalho, J.; Peters, J. (2025). Model Tensor Planning, ICRA @ RoboARCH: Robotics Acceleration with Computing Hardware and Systems.
2. •     Bib
     Le, A. T.; Hansel, K.; Carvalho, J.; Watson, J.; Urain, J.; Biess, A.; Chalvatzaki, G.; Peters, J. (2025). Global Tensor Motion Planning, IEEE Robotics and Automation Letters (RA-L), and ICRA 2026 (RA-L Track), 10, 7, pp.7302-7309.
   •   Bib
     Le, A. T.; Hansel, K.; Carvalho, J.; Urain, J.; Biess, A.; Chalvatzaki, G.; Peters, J. (2024). Global Tensor Motion Planning, CoRL 2024 Workshop on Differentiable Optimization Everywhere.
3. •     Bib
     Pompetzki, K.; Le, A. T.; Gruner, T.; Watson, J.; Chalvatzaki, G.; Peters, J. (2025). Geometrically-Aware Goal Inference: Leveraging Motion Planning as Inference, German Robotics Conference (GRC).
   •     Bib
     Pompetzki, K.; Le, A. T.; Gruner, T.; Watson, J.; Chalvatzaki, G.; Peters, J. (2025). Geometrically-Aware Goal Inference: Leveraging Motion Planning as Inference, Multi-disciplinary Conference on Reinforcement Learning and Decision Making (RLDM).

Random Ideas

1. •     Bib
     Le, A.T.; Pompetzki, K.; Peters, J.; Biess, A. (2025). Kinematics Correspondence As Inexact Graph Matching, German Robotics Conference (GRC).
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     Le, A.T.; Pompetzki, K.; Peters, J.; Biess, A. (2025). Kinematics Correspondence As Inexact Graph Matching, Multi-disciplinary Conference on Reinforcement Learning and Decision Making (RLDM).