An Thai Le

An Thai Le joined the Intelligent Autonomous System lab on November 1st, 2021, as a Ph.D. student. Currently, he is working to scale robotics learning and planning methods with batch optimization. In particular, he aims to scale planning methods to long-horizon, high-dimensional state-space, number of plans, and number of agents. Batch planning methods are crucial for robotics since they could discover many homotopy classes in the multi-objective problems, thereby exhibiting robustness to bad local minima. Furthermore, these batch methods can serve as a strong oracle for collecting datasets or striving to discover a global optimal solution for robotics skill execution. Besides his main works, he has applied entropic optimal transport in other ML applications, such as molecular representation aggregation for graph-based models or CLIP embedding alignments.

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.

Recent 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].
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     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.; Jahr, P. ; Sun, Q. ; Urain, J.; Koert, D.; Peters, J. (submitted). Grasp Diffusion Network: Learning Grasp Generators from Partial Point Clouds with Diffusion Models in SO(3)xR3, Submitted to the IEEE Robotics and Automation Letters (R-AL).
2. •       Bib
     Carvalho, J.; Le, A.T.; Kicki, P. ; Koert, D.; Peters, J. (submitted). Motion Planning Diffusion: Learning and Adapting Robot Motion Planning with Diffusion Models, Submitted to the IEEE Transactions on Robotics (T-Ro).
3. •     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).
4. •       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).
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     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.
5. •       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. (submitted). 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), 10, 7, pp.7302-7309.
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     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).
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     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).