An Thai Le
Research Interests
- Scaling robot learning & planning
- Dynamics generative models
- Optimal Transport theory and gradient flows
Affiliation
TU Darmstadt, Intelligent Autonomous Systems, Computer Science Department
Contact
an.le@tu-darmstadt.de
Room D202, Building S2|02, TU Darmstadt, FB-Informatik, FG-IAS, Hochschulstr. 10, 64289 Darmstadt
+49-6151-16-20073
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.
Reviewing
IROS, ICRA, R:SS, RLC, CoRL, NeurIPS, ICML, ICLR, AAAI, IEEE RA-L, IEEE T-RO, and various Robotics & ML workshops.
Teaching Assistant
- Reinforcement Learning (SS 2022)
- Statistical Machine Learning (SS 2023, WS 2023/2024, SS2024)
- Robot Learning Integrated Project (WS 2024/2025)
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
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- Le, A. T.; Chalvatzaki, G.; Biess, A.; Peters, J. (2023). Accelerating Motion Planning via Optimal Transport, Advances in Neural Information Processing Systems (NIPS / NeurIPS).
- 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].
- 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].
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- 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
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- 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).
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- 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
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- Carvalho, J.; Le, A. T.; Baierl, M.; Koert, D.; Peters, J. (2023). Motion Planning Diffusion: Learning and Planning of Robot Motions with Diffusion Models, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
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- 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).
- 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.
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- 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
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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.
Supervised Theses and Projects
Year | Type | Together with | Student(s) | Topic | Document |
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2022 | Project | Junning Huang | Lu Liu, Yuheng Ouyang, Jiahui Shi | Benchmark Neural Lyapunov Control Algorithms on Pendulum | |
2022 | 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 | |
2023 | Master Thesis | Kay Hansel, Jan Peters, Georgia Chalvatzaki | Alper Gece | Leveraging Structured-Graph Correspondence in Imitation Learning | |
2023 | Master Thesis | Kay Hansel, Jan Peters | Marius Zoeller | Enhancing Smoothness in Policy Blending with Gaussian Processes | [Ongoing] |
2023 | Master Thesis | Ali Younes, Georgia Chalvatzaki | Caio Freitas | Graph Correspondence Diffusion For Imitation Learning | |
2023 | Master Thesis | Georgia Chalvatzaki | Denis Andric | Learning Symplectic Manifold Of Dynamical Systems | [Ongoing] |
2023 | Master Thesis | Georgia Chalvatzaki | Nico Nonnengiesser | Graph Neural Network For Robotics Control | [Ongoing] |
2024 | Master Thesis | Joao Carvalho | Qiao Sun | Geometry-Aware Diffusion Models for Robotics | [Ongoing] |
2024 | Master Thesis | Kay Hansel | Sebastian Zach | Reactive Motion Generation through Probabilistic Dynamic Graphs | [Ongoing] |