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.

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).
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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.

Generative Modeling For Imitation Learning And Motion Planning

1. •       Bib
     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).
2. •       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.
3. •       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).
4. •       Bib
     Le, A. T. (2021). Learning Task-Parameterized Riemannian Motion Policies.

Task And Motion Planning

1. •     Bib
     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.
2. •       Bib
     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).