An Thai Le

Research Interests
- Scaling robot planning
- Learning to plan
- Optimal Transport theory and gradient flows
Affiliation
TU Darmstadt, Intelligent Autonomous Systems, Computer Science Department
Contact
an@robot-learning.de
an.le@tu-darmstadt.de
Room E225, 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.
Reviewing
IROS, ICRA, R:SS, CoRL'23, AAAI'22 & '23, IEEE RA-L, and various Robotics & ML workshops.
Teaching Assistant
- Reinforcement Learning (SS 2022)
- Statistical Machine Learning (SS 2023)
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.
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).
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- Le, A. T. (2021). Learning Task-Parameterized Riemannian Motion Policies.
Task And Motion Planning
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- Chalvatzaki, G.; Younes, A.; Nandha, D.; Le, A. T.; Ribeiro, L.F.R.; Gurevych, I. (2023). Learning to reason over scene graphs: a case study of finetuning GPT-2 into a robot language model for grounded task planning, in: Dimitrios Kanoulas (eds.), Frontiers in Robotics and AI.
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- 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).
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 | [Ongoing] |
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] |