# An Thai Le

> Robotics and machine learning researcher who turns sequential robot algorithms into parallel ones and fits robot foundation models onto small, low-cost hardware. Assistant Professor at VinUniversity (Hanoi, Vietnam), Visiting Professor at TU Darmstadt (IAS, Jan Peters' lab), and Director of Foundation AI at VinRobotics, where the methods run on humanoid robots. Contact: an@robot-learning.de.

- **Research to deployment.** Combines academic research with industry leadership. At VinRobotics: leads the RL stack for high-payload humanoid locomotion, the humanoid VLA architecture and training recipe, and the edge-deployment toolchain.
- **Parallel motion planning.** Recasts planning as tensor search that runs on GPUs: Global Tensor Motion Planning (IEEE RA-L 2025, ICRA 2026), Model Tensor Planning (TMLR 2025, ICLR 2026), Accelerating Motion Planning via Optimal Transport (NeurIPS 2023), Motion Planning Diffusion (IEEE T-RO 2025).
- **Efficient robot foundation models.** VLA policies that run on CPUs, Raspberry Pi 5 and Jetson Orin (vla.simd, FoldQuantVLA); data-efficient VLA adaptation (FOCA, ICML 2026); smooth asynchronous execution of action chunks without retraining (PAINT, CoRL 2026).
- **Humanoids in contact.** Whole-body compliance for a 70 kg humanoid (CompliantWBC), payload-robust perceptive stair descent (TACT-ful), uncertainty-aware humanoid locomotion (DoublyAware, IEEE RA-L 2026).
- **Trusted by peers.** Associate Editor for IEEE RA-L and ICRA 2027; Area Chair for ICML 2027, ICLR 2027, CoRL 2026 and RLC 2024; NeurIPS 2026 Top Reviewer.
- **Range.** Gradient-free training through optimal transport (PolyStep, TMLR 2026), and differentiable JAX tools for general relativity published in Physical Review D and Classical and Quantum Gravity.
- **Mentor.** Advises MSc students and VinRobotics residents; student-led work includes PAINT (CoRL 2026).
- **Background.** PhD (2025) at TU Darmstadt with Jan Peters, thesis *Tensor Search Methods for Vectorizing Motion Planning*, external examiner Siddhartha Srinivasa. MSc Universität Stuttgart; research at Bosch Center for Artificial Intelligence and HLRS; engineering at Intel.
- **Good fit for:** collaborations, invited talks and reviews on GPU-parallel planning, efficient VLA inference and quantization, humanoid locomotion and compliance, and optimal transport in robotics and ML; MSc and PhD supervision; industry partnerships that deploy robot learning on real hardware. Students and collaborators: https://anindex.github.io/#join
- Name variants: An Thai Le, An T. Le, Le Thai An. ORCID: 0000-0003-0929-3316.
- Paper status (accepted, under review, ongoing) is kept current on the homepage. Cite with the BibTeX entries in llms-full.txt.

Markdown version of https://anindex.github.io/ for AI agents and LLM tools, generated from the HTML. Site index: https://anindex.github.io/llms.txt · Markdown: https://anindex.github.io/index.md · Full text with BibTeX: https://anindex.github.io/llms-full.txt · News feed: https://anindex.github.io/feed.xml

---

Robotics · Learning · Control  
Optimization · Geometry · Relativity

# An Thai Le

Assistant Professor, [VinUniversity](https://vinuni.edu.vn/people/le-thai-an/)  
Visiting Professor, [TU Darmstadt](https://www.ias.informatik.tu-darmstadt.de/Team/AnThaiLe) · Director of Foundation AI, [VinRobotics](https://vinrobotics.net/)

I study how robots can plan, learn, and act reliably with limited computation and data.

Our group develops parallel motion planners, [data- and compute-efficient robot foundation models](https://anindex.github.io/#scaling-down), and force-aware controllers for heavy humanoids. We study locomotion and manipulation where robots must reason about physical contact.

- <an@robot-learning.de>
- [Google Scholar](https://scholar.google.com/citations?user=k0r0tfUAAAAJ)
- [GitHub](https://github.com/anindex)
- [OpenReview](https://openreview.net/profile?id=~An_Thai_Le1)
- [ORCID](https://orcid.org/0000-0003-0929-3316)
- [LinkedIn](https://www.linkedin.com/in/anindex/)
- [X](https://x.com/an_thai_le)

[Interested in working together?](https://anindex.github.io/#join) I welcome curious students and collaborators.

## Latest news

- Oct 2026 Happy to serve as an **Associate Editor** for [IEEE Robotics and Automation Letters (RA-L)](https://www.ieee-ras.org/publications/ra-l) and an **Area Chair** for [ICML 2027](https://icml.cc/). I look forward to handling your submissions!
- Sep 2026 **[Start Right, Arrive Right](https://paint-action-chunking.github.io/)** (PAINT) has been accepted at [CoRL](https://www.corl.org/) 2026. PAINT selects initial noise to smoothly connect action chunks during asynchronous execution, without retraining. Congratulations to my student Ho Trong Bao and the team!
- Sep 2026 Happy to serve as an **Associate Editor** for [ICRA 2027](https://www.ieee-ras.org/conferences-workshops/fully-sponsored/icra) and an **Area Chair** for [ICLR 2027](https://iclr.cc/). I look forward to reading and discussing your work.
- Sep 2026 **[Observer-robust energy condition verification for warp drive spacetimes](https://doi.org/10.1088/1361-6382/aeb25a)** (warpax) has been accepted by *Classical and Quantum Gravity* (published online October 2026). The paper introduces vectorized auto-diff tools with JAX for checking energy conditions in warp drive spacetimes. [Code](https://github.com/anindex/warpax) is online.
- Sep 2026 **[Training Non-Differentiable Networks via Optimal Transport](https://openreview.net/forum?id=8mlcqTTMuU)** (PolyStep) has been accepted by [TMLR](https://jmlr.org/tmlr/). PolyStep trains non-differentiable networks through optimal transport with only forward evaluations. [Code](https://github.com/anindex/polystep) is online.

Earlier news

- Jul 2026 A busy few months with our [VinUniversity](https://vinuni.edu.vn/) students and [VinRobotics](https://vinrobotics.net/) residents: new preprints on VLA fine-tuning, equivariance, noise selection, and inference, plus payload-robust locomotion and whole-body compliance. Lucky to work with this team! The [papers](https://anindex.github.io/#publications) are below.
- Jul 2026 **[StructSAM](https://arxiv.org/abs/2603.07307)**, token merging for Segment Anything, was presented at the ICML 2026 AdaptFM workshop.
- Jun 2026 Happy to serve as an **Area Chair** for [CoRL 2026](https://www.corl.org/). Looking forward to reading your submissions!
- Jun 2026 Invited talks on *Designing Planning Algorithms for the Era of Parallelism* at the **[ICRA 2026 Workshop on Robot Architecture](https://sites.google.com/view/roboarch-icra26/home)** and the **[2nd Next-Gen Robot Learning Symposium](https://sites.google.com/view/robot-learning-symposium)**. The [talk slides](https://anindex.github.io/slides/roboarch/) are online.
- Jun 2026 **[CLOT](https://mengguo.github.io/personal_site/papers/pdf/zhang2026clot.pdf)**, multi-robot motion planning via collaborative optimal transport under signal temporal logic tasks, appears at [ICRA 2026](https://2026.ieee-icra.org/) in Vienna. Joint work with [Meng Guo](https://mengguo.github.io/personal_site/)'s group at Peking University.
- May 2026 **[Rarity of rocket-driven Penrose extraction in Kerr spacetime](https://doi.org/10.1103/f3jn-7wcp)** accepted by [Physical Review D](https://journals.aps.org/prd/) (published June 2026, Phys. Rev. D 113, 124036).
- Apr 2026 **[FOCA: Future-Oriented Conditioning for Data-Efficient Vision-Language-Action Adaptation](https://focavla.github.io/)** accepted at [ICML 2026](https://icml.cc/virtual/2026/poster/66754). Congratulations to co-first authors Duc Minh Nguyen, Nghiem Tuong Diep and Binh Gia Nguyen, and thanks to the whole team!
- Feb 2026 **[DoublyAware](https://doi.org/10.1109/LRA.2025.3648611)**, uncertainty-aware TD-MPC for humanoid locomotion, published in [IEEE RA-L](https://www.ieee-ras.org/publications/ra-l) (vol. 11, no. 2). Well done, Khang Nguyen!
- Oct 2025 I joined [VinUniversity](https://vinuni.edu.vn/) as **Assistant Professor** and [TU Darmstadt](https://www.tu-darmstadt.de/) as **Visiting Professor**.
- Aug 2025 I joined **[VinRobotics](https://vinrobotics.net/)** as **Director of Foundation AI**, working on RL locomotion, humanoid VLA architectures, and onboard deployment. Plenty of algorithms to put to work on real hardware.
- Aug 2025 Defended my PhD thesis on *Tensor Search Methods for Vectorizing Motion Planning* at [TU Darmstadt](https://www.tu-darmstadt.de/)! The [full thesis](https://tuprints.ulb.tu-darmstadt.de/entities/publication/41c837ff-8bab-4ad1-acaf-4d4602f41b26) is on TUprints. Endless thanks to [Prof. Jan Peters](https://www.ias.informatik.tu-darmstadt.de/Team/JanPeters) for tolerating my mischief and supporting me throughout. I could not have asked for a better advisor. Huge gratitude to [Prof. Siddhartha Srinivasa](https://goodrobot.ai/) for readily agreeing to serve as external examiner, and to the rest of the committee for their time and feedback.
- Jul 2025 **[Model Tensor Planning](https://openreview.net/forum?id=fk1ZZdXCE3)** accepted at [TMLR](https://jmlr.org/tmlr/) 2025 and [ICLR](https://iclr.cc/) 2026 (J2C track). Thanks to my co-authors and the reviewers for making it a better paper.
- Jul 2025 **[Global Tensor Motion Planning](https://sites.google.com/view/gtmp/home)** published in [IEEE RA-L](https://www.ieee-ras.org/publications/ra-l) 2025 and accepted at [ICRA](https://www.ieee-ras.org/conferences-workshops/fully-sponsored/icra) 2026.
- Jun 2025 **[Motion Planning Diffusion](https://sites.google.com/view/motionplanningdiffusion)** published in [IEEE T-RO](https://www.ieee-ras.org/publications/t-ro) 2025.
- May 2025 Invited talks at **[RMIT University](https://www.rmit.edu.vn/)**, **[Rice University](https://www.rice.edu/)**, and **[HUST](https://hust.edu.vn/)** on tensor search methods for motion planning.

## Research

### Efficient algorithmic robotics

I study how the way we represent a problem changes what a robot can compute and learn. Can we turn a sequential search into parallel operations? Can a policy keep its useful behavior with less data and compute? I work on these questions in motion planning, robot foundation models, and humanoid control, where the answers have to hold up on real hardware.

01 / Problem representation

**[Expose parallel structure](https://anindex.github.io/#parallel-planning)** Change the representation of search so many candidates share the same computation. [GTMP · multipartite search graph](https://arxiv.org/abs/2411.19393)

02 / Robot foundation models

**[Scale down, retain capability](https://anindex.github.io/#scaling-down)** Use structure and shared computation to fit learned behavior into a smaller budget. [vla.simd · native SIMD computation](https://arxiv.org/html/2609.24274v1#S4.F2)

03 / Whole-body control

**[Reason through contact](https://anindex.github.io/#contact-control)** Coordinate motion and compliance across the body as forces and support change. [CompliantWBC · force response](https://compliantwbc.github.io/#demos)

01 / Planning & optimization

### Representations for efficient algorithms

How can a change of representation simplify an algorithm and expose more parallel computation?

Many planners explore one possibility at a time. By arranging candidate robot configurations in layers, we can evaluate many possible paths together on a GPU. I study how choices like this change both the algorithm and the computation it needs.

Tensor motion planning, optimal transport, and learned trajectory priors give us different ways to organize that search. I care about when they find a solution and whether more computation improves it. Our completeness and convergence results make the assumptions behind those answers explicit.

[GTMP](https://anindex.github.io/#pub-gtmp) · [Anytime GTMP](https://anindex.github.io/#pub-anytime-gtmp) · [MPOT](https://anindex.github.io/#pub-mpot) · [MTP](https://anindex.github.io/#pub-mtp) · [MPD](https://anindex.github.io/#pub-mpd-tro) · [CLOT](https://anindex.github.io/#pub-clot) · [PolyStep](https://anindex.github.io/#pub-polystep) · [AAC](https://anindex.github.io/#pub-aac)

[GTMP](https://anindex.github.io/#pub-gtmp) · parallel motion planning. [Layered search graph](https://anindex.github.io/images/gtmp.png)

[MTP](https://anindex.github.io/#pub-mtp) · tensor search for model predictive control.

02 / Robot foundation models

### Scaling down robot foundation models

How much capability can we retain with less data, memory, and compute?

I want robot foundation models to be useful on the hardware we can put on a robot. We remove redundant layers and use low-bit arithmetic to reduce inference cost. To learn from fewer demonstrations, we build geometric symmetries into policies and use future observations as supervision.

A smaller model still needs an implementation that suits the hardware. Memory layout and shared operations can matter as much as parameter count. We also use search and retrieval to guide a frozen policy during execution. For each approach, we measure what the robot can still do alongside latency and memory use.

#### Raspberry Pi 5 · CPU-only inference

With vla.simd, IMPACT supplies **33.5 actions/s in fp32** and **81.2 in int8** after a 90-second thermal soak. These rates measure action supply; feedback frequency is lower. [Paper results](https://arxiv.org/html/2609.24274v1#S6.SS2)

[FoldQuantVLA](https://anindex.github.io/#pub-foldquantvla) · [vla.simd](https://anindex.github.io/#pub-vlasimd) · [vla.cpp](https://anindex.github.io/#pub-vlacpp) · [CLP](https://anindex.github.io/#pub-clp) · [FOCA](https://anindex.github.io/#pub-foca) · [FINE](https://anindex.github.io/#pub-fine) · [EquiVLA](https://anindex.github.io/#pub-equivla) · [PAINT](https://anindex.github.io/#pub-paint) · [SDN](https://anindex.github.io/#pub-sdn-vla) · [R2A](https://anindex.github.io/#pub-r2a)

[vla.simd / IMPACT](https://vla-simd.github.io/) · "Put the tape into the box." SO-101, original speed.

[PAINT](https://anindex.github.io/#pub-paint) · connecting action chunks without retraining. 2× speed.

03 / Humanoid control

### Whole-body control through contact

How should a humanoid use its whole body to respond to contact and changing loads?

A pull on a humanoid's arm changes what its torso and legs must do to keep balance. I study controllers that respond through the whole body, using force estimates from the robot's own sensors, parallel foothold search, and compliance training. We test these ideas on 70–85 kg humanoids under contact and changing loads.

We also ask what a controller can reuse when the robot's body changes. CrossBFM transfers a shared behavior space, with the strongest transfer between similar humanoids. In [γ₀](https://anindex.github.io/#pub-gamma-zero), we train one motion policy across many robot bodies. Both approaches help us study which parts of behavior can be shared across hardware.

[γ₀](https://anindex.github.io/#pub-gamma-zero) · [CrossBFM](https://anindex.github.io/#pub-crossbfm) · [CompliantWBC](https://anindex.github.io/#pub-compliantwbc) · [TACT-ful](https://anindex.github.io/#pub-tactful) · [DoublyAware](https://anindex.github.io/#pub-doublyaware) · [Hardware experiments](https://anindex.github.io/#humanoids)

[CompliantWBC](https://anindex.github.io/#pub-compliantwbc) · whole-body response to a downward pull.

[CrossBFM](https://anindex.github.io/#pub-crossbfm) · shared behavior across humanoids.

The bigger picture

### The geometry of computation

I keep coming back to the connection between a problem's geometry and the computation it needs. Nearby trajectories can share work during search. Symmetries can reduce what a policy has to learn. A robot's configuration determines how it can exert or withstand a force. I want our algorithms to make use of these relationships.

1.  **Problem geometry**Trajectories, symmetries, contact
2.  **Computational structure**Batches, shared work, coordination
3.  **Hardware organization**Parallel lanes, memory, native kernels

By the geometry of computation, I mean how problem structure maps onto hardware. [Tensor planning](https://anindex.github.io/#pub-gtmp) makes candidate paths regular enough to evaluate in batches; [vla.simd](https://anindex.github.io/#pub-vlasimd) organizes shared work around CPU instructions; [foothold search](https://anindex.github.io/#pub-tactful) brings parallel candidate evaluation into locomotion.

My longer-term goal is to bring these pieces together: policies and planners that fit the robot's compute budget, connected to controllers that regulate motion and compliance. We want to test the combined system on humanoid tasks involving contact, measuring task success alongside data requirements, memory use, and response time.

A personal interest

### Computational general relativity

[warpax](https://anindex.github.io/#pub-warpax) · expansion and energy-condition diagnostics for an Alcubierre-bubble parameter sweep.

I write numerical tools for general relativity in my free time because, to me, the physics is art. [warpax](https://anindex.github.io/#pub-warpax) computes spacetime curvature with vectorized auto-diff in JAX and checks energy conditions for all observers at a point through matrix inequalities. These pointwise tests alone cannot establish whether a warp drive is physically realizable. The [Kerr-flyby study](https://anindex.github.io/#pub-penrose) simulates rocket trajectories to find when energy extraction from a rotating black hole still allows escape. I use the same design thinking as in robotics: batch the computation and make the assumptions explicit.

[warpax · Classical and Quantum Gravity](https://anindex.github.io/#pub-warpax) · [Penrose extraction · Physical Review D](https://anindex.github.io/#pub-penrose)

In the news: warpax in [Popular Mechanics](https://www.popularmechanics.com/science/a71362600/scientists-say-physical-warp-drive-possible/) (May 2026); VinRobotics' visit to [DFKI](https://iml.dfki.de/news/promising-exchange-with-vinrobotics-on-future-collaboration/); VinRobotics humanoids at ICRA 2026 in [Báo Tin Tức](https://baotintuc.vn/cong-nghe-va-kien-tao/robot-hinh-nguoi-cua-viet-nam-gay-an-tuong-tai-dien-dan-hang-dau-the-gioi-20260605093328796.htm).

## Humanoids at work

Hardware experiments with our [VinRobotics](https://vinrobotics.net/) team: handling parts, performing martial arts, responding to contact, and walking under load.

### Kitting

Part-handling pilot.

### Humanoid high kick

Whole-body motion imitation on a humanoid.

### [Compliant whole-body control](https://anindex.github.io/#pub-compliantwbc)

Coordinating the whole body to respond to contact.

### [Stair descent with a 20 kg payload](https://anindex.github.io/#pub-tactful)

TACT-ful combines foothold planning with compliance training.

[More humanoid videos](https://anindex.github.io/#demos) · [Research behind the demos](https://anindex.github.io/#research)

## Publications

Papers and ongoing projects, each with its current status. \* marks co-first or co-last authors. A blue line marks representative papers. Select a figure to view it at full size. See also [Google Scholar](https://scholar.google.com/citations?user=k0r0tfUAAAAJ).

### 2026

1.  #### [γ₀: A Generalist Policy for Multi-Embodiment Motion Control](https://gamma-zero.com/)

    [Nico Bohlinger](https://www.ias.informatik.tu-darmstadt.de/Team/NicoBohlinger)\*, Bo Ai\*, Dichen Li, Tongzhou Mu, …, Tan-Dzung Do, …, An Thai Le, [Yue Wang](https://yuewang.xyz/), Davide Scaramuzza, Guanya Shi, Marco Hutter, Mac Schwager, Georgia Chalvatzaki, Henrik I. Christensen, Hao Su, Chelsea Finn, [Jan Peters](https://www.ias.informatik.tu-darmstadt.de/Member/JanPeters)

    We train a single motion policy across varied robot bodies, randomizing their embodiments during training to learn what carries over between them. The same policy controls humanoids and quadrupeds at the joint level, with outdoor hardware demonstrations. Try it on different robots in the interactive playground.

    Ongoing project · 2026 · [Project page](https://gamma-zero.com/) · [Playground](https://play.gamma-zero.com/)

2.  #### [CrossBFM: Distilling a Shared Latent Behavior Space Across Humanoid Embodiments](https://arxiv.org/abs/2609.38087)

    T.D. Do\*, T.D. Phuong\*, N. Bohlinger, C.T. Trinh, S. Ju, V.A. Ngo, J. Peters, X. Wang\*, An Thai Le\*

    Transfers a shared behavior space across humanoids. We use corresponding frames of retargeted motion to train an encoder without simulation, then train a controller for each robot. The shared space supports motion tracking, goal reaching, and reward prompts, with hardware demonstrations. Transfer to an unseen robot is strongest when its body resembles those used in training.

    Under review · 2026 · [arXiv](https://arxiv.org/abs/2609.38087) · [Project page](https://dotandung.github.io/crossbfm/)

3.  #### [Representative paper: CompliantWBC: Whole-Body Compliance for Heavy Humanoids via Force Latent Estimation and Residual Impedance Targets](https://compliantwbc.github.io/)

    T.D. Do\*, C.T. Trinh\*, T.D. Phuong, T.D. Dang, C. Le, T. Ly, V.A. Ngo, An Thai Le

    Estimates external forces from proprioception and adjusts impedance targets so a 70 kg humanoid yields to contact across its body. The analytical passivity guarantee is local and assumes fixed stiffness.

    Under review · 2026 · [Project page](https://compliantwbc.github.io/) · [PDF](https://compliantwbc.github.io/paper/compliant_wbc.pdf)

4.  #### [TACT-ful: Multi-Channel Terrain Affordance and Compliance Training for Payload-Robust Perceptive Humanoid Locomotion](https://arxiv.org/abs/2606.20645)

    T. Ly\*, T.D. Dang\*, C. Le, T.D. Do, T.D. Phuong, C.T. Trinh, V.A. Ngo, An Thai Le

    Combines GPU-parallel foothold search with compliance training to help humanoids walk over uneven terrain with changing payloads. Evaluated quantitatively in simulation, with qualitative hardware demonstrations.

    Under review · 2026 · [arXiv](https://arxiv.org/abs/2606.20645) · [Project page](https://fai-rl-tech.github.io/tact-locomotion.github.io/)

5.  #### [Pretraining Foundation Policies for Perceptive Humanoid Locomotion with Offline RL (SOFIE)](https://silentgradient.github.io/SOFIE/)

    Learns humanoid locomotion offline from expert rollouts of mixed quality. We score whole footsteps and give better examples more weight, so the policy is less likely to copy the experts' mistakes. A shared policy walks three humanoids over stepping stones, stairs, and rough ground. Simulation tests cover twelve humanoids across two simulators; pretraining also helps fine-tuning on an unseen humanoid.

    Under review · 2026 · [Project page](https://silentgradient.github.io/SOFIE/)

6.  #### [Terrain Traversal Without Motion Priors: Expert-Routed Humanoid Locomotion at Deployment Fidelity](https://fai-rl-tech.github.io/moe-perceptive/)

    Learns perceptive humanoid locomotion without motion-capture retargeting, a generative motion prior, or distillation. A terrain classifier selects among five experts, and gradient steps refine each foothold using a terrain-cost map. The policy completes an 85 m course in 18 of 20 runs in a second simulator configured for deployment. Hardware tests are still to come.

    Under review · 2026 · [Project page](https://fai-rl-tech.github.io/moe-perceptive/)

7.  #### [DoublyAware: Dual Planning and Policy Awareness for Temporal Difference Learning in Humanoid Locomotion](https://doi.org/10.1109/LRA.2025.3648611)

    Khang Nguyen, An Thai Le, Jan Peters, Minh Nhat Vu

    Handles planning and policy uncertainty separately in TD-MPC: conformal filtering screens candidate trajectories, and a group-relative trust region constrains policy updates. Evaluated on a simulated H1-2 humanoid.

    IEEE RA-L 2026 · [DOI](https://doi.org/10.1109/LRA.2025.3648611) · [arXiv](https://arxiv.org/abs/2506.12095)

8.  #### Representative paper: [FoldQuantVLA: Native Low-Bit Quantization of Vision-Language-Action Models via Consistent Folding](https://arxiv.org/abs/2609.24433)

    Hung T. Ho, Khanh D. Nguyen, Quang D. Nguyen, Thanh Q. Duong, Ngan Le, Meng Guo, Vien A. Ngo, An Thai Le

    Quantizes VLA policies for native low-bit inference without retraining, using a consistent activation representation. W4A4 projections run 1.20–1.33× faster than floating-point TensorRT on Jetson AGX Orin; selective INT8 improves action fidelity and measured real-robot success.

    Preprint · 2026 · [arXiv](https://arxiv.org/abs/2609.24433) · [Project page](https://review-artifact-27f4.github.io/foldquantvla/) · [Code](https://github.com/cair-vinuni/FoldQuantVLA)

9.  #### Representative paper: [vla.simd: Efficient CPU Inference for Language-Conditioned Manipulation](https://arxiv.org/abs/2609.24274)

    Khanh D. Nguyen, Hoang M. Truong, An Thai Le

    Runs VLA policies on CPUs by sharing SIMD kernels and reusing computation, with demonstrations on SO-101 and UR10e. On Raspberry Pi 5, IMPACT outputs 33.5 actions/s in fp32 after a 90 s thermal soak. This measures action throughput, not feedback frequency.

    Preprint · 2026 · [arXiv](https://arxiv.org/abs/2609.24274) · [Project page](https://vla-simd.github.io/) · [Code](https://github.com/cair-vinuni/vla.simd)

10. #### [Representative paper: Training Non-Differentiable Networks via Optimal Transport (PolyStep)](https://openreview.net/forum?id=8mlcqTTMuU)

    An Thai Le

    Uses optimal transport to weight candidate parameter updates and train non-differentiable networks with only forward evaluations. Compares gradient-free methods at matched evaluation budgets.

    TMLR 2026 · [OpenReview](https://openreview.net/forum?id=8mlcqTTMuU) · [arXiv](https://arxiv.org/abs/2605.01928) · [Code](https://github.com/anindex/polystep)

11. #### [Representative paper: Observer-robust energy condition verification for warp drive spacetimes (warpax)](https://doi.org/10.1088/1361-6382/aeb25a)

    An Thai Le

    Uses vectorized auto-diff in JAX to check energy conditions in warp drive spacetimes. Combines 4×4 matrix inequalities over all observers at a point with interval bounds for certified decisions.

    Class. Quantum Grav. 2026 · [DOI](https://doi.org/10.1088/1361-6382/aeb25a) · [arXiv](https://arxiv.org/abs/2602.18023) · [Code](https://github.com/anindex/warpax)

12. #### [Representative paper: Start Right, Arrive Right: Asynchronous Execution via Initial Noise Selection (PAINT)](https://arxiv.org/abs/2606.19774)

    T.B. Ho, Q.T. Nguyen, T.L. Ha, G.B. Nguyen, V.T. Nguyen, L. Dinh, M.N. Vu, D.M.H. Nguyen, An Thai Le, V.A. Ngo

    Uses flow inversion to select initial noise that smoothly connects action chunks during asynchronous execution. Requires no retraining or inference-time gradients; evaluated on 12 simulated benchmarks and 6 real manipulation tasks.

    CoRL 2026 · [arXiv](https://arxiv.org/abs/2606.19774) · [Project page](https://paint-action-chunking.github.io/) · [Repository](https://github.com/cair-vinuni/paint-action-chunking)

13. #### Representative paper: [FOCA: Future-Oriented Conditioning for Data-Efficient Vision-Language-Action Adaptation](https://icml.cc/virtual/2026/poster/66754)

    D.M. Nguyen\*, N.T. Diep\*, G.B. Nguyen\*, T.B. Ho, D.T. Le, Q.T. Nguyen, T.L. Ha, V.N. Tran, B. Thach, X.N. Tran, T.A. Tran, A. Habuda, P.L. Møller, T.N. Le, D. Sonntag, M. Niepert, K.D. Doan, V.N. Duong, H. Ngo, M.N. Vu, D.M.H. Nguyen\*, An Thai Le\*, V.A. Ngo\*

    Adapts VLA policies from few demonstrations by learning representations of future interactions and goals. Reaches 95.7% success on LIBERO with 20 demonstrations per task. Also evaluated in RoboCasa and on real robots.

    ICML 2026 · [arXiv](https://arxiv.org/abs/2606.20867) · [Project page](https://focavla.github.io/) · [Code](https://github.com/cair-vinuni/FOCA)

14. #### [CLOT: Multi-Robot Motion Planning via Collaborative Optimal Transport under Signal Temporal Logic Tasks](https://mengguo.github.io/personal_site/papers/pdf/zhang2026clot.pdf)

    Y. Zhang, Y. Zhang, An Thai Le, M. Guo

    Uses collaborative optimal transport to plan multi-robot trajectories for tasks specified in signal temporal logic. Updates trajectories without gradients; evaluated in simulation and on hardware.

    ICRA 2026 · [PDF](https://mengguo.github.io/personal_site/papers/pdf/zhang2026clot.pdf)

15. #### [StructSAM: Structure- and Spectrum-Preserving Token Merging for Segment Anything Models](https://arxiv.org/abs/2603.07307)

    D.M.H. Nguyen, T.A. Tran, D. Nguyen, S. Xie, T.Q. Nguyen, M.T.N. Truong, D. Palenicek, An Thai Le, M. Barz, E. Hannus, T. Nguyen, T. Dam, T.N. Le, N. Le, M. Vu, K. Doan, V. Ngo, P. Xie, J. Zou, D. Sonntag, J. Peters, M. Niepert

    Merges redundant tokens in Segment Anything's encoder while preserving boundaries and prompt regions. Measures the tradeoff between computation and segmentation accuracy.

    ICML 2026 Workshop (AdaptFM) · [arXiv](https://arxiv.org/abs/2603.07307) · [OpenReview](https://openreview.net/forum?id=K41rNB0F8j) · [Workshop](https://icml.cc/virtual/2026/75281)

16. #### [Rarity of rocket-driven Penrose extraction in Kerr spacetime](https://journals.aps.org/prd/abstract/10.1103/f3jn-7wcp) (Penrose rocket)

    An Thai Le

    Simulates 320,000 rocket flybys around rotating black holes to test energy extraction and escape. Successful cases are rare in the broad scans and concentrated at high black-hole spin and relativistic exhaust speeds.

    Phys. Rev. D 2026 · [DOI](https://doi.org/10.1103/f3jn-7wcp) · [arXiv](https://arxiv.org/abs/2601.19616) · [Code](https://github.com/anindex/penrose_process)

17. #### [Anytime Global Tensor Motion Planning (Anytime GTMP)](https://arxiv.org/abs/2608.25830)

    S. Coumar, An Thai Le\*, Z. Kingston\*

    Extends tensor motion planning to black-box local planners, finding more paths and lowering their cost as computation continues. Asymptotic optimality holds under stated clearance and sampling assumptions.

    Under review · 2026 · [arXiv](https://arxiv.org/abs/2608.25830) · [Code](https://github.com/CoMMALab/anytime_gtmp)

18. #### [TD-MTP: Coupling Structured Planning with Multi-Step Value Learning](https://tdmtp.github.io/)

    Asks whether a planner and its critic improve when changed together. Across ten paired tasks, adding a tensor-graph planner has different effects depending on whether the critic uses a one-step or TD(λ) target. On humanoid stairs, each change alone lowers return, but combining them improves on the baseline. We find clear evidence of this interaction on two of the ten tasks.

    Under review · 2026 · [Project page](https://tdmtp.github.io/)

19. #### [EquiVLA: A General Framework for Rotationally Equivariant Vision-Language-Action Models](https://arxiv.org/abs/2606.19784)

    T.L. Ha\*, Q.T. Nguyen\*, T.B. Ho\*, L. Dinh, M.D. Nguyen, G.B. Nguyen, T.Q. Pham, M.N. Vu, D.M.H. Nguyen, An Thai Le, V.A. Ngo

    Adds rotational equivariance to pretrained VLAs so actions rotate with the input. The perception module is approximately equivariant; the action head is exactly equivariant. Evaluated with GR00T N1.5 on LIBERO.

    Under review · 2026 · [arXiv](https://arxiv.org/abs/2606.19784) · [Project page](https://equivla.github.io/)

20. #### [vla.cpp: A Unified Inference Runtime for Vision-Language-Action Models](https://arxiv.org/abs/2606.08094)

    K.D. Nguyen, H.T. Ho, C.T. Nguyen, T.Q. Duong, L.D. Le, D.M.H. Nguyen, V.A. Ngo, An Thai Le

    Runs eleven VLA models in a C++ runtime for onboard inference. BitVLA completes 200/200 simulated LIBERO-Object episodes with inference on an 8 GB Jetson Orin Nano in the reported evaluation.

    Under review · 2026 · [arXiv](https://arxiv.org/abs/2606.08094) · [Project page](https://fai-modelopt-tech.github.io/vla-cpp.github.io/) · [Code](https://github.com/VinRobotics/vla.cpp)

21. #### [Finetuning Vision-Language-Action Models Requires Fewer Layers Than You Think (CLP)](https://arxiv.org/abs/2606.20246)

    G.B. Nguyen, T.B. Ho, T.L. Ha, K. Vo, P.L. Møller, Q.T. Nguyen, L. Dinh, T.M. Luu, T. Dam, V. Duong, T. Le, N.D.Q. Bui, M. Vu, T.N. Le, An Thai Le, N. Le, D. Sonntag, J. Zou, J. Peters, D.M.H. Nguyen, V.A. Ngo

    Prunes redundant VLA layers before fine-tuning with one pass that measures representation similarity. Evaluates how reducing model depth and training time affects task performance.

    Under review · 2026 · [arXiv](https://arxiv.org/abs/2606.20246) · [Project page](https://clpvla.github.io/) · [Code](https://github.com/cair-vinuni/CLP_VLA)

22. #### [Self-Improving VLA Policies: Selected Diffusion Noise for Spurious-Robust Action Smoothing (SDN)](https://arxiv.org/abs/2606.14084)

    D.M. Nguyen, B.N. Dao, T.M. Luu, B.G. Nguyen, V. Tong, A. Liu, V.N. Duong, D.D. Le, D. Sonntag, T. Le, N. Le, J. Peters, An Thai Le, M.N. Vu, M. Niepert, K.D. Doan, D.M.H. Nguyen, V.A. Ngo

    Selects initial diffusion noise for a frozen VLA by filtering for object-grounded action candidates, then executing the smoothest. Requires no policy retraining; evaluated in simulation and on real robots.

    Under review · 2026 · [arXiv](https://arxiv.org/abs/2606.14084)

23. #### [RoboGaze: Evaluating Robot World Models via Structured Vision-Language Analysis](https://arxiv.org/abs/2606.28385)

    M.L. Nguyen, N.T. Diep, H.K. Nguyen, M. Le, D.T. Le, H.H. Tran, D.D. Le, V.N. Duong, D. Sonntag, An Thai Le, D.M.H. Nguyen, V.A. Ngo, V.N. Tran

    Uses structured vision-language analysis to classify errors and identify when they occur in videos generated by robot world models, without training an evaluator.

    Under review · 2026 · [arXiv](https://arxiv.org/abs/2606.28385) · [Project page](https://robogaze-eval.github.io/) · [Code](https://github.com/cair-vinuni/RoboGaze)

24. #### [FINE: Future-Informed Navigation Encoding for Data-Efficient Vision-Language Navigation](https://arxiv.org/abs/2609.32855)

    K.H. Nguyen\*, H.P.Q. Nguyen\*, H.P. Nguyen, K.D. Binh, X.H. Nguyen, V.A. Ngo, D.M.H. Nguyen, H. Nguyen, An Thai Le

    Learns from future observations in navigation demonstrations. Landmark tokens predict upcoming landmarks' appearance and 3D geometry; a future token distinguishes the reached state from world-model counterfactuals. Gains on R2R-CE and RxR-CE grow with fewer demonstrations. This supervision adds no computation at deployment.

    Preprint · 2026 · [arXiv](https://arxiv.org/abs/2609.32855) · [Project page](https://finevln.github.io/)

25. #### [Retrieve to Act: Motion Primitive Graph Retrieval for Robust VLA Execution (R2A)](https://retrieve2act.github.io/r2a/)

    Builds a motion primitive graph from successful demonstrations. At test time, a query-conditioned graph network retrieves reference motion and blends it into a frozen VLA's actions through a per-dimension gate. Improves GR00T N1.7, π_(0.5), and StableVLA under camera, lighting, noise, and initial-state shifts in simulation and on a real Aloha robot, without fine-tuning.

    Under review · 2026 · [Project page](https://retrieve2act.github.io/r2a/)

26. #### [AAC: Admissible-by-Architecture Differentiable Landmark Compression for ALT](https://arxiv.org/abs/2604.20744)

    An Thai Le, V.A. Ngo

    Learns to compress landmark heuristics for ALT graph search while preserving admissibility: the heuristic never overestimates the remaining path cost. Evaluated on road networks against fixed landmark-selection baselines.

    Under review · 2026 · [arXiv](https://arxiv.org/abs/2604.20744) · [Code](https://github.com/anindex/aac)

### 2025

1.  #### Representative paper: [Model Tensor Planning](https://openreview.net/forum?id=fk1ZZdXCE3) (MTP)

    An Thai Le, Khai Nguyen, Minh Nhat Vu, João Carvalho, Jan Peters

    Combines global candidates from layered graphs with local trajectory sampling for model predictive control. Batches simulator rollouts in MuJoCo XLA; available in hydrax.

    TMLR 2025, ICLR 2026 (J2C) · [OpenReview](https://openreview.net/forum?id=fk1ZZdXCE3) · [Code](https://github.com/anindex/mtp)

2.  #### Representative paper: [Global Tensor Motion Planning](https://sites.google.com/view/gtmp/home) (GTMP)

    An Thai Le, Kay Pompetzki, João Carvalho, Joe Watson, Julen Urain, Armin Biess, Georgia Chalvatzaki, Jan Peters

    Searches a layered graph with tensor operations to produce batches of smooth spline paths in parallel. Provides probabilistic completeness under the paper's assumptions.

    IEEE RA-L 2025, ICRA 2026 · [DOI](https://doi.org/10.1109/LRA.2025.3575307) · [arXiv](https://arxiv.org/abs/2411.19393) · [Code](https://github.com/anindex/gtmp)

3.  #### Representative paper: [Motion Planning Diffusion: Learning and Adapting Robot Motion Planning with Diffusion Models](https://sites.google.com/view/motionplanningdiffusion) (MPD)

    João Carvalho, An Thai Le, Piotr Kicki, Dorothea Koert, Jan Peters

    Learns a diffusion prior over B-spline trajectories and guides sampling with task costs. Evaluated on 2D tasks and 7-DoF arms, including real pick-and-place tasks learned from human demonstrations.

    IEEE T-RO 2025, AAAI 2026 (journal track) · [DOI](https://doi.org/10.1109/TRO.2025.3593109) · [arXiv](https://arxiv.org/abs/2412.19948) · [Code](https://github.com/joaoamcarvalho/mpd-splines-public)

4.  #### [FlowMP: Learning Motion Fields for Robot Planning with Conditional Flow Matching](https://arxiv.org/abs/2503.06135)

    Khang Nguyen, An Thai Le, T. Pham, M. Huber, Jan Peters, Minh Nhat Vu

    Uses second-order conditional flow matching to learn acceleration-aware motion fields that generate smooth robot trajectories.

    IROS 2025 · [arXiv](https://arxiv.org/abs/2503.06135) · [Code](https://github.com/mkhangg/flow_mp)

5.  #### [Machine Learning with Physics Knowledge for Prediction: A Survey](https://arxiv.org/abs/2408.09840)

    Joe Watson, Chen Song, Oliver Weeger, Theo Gruner, An Thai Le, Kay Hansel, Ahmed Hendawy, Oleg Arenz, Will Trojak, Miles Cranmer, Carlo D'Eramo, Fabian Bülow, Tanmay Goyal, Jan Peters, Martin W. Hoffmann

    Surveys how physical knowledge informs model architectures, training objectives, and data, focusing on partial differential equations and open-source tools.

    TMLR 2025 · [arXiv](https://arxiv.org/abs/2408.09840)

### 2024

1.  #### [Structure-Aware E(3)-Invariant Molecular Conformer Aggregation Networks (ConAN)](https://arxiv.org/abs/2402.01975)

    D.M.H. Nguyen\*, N. Lukashina\*, T. Nguyen, An Thai Le, T. Nguyen, N. Ho, Jan Peters, D. Sonntag, V. Zaverkin, M. Niepert

    Predicts molecular properties by combining a 2D molecular graph with multiple 3D conformers using a differentiable fused Gromov–Wasserstein barycenter. Predictions are invariant to rotations, translations, and reflections.

    ICML 2024 · [arXiv](https://arxiv.org/abs/2402.01975) · [Code](https://github.com/duyhominhnguyen/conan-fgw)

2.  #### [Dude: Dual Distribution-Aware Context Prompt Learning For Large Vision-Language Model](https://proceedings.mlr.press/v260/nguyen25c.html)

    D.M.H. Nguyen\*, An Thai Le\*, T.Q. Nguyen, N.T. Diep, T. Nguyen, D. Duong-Tran, Jan Peters, L. Shen, M. Niepert, D. Sonntag

    Adapts vision-language models from few examples by aligning shared and LLM-generated class prompts with visual tokens through unbalanced optimal transport.

    ACML 2024 · [PMLR](https://proceedings.mlr.press/v260/nguyen25c.html) · [arXiv](https://arxiv.org/abs/2407.04489)

3.  #### [Grasp Diffusion Network: Learning Grasp Generators from Partial Point Clouds with Diffusion Models in SO(3)×R³](https://sites.google.com/view/graspdiffusionnetwork) (GDN)

    João Carvalho, An Thai Le, Philipp Jahr, Qiao Sun, Julen Urain, Dorothea Koert, Jan Peters

    Generates grasp poses from a partial point cloud using diffusion on SO(3)×R³, with collision-cost guidance. Evaluated on grasping from raw depth observations.

    Preprint · 2024 · [arXiv](https://arxiv.org/abs/2412.08398)

### 2023

1.  #### Representative paper: [Accelerating Motion Planning via Optimal Transport](https://sites.google.com/view/sinkhorn-step/) (MPOT)

    An Thai Le, Georgia Chalvatzaki, Armin Biess, Jan Peters

    Optimizes batches of trajectories without gradients. The Sinkhorn Step coordinates waypoint updates using entropic optimal transport, with a Gaussian-process smoothness prior.

    NeurIPS 2023 · [arXiv](https://arxiv.org/abs/2309.15970) · [Code](https://github.com/anindex/mpot)

2.  #### [Hierarchical Policy Blending As Optimal Transport](https://sites.google.com/view/hipobot) (HiPBOT)

    An Thai Le, Kay Hansel, Jan Peters, Georgia Chalvatzaki

    Uses unbalanced optimal transport in a look-ahead layer to choose weights for blending reactive Riemannian motion policies.

    L4DC 2023 · [Proceedings](https://proceedings.mlr.press/v211/le23a.html)

3.  #### [Motion Planning Diffusion: Learning and Planning of Robot Motions with Diffusion Models](https://sites.google.com/view/motionplanningdiffusion) (MPD)

    João Carvalho, An Thai Le, Mark Baierl, Dorothea Koert, Jan Peters

    Learns diffusion priors over robot trajectories and generates goal-conditioned motion plans through denoising. The earlier conference version of Motion Planning Diffusion.

    IROS 2023 · [arXiv](https://arxiv.org/abs/2308.01557)

4.  #### [Learning to reason over scene graphs: a case study of finetuning GPT-2 into a robot language model for grounded task planning](https://arxiv.org/abs/2305.07716)

    Georgia Chalvatzaki, Ali Younes, Daljeet Nandha, An Thai Le, L.F.R. Ribeiro, I. Gurevych

    Fine-tunes GPT-2 on scene graphs to turn long-horizon requests into subgoals for a planner. Evaluated on ALFRED.

    Frontiers in Robotics and AI 2023 · [arXiv](https://arxiv.org/abs/2305.07716)

### 2022

1.  #### [Learning Implicit Priors for Motion Optimization](https://sites.google.com/view/implicit-priors/home) (StochGPMP)

    Julen Urain\*, An Thai Le\*, Alexander Lambert\*, Georgia Chalvatzaki, Byron Boots, Jan Peters

    Learns energy-based motion priors to initialize trajectory optimization or guide it through cost factors. Tested in simulation and on a real robot.

    IROS 2022 · [arXiv](https://arxiv.org/abs/2204.05369) · [Project page](https://sites.google.com/view/implicit-priors/home) · [Code](https://github.com/anindex/stoch_gpmp)

### 2021

1.  #### [Learning forceful manipulation skills from multi-modal human demonstrations](https://arxiv.org/abs/2109.04222)

    An Thai Le, Meng Guo, Niels van Duijkeren, L. Rozo, R. Krug, A.G. Kupcsik, M. Bürger

    Learns insertion, sliding, and twisting from demonstrations of pose and force, using attractor-based impedance control. Demonstrated in e-bike motor assembly.

    IROS 2021 · [arXiv](https://arxiv.org/abs/2109.04222)

2.  #### [Hierarchical Human-Motion Prediction and Logic-Geometric Programming for Minimal Interference Human-Robot Tasks](https://arxiv.org/abs/2104.08137)

    An Thai Le, P. Kratzer, S. Hagenmayer, M. Toussaint, Jim Mainprice

    Combines hierarchical human-motion prediction with logic-geometric replanning to reduce interference in shared workspaces. Evaluated on MoGaze.

    IEEE RO-MAN 2021 · [arXiv](https://arxiv.org/abs/2104.08137)

No entries match both filters.

## Humanoid demos

At [VinRobotics](https://vinrobotics.net/), our researchers and engineers test learned locomotion, whole-body control, and physical interaction on real humanoids.

[ ](https://www.youtube.com/watch?v=TMuVG7YjUEk)

### Compliant whole-body control

CompliantWBC uses proprioceptive force estimates to coordinate the arms, torso, and legs of the ~70 kg VR-M3 during contact. The demos show guided interaction and cooperative tasks; the paper's passivity analysis is local and assumes fixed stiffness.

[Project page: Compliant whole-body control](https://compliantwbc.github.io/)

[ ](https://www.youtube.com/watch?v=rMq0qX4UDoY)

### Perceptive stair locomotion

VR-M3 (~70 kg) climbs real staircases of varying geometry at up to 0.6 m/s, including with a 5 kg payload. It uses a single onboard camera and a learned locomotion policy, with no LiDAR, motion capture, or teleoperation.

[Read article: Perceptive stair locomotion](https://vinrobotics.net/blog/perceptive-stair-locomotion)

[ ](https://www.youtube.com/watch?v=AIRifsVKtyc)

### Fast walking on VR-H3

VR-H3 (178 cm, 85 kg) uses an RL locomotion policy trained with phase-aware gait rewards and domain randomization. The training curriculum increases difficulty only after the policy learns to balance at each level.

[Read article: Fast walking on VR-H3](https://vinrobotics.net/blog/vin-robotics-humanoid-robots-reaching-human-level-walking-speed)

[ ](https://www.youtube.com/watch?v=dlNunvCxXcw)

### Built from the motor up

In-house actuators and real-time EtherCAT communication support learned locomotion on our humanoid platform. The team reports walking at 1.5 m/s, reaching 1.8 m/s with controllers trained on human motion data.

[Read article: Built from the motor up](https://vinrobotics.net/blog/humanoid-heartbeat)

[ ](https://www.youtube.com/watch?v=k_bzCFVfQ_k)

### Real-world football

An outdoor football game with people: passing, running, and celebrating goals together.

[Read article: Real-world football](https://vinrobotics.net/blog/a-new-milestone-in-proud-times)

[ ](https://www.youtube.com/watch?v=rSVRZtWi1Yc)

### Global debut

Platform preview for Computex and ICRA 2026: whole-body teleoperation, dynamic payload handling, MPC + RL locomotion, and perception-action learning.

[Read article: Global debut](https://vinrobotics.net/blog/vin-robotics-humanoid-platform-global-debut)

## Join us

Our group is based at VinUniversity in Hanoi, with close ties to VinRobotics and IAS at TU Darmstadt. If you enjoy designing algorithms, understanding why they work, and testing them on hardware, I'd like to hear from you.

### How we work together

I help students develop their own research judgment. We work through the mathematics and code together: which assumptions matter, and what evidence would change our minds? We turn rough ideas into questions we can test, with room to make mistakes and rethink. I encourage students to pursue their own directions and question mine.

### Open positions and projects

- PhD and MSc research at [VinUniversity](https://vinuni.edu.vn/).
- Research residencies and internships at [VinRobotics](https://vinrobotics.net/) (Foundation AI).
- MSc theses and co-advised PhD projects at [IAS, TU Darmstadt](https://www.ias.informatik.tu-darmstadt.de/).

### How to apply

- Email <an@robot-learning.de> with the subject `[Join][<track>] <Your Name>`.
- Attach a CV and a transcript, and link to code you wrote.
- Add a paragraph (under 300 words) about one of our papers: an idea that interests you, a question about the method, or an experiment you would like to try.

I read emails with this subject line first, but may not be able to reply to every message. Thanks for your patience.

## Group

The students, residents, alumni, and collaborators behind our research. Read about [how we work together and how to join](https://anindex.github.io/#join).

### MSc students

Dinh Van The Long [Learning under partial observability](https://proceedings.mlr.press/v270/nguyen25b.html)

### VinRobotics residents

Trinh Thi Cuc [Whole-body compliance](https://anindex.github.io/#pub-compliantwbc)

Dang Truong Duy [Payload-robust locomotion](https://anindex.github.io/#pub-tactful)

Le Anh Chien [Humanoid locomotion & control](https://anindex.github.io/#pub-tactful)

Nguyen Viet Duong Neuromorphic learning

Ly Phuc Thanh [Perceptive humanoid locomotion](https://anindex.github.io/#pub-tactful)

Do Tan Dung [Humanoid behavior models](https://anindex.github.io/#pub-crossbfm)

Ho Thinh Hung [VLA quantization & deployment](https://anindex.github.io/#pub-foldquantvla)

Ha Thien Loc [Equivariant robot policies](https://anindex.github.io/#pub-equivla)

Nguyen Quang Tan [Equivariance & action generation](https://anindex.github.io/#pub-equivla)

Ho Trong Bao [Asynchronous robot policies](https://anindex.github.io/#pub-paint)

Nguyen Dang Khanh [Efficient VLA inference](https://anindex.github.io/#pub-vlasimd)

Le Van Minh [World models & synthetic data evaluation](https://anindex.github.io/#pub-robogaze)

Nguyen Minh Hoang [Reinforcement learning & agents](https://mhngu23.github.io/mhnguyen-ai.github.io/)

Dao Duc Thinh [Egocentric VLA learning](https://thinh-dao.github.io/)

Truong Minh Hoang [CPU inference & SIMD kernels](https://anindex.github.io/#pub-vlasimd)

Nguyen Truong Chinh [VLA inference runtimes](https://anindex.github.io/#pub-vlacpp)

### Alumni

Phuong Tuan Dat [Cross-embodiment robot learning](https://anindex.github.io/#pub-crossbfm)

Magnus Dierking [Contact-rich model predictive control](https://magnusdierking.github.io/)

Caio Freitas [Graph diffusion for imitation learning](https://caio-freitas.github.io/)

Qiao Sun [Geometric diffusion for grasping](https://anindex.github.io/#pub-graspdiffusion)

Denis Andrić [Learning Hamiltonian dynamics](https://www.ias.informatik.tu-darmstadt.de/Off-Campus/AnThaiLe)

Sebastian Zach [Reactive motion generation](https://www.ias.informatik.tu-darmstadt.de/Off-Campus/AnThaiLe)

### Collaborators

[Zachary Kingston](https://commalab.org) Purdue University

[Meng Guo](https://mengguo.github.io/personal_site/) Peking University

[Nguyen Ho Minh Duy](https://duyhominhnguyen.github.io) DFKI

[Ngo Anh Vien](https://vienngo.github.io) VinRobotics

[Jan Peters](https://www.ias.informatik.tu-darmstadt.de/Member/JanPeters) TU Darmstadt

[Georgia Chalvatzaki](https://pearl-lab.com/people/georgia-chalvatzaki/) TU Darmstadt

[João Carvalho](https://joaoamcarvalho.github.io/) DFKI

[Viet T. Nguyen](https://vietngth.github.io/) University of Würzburg

## Open source

Research code and deployment tools developed with students and collaborators, including our releases at [CAIR, VinUniversity](https://github.com/cair-vinuni). Start with [GTMP](https://github.com/anindex/gtmp) or [MPOT](https://github.com/anindex/mpot) for planning, [vla.simd](https://github.com/cair-vinuni/vla.simd) for CPU policy inference, or [warpax](https://github.com/anindex/warpax) for numerical relativity. Questions and bug reports are welcome; reproducible examples help us investigate.

[cair-vinuni/vla.simd](https://github.com/cair-vinuni/vla.simd): C++ inference for VLA policies with native SIMD kernels on x86, Apple Silicon, and Raspberry Pi 5. (C++)  
[cair-vinuni/FoldQuantVLA](https://github.com/cair-vinuni/FoldQuantVLA): Native INT4 and INT8 inference for VLA models through consistent offline folding, with deployment on Jetson AGX Orin. (Python)  
[cair-vinuni/FOCA](https://github.com/cair-vinuni/FOCA): Adapts VLA policies from few demonstrations by learning from future interactions and goals. ICML 2026. (Python)  
[cair-vinuni/CLP_VLA](https://github.com/cair-vinuni/CLP_VLA): Prunes redundant VLA layers before fine-tuning, with training and evaluation scripts for robot manipulation. (Jupyter Notebook)  
[cair-vinuni/RoboGaze](https://github.com/cair-vinuni/RoboGaze): Diagnoses failures in videos generated by robot world models using structured vision-language analysis. (Python)  
[cair-vinuni/paint-action-chunking](https://github.com/cair-vinuni/paint-action-chunking): Project materials for PAINT: connecting action chunks through initial noise selection without retraining. CoRL 2026.  
[VinRobotics/vla.cpp](https://github.com/VinRobotics/vla.cpp): C++ runtime for onboard inference with eleven vision-language-action models. (C++)  
[anindex/mpot](https://github.com/anindex/mpot): Gradient-free trajectory optimization with optimal transport in PyTorch. NeurIPS 2023. (Python)  
[anindex/mtp](https://github.com/anindex/mtp): Global and local trajectory sampling for model predictive control in JAX. TMLR 2025 & ICLR 2026. (Python)  
[anindex/gtmp](https://github.com/anindex/gtmp): Parallel motion planning with layered graphs and tensor operations in JAX. RA-L 2025 & ICRA 2026. (Python)  
[anindex/polystep](https://github.com/anindex/polystep): Optimal-transport training for non-differentiable networks using only forward evaluations. TMLR 2026. (Python)  
[anindex/warpax](https://github.com/anindex/warpax): Vectorized auto-diff in JAX for checking energy conditions in warp drive spacetimes. CQG 2026. (Python)  
[anindex/penrose_process](https://github.com/anindex/penrose_process): Rocket-flyby simulations testing energy extraction and escape around rotating black holes. Phys. Rev. D 2026. (Python)  
[anindex/aac](https://github.com/anindex/aac): Learned compression of landmark heuristics for A\* search, preserving admissibility by construction. (Python)  
[VinRobotics/vinrobotics_mjlab](https://github.com/VinRobotics/vinrobotics_mjlab): RL training pipeline for high-payload humanoid locomotion, built on MuJoCo Warp. (Python)  
[VinRobotics/model-quantization-recipes](https://github.com/VinRobotics/model-quantization-recipes): Quantization recipes for language and speech models, with deployment checks. (Python)  
[anindex/note_model_opt](https://github.com/anindex/note_model_opt): Notes and runnable examples on model deployment.  
[vincekurtz/hydrax (contributor)](https://github.com/vincekurtz/hydrax): Sampling-based model predictive control on the GPU with JAX/MJX. Includes MTP. (Python)  

## Teaching & mentoring

I start with a small example, work through the mathematics, then implement it to test the assumptions. Our [student projects and mentoring](https://anindex.github.io/#join) follow the same approach: learn to ask precise questions and judge the evidence for yourself.

### Courses at TU Darmstadt

- [Statistical Machine Learning](https://www.ias.informatik.tu-darmstadt.de/Teaching/StatisticalMachineLearning) · Teaching assistant Summer 2023, Winter 2023/24,  
  Summer 2024, Winter 2024/25
- [Probabilistic Methods in Computer Science](https://www.ias.informatik.tu-darmstadt.de/Teaching/PROMI) Winter 2024/25
- Robot Learning Integrated Project / Expert Lab / Mechatronics Winter 2024/25
- [Reinforcement Learning](https://www.ias.informatik.tu-darmstadt.de/Teaching/ReinforcementLearning) · Teaching assistant Summer 2022

### Open learning resources

- [Designing Planning Algorithms for the Era of Parallelism](https://anindex.github.io/slides/roboarch/) · talk slides on tensor search and parallel motion planning.
- [Model deployment notes](https://github.com/anindex/note_model_opt) · examples to run and modify.

## Community & service

### Editorial and reviewing roles

Associate Editor  
[IEEE RA-L](https://www.ieee-ras.org/publications/ra-l), [ICRA 2027](https://www.ieee-ras.org/conferences-workshops/fully-sponsored/icra)

Area Chair  
[ICML 2027](https://icml.cc/), [ICLR 2027](https://iclr.cc/), [CoRL 2026](https://www.corl.org/), RLC 2024

Conference reviewer  
IROS, ICRA, RSS, L4DC, NeurIPS (Top Reviewer 2026), ICML, ICLR, AAAI

Journal reviewer  
IEEE RA-L, IEEE T-RO, Neurocomputing, TMLR, Frontiers in Robotics and AI

### Selected invited talks

- **Designing Planning Algorithms for the Era of Parallelism** · [ICRA Workshop on Robot Architecture](https://sites.google.com/view/roboarch-icra26/home) and the [2nd Next-Gen Robot Learning Symposium](https://sites.google.com/view/robot-learning-symposium), 2026. [Slides](https://anindex.github.io/slides/roboarch/).
- **Tensor search methods for motion planning** · RMIT University, Rice University, and HUST, 2025.

## Experience & education

1.  Oct 2025–present  
    Hanoi, Vietnam

    ### Assistant Professor

    [VinUniversity](https://vinuni.edu.vn/)

    Building a research group on efficient learning and planning for robot locomotion and manipulation.

2.  Oct 2025–present  
    Darmstadt, Germany

    ### Visiting Professor

    [TU Darmstadt](https://www.tu-darmstadt.de/)

    Co-advising MSc and PhD students in robot learning at IAS.

3.  Aug 2025–present  
    Hanoi, Vietnam

    ### Director of Foundation AI

    [VinRobotics](https://vinrobotics.net/)

    - RL stack for high-payload humanoid locomotion
    - Humanoid VLA architecture and training recipe
    - Model optimization and edge-deployment toolchain

4.  2022–2025  
    Darmstadt, Germany

    ### PhD in Computer Science

    [TU Darmstadt, Intelligent Autonomous Systems (IAS)](https://www.ias.informatik.tu-darmstadt.de/)

    Thesis: *Tensor Search Methods for Vectorizing Motion Planning*, supervised by [Prof. Jan Peters](https://www.ias.informatik.tu-darmstadt.de/Team/JanPeters).

5.  2019–2021  
    Stuttgart, Germany

    ### MSc in Information Technology

    [Universität Stuttgart](https://www.uni-stuttgart.de/)

    Thesis: *Learning task-parameterized Riemannian motion policies*, supervised by [Dr. Jim Mainprice](https://sites.google.com/site/jimmainprice/home) and [Dr. Meng Guo](https://mengguo.github.io/personal_site/). Graduated first class. Info-Preis for the best diploma, Sony Research Award, Deutschlandstipendium.

6.  May–Dec 2020  
    Renningen, Germany

    ### Research Intern

    [Bosch Center for Artificial Intelligence](https://www.bosch.com/research/bcai/)

    Forceful imitation learning for e-bike assembly, hosted by [Dr. Meng Guo](https://mengguo.github.io/personal_site/) in the robotics team.

7.  Nov 2019–Apr 2020  
    Stuttgart, Germany

    ### Research Assistant

    [HLRS (High-Performance Computing Center Stuttgart)](https://www.hlrs.de/)

    Configured and maintained HPC systems.

8.  2015–2019  
    Frankfurt, Germany

    ### BEng in Electrical Engineering and Information Technology

    [Frankfurt University of Applied Sciences](https://www.frankfurt-university.de/)

    Thesis: *Approaches to solve the kidnapped robot problem*. Graduated first class. DAAD, AmCham and eSilicon scholarships.

9.  Jan–May 2017  
    Ho Chi Minh City, Vietnam

    ### Engineering Intern

    [Intel Corporation](https://www.intel.com/)

    Designed systems to analyze high-volume manufacturing unit-test data and supported quality validation and reporting on the Thunderbolt line.

## Contact

<an@robot-learning.de>

VinUniversity, Hanoi, Vietnam

## Elsewhere

- [VinUniversity profile](https://vinuni.edu.vn/people/le-thai-an/)
- [CAIR, VinUniversity](https://cair.vinuni.edu.vn/people/)
- [IAS, TU Darmstadt](https://www.ias.informatik.tu-darmstadt.de/Team/AnThaiLe)
- [Experience & education](https://anindex.github.io/#experience)
- [Humanoid demos](https://anindex.github.io/#demos) · [Teaching](https://anindex.github.io/#teaching) · [Community](https://anindex.github.io/#service)

## This site

Last updated October 2026.

© 2026 An Thai Le