Portrait of An Thai Le

Robotics · Learning · Control
Optimization · Geometry · Relativity

An Thai Le

Assistant Professor, VinUniversity
Visiting Professor, TU Darmstadt · Director of Foundation AI, VinRobotics

For AI agents and LLM tools: a token-efficient Markdown version of this page is at https://anindex.github.io/index.md. Site index: https://anindex.github.io/llms.txt. Full text with BibTeX: https://anindex.github.io/llms-full.txt. News feed: https://anindex.github.io/feed.xml. Read-only WebMCP tools are registered on this page.

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, and force-aware controllers for heavy humanoids. We study locomotion and manipulation where robots must reason about physical contact.

Interested in working together? I welcome curious students and collaborators.

Latest news

  • I'm happy to share that AAC has been accepted by TMLR! Many thanks to Vien for the collaboration, and to the TMLR reviewers and editors for their valuable feedback that made the paper much better! The code is online.
  • CrossBFM was selected for an oral presentation at the CoRL 2026 Pretrain-to-Adapt workshop on November 12! Congratulations to Tan-Dzung Do, Tuan Dat Phuong, and the whole team on this recognition!
  • I'm happy to serve as an Associate Editor for IEEE Robotics and Automation Letters (RA-L) and an Area Chair for ICML 2027. I'm looking forward to working with authors and reviewers, and learning from your ideas!
  • Congratulations to my student Ho Trong Bao and the team! Start Right, Arrive Right (PAINT) has been accepted at CoRL 2026! It was also selected for a spotlight at the RSS 2026 Diff4RL workshop. PAINT connects action chunks smoothly during asynchronous execution, without retraining.
  • I'm glad to contribute as an Associate Editor for ICRA 2027 and an Area Chair for ICLR 2027. Looking forward to your papers and the discussions ahead!
Earlier news
  • Some good news from my physics side project: Observer-robust energy condition verification for warp drive spacetimes (warpax) has been accepted by Classical and Quantum Gravity (published online October 2026)! I use vectorized automatic differentiation in JAX to check energy conditions in warp drive spacetimes. Many thanks to the reviewers for helping me improve the paper! Code is online.
  • Glad to share that Training Non-Differentiable Networks via Optimal Transport (PolyStep) has been accepted by TMLR! PolyStep trains non-differentiable networks through optimal transport with only forward evaluations. Thank you to the reviewers and editors for their thoughtful feedback! Code is online.
  • It's been a busy few months with our VinUniversity students and VinRobotics residents. We have new preprints on VLA fine-tuning, equivariance, noise selection, and inference, plus payload-robust locomotion and whole-body compliance. I'm lucky to work with this team! Thank you all for the care and effort behind these papers! The papers are below.
  • StructSAM, token merging for Segment Anything, was presented at the ICML 2026 AdaptFM workshop. Congratulations to the team!
  • I'm happy to serve as an Area Chair for CoRL 2026. Looking forward to reading your work and exchanging ideas with the community!
  • Thank you to the organizers for inviting me to speak about Designing Planning Algorithms for the Era of Parallelism at the ICRA 2026 Workshop on Robot Architecture and the 2nd Next-Gen Robot Learning Symposium. The talk slides are online!
  • Happy to share CLOT, our work on multi-robot planning with optimal transport and signal temporal logic, at ICRA 2026 in Vienna. Many thanks to Meng Guo's group at Peking University for the collaboration!
  • I'm delighted that Rarity of rocket-driven Penrose extraction in Kerr spacetime has been accepted by Physical Review D (published June 2026, Phys. Rev. D 113, 124036)! Thank you to the reviewers and editors for their helpful feedback!
  • FOCA: Future-Oriented Conditioning for Data-Efficient Vision-Language-Action Adaptation has been accepted at ICML 2026! Congratulations to co-first authors Duc Minh Nguyen, Nghiem Tuong Diep and Binh Gia Nguyen, and thanks to the whole team!
  • DoublyAware, uncertainty-aware TD-MPC for humanoid locomotion, is now published in IEEE RA-L (vol. 11, no. 2). Congratulations, Khang Nguyen! It's been a pleasure working on this together!
  • I joined VinUniversity as Assistant Professor and TU Darmstadt as Visiting Professor. I'm looking forward to working with the students and colleagues at both places!
  • I joined VinRobotics as Director of Foundation AI, working on RL locomotion, humanoid VLA architectures, and onboard deployment. I'm excited to work with the team and put these algorithms to use on real hardware!
  • I defended my PhD thesis on Tensor Search Methods for Vectorizing Motion Planning at TU Darmstadt! The full thesis is on TUprints. A heartfelt thank you to Prof. Jan Peters for tolerating my mischief and supporting me throughout. I could not have asked for a better advisor! I'm also grateful to Prof. Siddhartha Srinivasa for serving as external examiner, and to the rest of the committee for their time and thoughtful feedback!
  • Model Tensor Planning has been accepted at TMLR 2025 and ICLR 2026 (J2C track)! Many thanks to my co-authors for the collaboration and to the reviewers for feedback that made the paper better!
  • Global Tensor Motion Planning is now published in IEEE RA-L 2025 and accepted at ICRA 2026! Thanks to my co-authors and the reviewers for helping bring this work together!
  • Motion Planning Diffusion is now published in IEEE T-RO 2025! Thanks to João and all our co-authors for the collaboration, and to the reviewers for their helpful feedback!
  • Thank you to my hosts at RMIT University, Rice University, and HUST for inviting me to discuss tensor search methods for motion planning!

Research

Efficient Algorithmic Robotics

My research agenda is to make capable robot behavior possible within practical limits on computation, memory, and data. I approach efficiency through algorithm design: how we represent a problem, organize its computation, and connect the result to the robot's physical behavior.

The philosophy behind this work is the geometry of computation. I look for representations that reveal which operations can run in parallel, which experiences can be shared, and which physical constraints must stay coupled. This connects our work in motion planning, robot foundation models, and humanoid control.

01 / Problem representation GTMP represents motion planning as a multipartite graph, with sampled configurations arranged in layers and candidate paths connecting them
Plan in parallel Arrange candidate paths so a GPU can evaluate them together. GTMP · multipartite search graph
02 / Robot foundation models The vla.simd paper figure maps linear layers, convolutions, and attention onto tiled matrix operations and a shared SIMD micro-kernel with a fixed register budget
Fit models to robot hardware Reduce memory and computation while measuring what the robot can still do. vla.simd · native SIMD computation
03 / Whole-body control A simulated humanoid bends its knees under a downward force at the pelvis; a red arrow indicates the applied force
Respond to physical contact Coordinate the whole body as contact forces and support change. CompliantWBC · force response

01 / Planning & optimization

Representations for efficient algorithms

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

Many planners make a sequence of small decisions: sample a configuration, check an edge, and decide where to search next. That structure can limit how much work runs in parallel. In tensor motion planning, we arrange candidate configurations in layers so a GPU can evaluate many possible paths together. Changing the representation changes the operations available to the planner.

Tensor planning, optimal transport, and learned trajectory priors offer different ways to organize this search. I study how these choices affect solution quality, path diversity, and the value of additional computation. Our completeness and convergence results make the conditions for these guarantees explicit. Runtime and memory measurements then help us judge whether it fits a robot's budget.

GTMP · Anytime GTMP · MPOT · MTP · MPD · CLOT · PolyStep · AAC

A simulated Panda arm follows a planned path around a box
GTMP · parallel motion planning. Layered search graph
Model Tensor Planning controls a simulated robot through repeated trajectory planning
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 vision-language-action (VLA) models to be useful on hardware a robot can carry. This means studying both what a policy learns and how its computations run. We remove redundant layers and use low-bit arithmetic to reduce inference cost. Geometric symmetries and supervision from future observations help policies learn from fewer demonstrations.

A smaller model still needs an implementation that suits the hardware. Memory layout, data movement, and shared operations can matter as much as parameter count. We also use search and retrieval to guide frozen policies during execution. The test is whether the robot retains useful behavior: we measure task success alongside response time, memory use, and data requirements.

FoldQuantVLA · vla.simd · vla.cpp · CLP · FOCA · FINE · EquiVLA · PAINT · SDN · R2A

An SO-101 arm running IMPACT picks up a roll of tape and places it in a box in the vla.simd hardware demonstration
vla.simd / IMPACT · "Put the tape into the box." SO-101, original speed.
PAINT hardware experiments with towel flinging on ALOHA and part placement on a humanoid
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 how its torso and legs must move to keep balance. Contact makes these parts of the body a coupled system. I study how controllers can respond through the whole body, using force estimates from onboard sensors, parallel foothold search, and compliance training. We test these ideas on 70–85 kg humanoids under contact and changing loads.

The same question extends across robot bodies: which parts of a behavior can transfer, and which depend on the hardware? CrossBFM shares a behavior space across humanoids, with the strongest transfer between similar bodies. In γ₀, we train one motion policy across many robot bodies. These projects help us study how shared behavior and body-specific control can work together.

γ₀ · CrossBFM · CompliantWBC · TACT-ful · DoublyAware · Hardware experiments

CompliantWBC: a 70 kg humanoid bends its knees under a person's downward pull, then rises again
CompliantWBC · whole-body response to a downward pull.
Fourier N1 and Booster T1 humanoids perform related motions in a CrossBFM simulation
CrossBFM · shared behavior across humanoids.

Research philosophy

The geometry of computation

I use the geometry of computation as a design philosophy: let the relationships in a problem shape its algorithms and their implementation. Nearby trajectories can share work during search. A rotated scene may call for a correspondingly rotated action. Contact couples forces across a robot's body. I want to understand what these relationships let us compute independently, reuse, or coordinate.

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

The connection becomes concrete in the implementation. Tensor planning organizes candidate paths into regular batches that parallel hardware can evaluate. vla.simd arranges shared computation around CPU instructions and memory access. Foothold search brings parallel candidate evaluation into locomotion. Choosing a representation is already an algorithmic decision: it changes which operations become cheap and which dependencies remain.

My longer-term goal is to connect these planners and policies to controllers that regulate motion and compliance. I want to test the combined system on humanoid tasks involving contact, within the robot's computation budget. Alongside task success, we need to understand how its data requirements, memory use, and response time change as tasks become harder.

A personal interest

Computational general relativity

An Alcubierre-bubble parameter sweep shows expansion, Eulerian energy density, and the null-energy-condition margin
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 computes spacetime curvature with automatic differentiation in JAX. It checks energy conditions for all observers at a point through matrix inequalities. These tests alone cannot establish whether a warp drive is physically realizable.

The Kerr-flyby study simulates rocket trajectories to test when energy extraction from a rotating black hole still allows escape. I enjoy turning geometric equations into numerical experiments that I can run and inspect. As in robotics, the representation matters: I batch the computation and make the assumptions explicit.

warpax · Classical and Quantum Gravity · Penrose extraction · Physical Review D

In the news: warpax in Popular Mechanics (May 2026); VinRobotics' visit to DFKI; VinRobotics humanoids at ICRA 2026 in Báo Tin Tức.

Humanoids at work

Hardware experiments with our VinRobotics team: handling parts, performing martial arts, responding to contact, and walking under load.

A humanoid picks and places parts between trays at a kitting workstation

Kitting

A humanoid handles parts in a kitting pilot.

A humanoid balances on one leg while performing a high kick

Humanoid high kick

Whole-body motion imitation on a humanoid.

A humanoid yields to external contact through its arms, torso, and legs while balancing

Compliant whole-body control

Coordinating the whole body to respond to contact.

A humanoid carrying a 20 kg backpack descends laboratory stairs

Stair descent with a 20 kg payload

TACT-ful combines foothold planning with compliance training.

More humanoid videos · Research behind the demos

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.

Topic
Year

2026

  1. RoboWorM: Evaluating Movement Realism, Consistency and Acceleration Affects in Embodied Video World Models

    Doanh Le Thien*, Viet-Thanh Nguyen*, Pham Tri Quang*, Nhi Pham, Christopher Wewer, Minh-Loi Nguyen, Hoang H. Tran, Tho Le, Tung M. Luu, Dung D. Le, Vu N. Duong, Jonas Fischer, Ngan Le, An Thai Le, Daniel Sonntag, Gim Hee Lee, James Zou, Mathias Niepert, Jan Peters, Duy M. H. Nguyen, Ngo Anh Vien

    RoboWorM benchmarks robot video models on 1,605 episodes across single-arm, bimanual, and humanoid robots. KIVA checks kinematic stability, and RIGIS checks consistency across camera views, without vision-language judges. We compare nine acceleration methods on three models to measure the tradeoff between speed and physical fidelity.

    Under review · 2026arXivPDFProject page
    BibTeX
    @misc{thien2026robowormevaluatingmovementrealism,
      title         = {{RoboWorM}: Evaluating Movement Realism, Consistency and Acceleration Affects in Embodied Video World Models},
      author        = {Doanh Le Thien and Viet-Thanh Nguyen and Pham Tri Quang and Nhi Pham and Christopher Wewer and Minh-Loi Nguyen and Hoang H. Tran and Tho Le and Tung M. Luu and Dung D. Le and Vu N. Duong and Jonas Fischer and Ngan Le and An Thai Le and Daniel Sonntag and Gim Hee Lee and James Zou and Mathias Niepert and Jan Peters and Duy M. H. Nguyen and Ngo Anh Vien},
      year          = {2026},
      eprint        = {2610.33807},
      archivePrefix = {arXiv},
      primaryClass  = {cs.RO},
      url           = {https://arxiv.org/abs/2610.33807}
    }
    RoboWorM audits generated robot videos: KIVA flags distorted arm geometry, then RIGIS compares two camera views through a shared 3D reconstruction
  2. SeeBFM: Behavior Foundation Model That Sees

    Thanh Ly, Truong-Duy Dang, Hung Nguyen, Duy M. H. Nguyen, Vien Anh Ngo, An Thai Le

    SeeBFM adds terrain perception to BFM-Zero while keeping reward, motion, and language prompts. A student model predicts a height scan from two depth cameras to guide steps over stairs, gaps, and moving obstacles. Tests are in simulation; hardware tests are planned.

    Under review · 2026Project pagePlayground
    BibTeX
    @misc{ly2026seebfm,
      title        = {{SeeBFM}: Behavior Foundation Model That Sees},
      author       = {Ly, Thanh and Dang, Truong-Duy and Nguyen, Hung and Nguyen, Duy M. H. and Ngo, Vien Anh and Le, An T.},
      year         = {2026},
      url          = {https://capfab.github.io/seebfm-media/},
      note         = {Under review}
    }
    SeeBFM in simulation: a humanoid crosses a 40 cm gap, steps over a moving 15 cm block, and climbs stairs using depth cameras
  3. γ₀: A Generalist Policy for Multi-Embodiment Motion Control

    Nico Bohlinger*, Bo Ai*, Dichen Li, Tongzhou Mu, …, Tan-Dzung Do, …, An Thai Le, Yue Wang, Davide Scaramuzza, Guanya Shi, Marco Hutter, Mac Schwager, Georgia Chalvatzaki, Henrik I. Christensen, Hao Su, Chelsea Finn, Jan Peters

    One motion policy controls humanoids and quadrupeds at the joint level, with outdoor hardware demonstrations. We vary robot bodies during training to learn what transfers between them. Try different robots in the interactive playground.

    Ongoing project · 2026Project pagePlayground
    A Unitree G1 humanoid walks outdoors alongside Unitree Go2 and Silver Badger quadrupeds, all controlled by Gamma Zero
  4. CrossBFM: Distilling a Shared Latent Behavior Space Across Humanoid Embodiments

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

    CrossBFM shares a behavior space across humanoids for motion tracking, goal reaching, and reward prompts, with hardware demonstrations. We train the encoder on retargeted motion without simulation, then train a controller for each robot. Transfer is strongest between similar bodies.

    Under review · CoRL 2026 Pretrain-to-Adapt Workshop (Oral)arXivProject pageWorkshop
    BibTeX
    @misc{do2026crossbfm,
      title         = {{CrossBFM}: Distilling a Shared Latent Behavior Space Across Humanoid Embodiments},
      author        = {Do, Tan-Dzung and Phuong, Tuan Dat and Bohlinger, Nico and Trinh, Cuc T. and Ju, Siwei and Ngo, Vien Anh and Peters, Jan and Wang, Xinchao and Le, An Thai},
      year          = {2026},
      eprint        = {2609.38087},
      archivePrefix = {arXiv},
      primaryClass  = {cs.RO},
      url           = {https://arxiv.org/abs/2609.38087}
    }
  5. Representative paper: CompliantWBC: Whole-Body Compliance for Heavy Humanoids via Force Latent Estimation and Residual Impedance Targets

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

    CompliantWBC estimates external forces from proprioception and adjusts impedance targets so a 70 kg humanoid yields to contact. The passivity guarantee is local and assumes fixed stiffness.

    Under review · 2026Project pagePDF
    BibTeX
    @misc{do2026compliantwbc,
      title        = {{CompliantWBC}: Whole-Body Compliance for Heavy Humanoids via Force Latent Estimation and Residual Impedance Targets},
      author       = {Do, Tan-Dzung and Trinh, Cuc T. and Phuong, Tuan Dat and Dang, Truong-Duy and Le, Chien and Ly, Thanh and Ngo, Vien Anh and Le, An T.},
      year         = {2026},
      howpublished = {\url{https://compliantwbc.github.io/paper/compliant_wbc.pdf}},
      note         = {Under review}
    }
  6. TACT-ful: Multi-Channel Terrain Affordance and Compliance Training for Payload-Robust Perceptive Humanoid Locomotion

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

    TACT-ful combines GPU-parallel foothold search with compliance training for uneven terrain and changing payloads. We report quantitative simulation results and qualitative hardware demonstrations.

    Under review · 2026arXivProject page
    BibTeX
    @misc{ly2026tactful,
      title         = {{TACT}-ful: Multi-Channel Terrain Affordance and Compliance Training for Payload-Robust Perceptive Humanoid Locomotion},
      author        = {Ly, Thanh and Dang, Truong-Duy and Le, Chien and Do, Tan-Dzung and Phuong, Tuan Dat and Trinh, Cuc T. and Ngo, Vien Anh and Le, An T.},
      year          = {2026},
      eprint        = {2606.20645},
      archivePrefix = {arXiv},
      primaryClass  = {cs.RO},
      url           = {https://arxiv.org/abs/2606.20645}
    }
  7. Pretraining Foundation Policies for Perceptive Humanoid Locomotion with Offline RL SOFIE

    SOFIE learns from offline expert rollouts and gives better footsteps more weight. One policy walks three humanoids over stepping stones, stairs, and rough ground. Simulation tests cover twelve humanoids across two simulators. Pretraining also helps adaptation to an unseen humanoid.

    Under review · 2026Project page
    BibTeX
    @misc{sofie2026,
      title        = {Pretraining Foundation Policies for Perceptive Humanoid Locomotion with Offline {RL}},
      author       = {Anonymous},
      year         = {2026},
      howpublished = {\url{https://silentgradient.github.io/SOFIE/}},
      note         = {Under review}
    }
  8. Terrain Traversal Without Motion Priors: Expert-Routed Humanoid Locomotion at Deployment Fidelity

    A terrain classifier selects among five locomotion experts, and gradient steps refine each foothold. Training uses no motion-capture retargeting, generative motion prior, or distillation. The policy completes an 85 m course in 18 of 20 runs in a second simulator configured for deployment. Hardware tests are pending.

    Under review · 2026Project page
    BibTeX
    @misc{moeterrain2026,
      title        = {Terrain Traversal Without Motion Priors: Expert-Routed Humanoid Locomotion at Deployment Fidelity},
      author       = {Anonymous},
      year         = {2026},
      howpublished = {\url{https://fai-rl-tech.github.io/moe-perceptive/}},
      note         = {Under review}
    }
  9. DoublyAware: Dual Planning and Policy Awareness for Temporal Difference Learning in Humanoid Locomotion

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

    DoublyAware handles planning and policy uncertainty separately in TD-MPC. Conformal filtering screens candidate trajectories, while a group-relative trust region constrains policy updates. We evaluate it on a simulated H1-2 humanoid.

    IEEE RA-L 2026DOIarXiv
    BibTeX
    @article{nguyen2026doublyaware,
      title         = {{DoublyAware}: Dual Planning and Policy Awareness for Temporal Difference Learning in Humanoid Locomotion},
      author        = {Nguyen, Khang and Le, An Thai and Peters, Jan and Vu, Minh Nhat},
      journal       = {IEEE Robotics and Automation Letters},
      volume        = {11},
      number        = {2},
      pages         = {2162--2169},
      year          = {2026},
      doi           = {10.1109/LRA.2025.3648611},
      eprint        = {2506.12095},
      archivePrefix = {arXiv},
      primaryClass  = {cs.RO}
    }
  10. Representative paper: FoldQuantVLA: Native Low-Bit Quantization of Vision-Language-Action Models via Consistent Folding

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

    FoldQuantVLA enables native low-bit VLA inference without retraining. W4A4 projections run 1.20–1.33× faster than floating-point TensorRT on Jetson AGX Orin. Selective INT8 improves action fidelity and measured success on real robots.

    Preprint · 2026arXivProject pageCode
    BibTeX
    @misc{ho2026foldquantvla,
      title         = {FoldQuantVLA: Native Low-Bit Quantization of Vision-Language-Action Models via Consistent Folding},
      author        = {Ho, Hung T. and Nguyen, Khanh D. and Nguyen, Quang D. and Duong, Thanh Q. and Le, Ngan and Guo, Meng and Ngo, Vien A. and Le, An T.},
      year          = {2026},
      eprint        = {2609.24433},
      archivePrefix = {arXiv},
      primaryClass  = {cs.RO},
      url           = {https://arxiv.org/abs/2609.24433}
    }
    FoldQuantVLA: consistent folding and native low-bit policy execution
  11. Representative paper: vla.simd: Efficient CPU Inference for Language-Conditioned Manipulation

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

    vla.simd runs VLA policies on CPUs with shared SIMD kernels, demonstrated on SO-101 and UR10e. On Raspberry Pi 5, IMPACT supplies 33.5 actions/s in FP32 after 90 seconds of sustained operation. Feedback frequency is lower than action throughput.

    Preprint · 2026arXivProject pageCode
    BibTeX
    @misc{nguyen2026vlasimd,
      title         = {vla.simd: Efficient CPU Inference for Language-Conditioned Manipulation},
      author        = {Nguyen, Khanh D. and Truong, Hoang M. and Le, An T.},
      year          = {2026},
      eprint        = {2609.24274},
      archivePrefix = {arXiv},
      primaryClass  = {cs.RO},
      url           = {https://arxiv.org/abs/2609.24274}
    }
    vla.simd: shared SIMD micro-kernels for policy inference on CPUs
  12. Representative paper: Training Non-Differentiable Networks via Optimal Transport PolyStep

    An Thai Le

    PolyStep trains non-differentiable networks with forward evaluations alone. Optimal transport weights candidate parameter updates. We compare gradient-free methods with matched evaluation budgets.

    BibTeX
    @article{le2026training,
      title         = {Training Non-Differentiable Networks via Optimal Transport},
      author        = {Le, An T.},
      journal       = {Transactions on Machine Learning Research},
      issn          = {2835-8856},
      year          = {2026},
      url           = {https://openreview.net/forum?id=8mlcqTTMuU},
      eprint        = {2605.01928},
      archivePrefix = {arXiv},
      primaryClass  = {cs.LG}
    }
  13. Representative paper: Observer-robust energy condition verification for warp drive spacetimes warpax

    An Thai Le

    warpax uses automatic differentiation in JAX to check energy conditions in warp drive spacetimes. It combines 4×4 matrix inequalities over all observers at a point with interval bounds for certified decisions.

    Class. Quantum Grav. 2026DOIarXivCode
    BibTeX
    @article{le2026observer,
      title         = {Observer-robust energy condition verification for warp drive spacetimes},
      author        = {Le, An T.},
      journal       = {Classical and Quantum Gravity},
      year          = {2026},
      month         = oct,
      doi           = {10.1088/1361-6382/aeb25a},
      eprint        = {2602.18023},
      archivePrefix = {arXiv},
      primaryClass  = {gr-qc}
    }
  14. Representative paper: Start Right, Arrive Right: Asynchronous Execution via Initial Noise Selection PAINT

    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

    PAINT selects initial noise through flow inversion to connect action chunks smoothly during asynchronous execution. It needs no retraining or inference-time gradients. We evaluate it on 12 simulated benchmarks and 6 real manipulation tasks.

    CoRL 2026 · RSS 2026 Diff4RL Workshop (Spotlight)arXivProject pageRepositoryWorkshop
    BibTeX
    @inproceedings{ho2026start,
      title         = {Start Right, Arrive Right: Asynchronous Execution via Initial Noise Selection},
      author        = {Ho, Trong-Bao and Nguyen, Quang-Tan and Ha, Thien-Loc and Nguyen, Gia-Binh and Nguyen, Viet-Thanh and Dinh, Long and Vu, Minh N. and Nguyen, Duy M. H. and Le, An Thai and Ngo, Vien Anh},
      booktitle     = {Conference on Robot Learning (CoRL)},
      year          = {2026},
      eprint        = {2606.19774},
      archivePrefix = {arXiv},
      primaryClass  = {cs.RO},
      url           = {https://arxiv.org/abs/2606.19774}
    }
  15. Representative paper: FOCA: Future-Oriented Conditioning for Data-Efficient Vision-Language-Action Adaptation

    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*

    FOCA learns from future interactions and goals to adapt VLA policies with few demonstrations. It reaches 95.7% success on LIBERO with 20 demonstrations per task. We also test it in RoboCasa and on real robots.

    BibTeX
    @inproceedings{nguyen2026foca,
      title         = {{FOCA}: Future-Oriented Conditioning for Data-Efficient Vision-Language-Action Adaptation},
      author        = {Nguyen, Duc Minh and Diep, Nghiem Tuong and Nguyen, Binh Gia and Ho, Trong-Bao and Le, Doanh and Nguyen, Tan Q. and Ha, Thien-Loc and Tran, Nhiem and Thach, Bao and Tran, Nhat X. and Tran, Tuan A. and Habuda, Artur and M{\o}ller, Philip Lund and Le, Tran Nguyen and Sonntag, Daniel and Niepert, Mathias and Doan, Khoa D. and Duong, Vu and Ngo, Hung and Vu, Minh N. and Nguyen, Duy M. H. and Le, An Thai and Ngo, Vien Anh},
      booktitle     = {International Conference on Machine Learning (ICML)},
      series        = {Proceedings of Machine Learning Research},
      volume        = {306},
      publisher     = {PMLR},
      year          = {2026},
      eprint        = {2606.20867},
      archivePrefix = {arXiv},
      primaryClass  = {cs.CV},
      url           = {https://arxiv.org/abs/2606.20867}
    }
  16. CLOT: Multi-Robot Motion Planning via Collaborative Optimal Transport under Signal Temporal Logic Tasks

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

    CLOT uses collaborative optimal transport to plan multi-robot trajectories for tasks specified in signal temporal logic. It updates trajectories without gradients, with tests in simulation and on hardware.

    ICRA 2026PDF
    BibTeX
    @inproceedings{zhang2026clot,
      title     = {{CLOT}: Multi-robot Motion Planning via Collaborative Optimal Transport under Signal Temporal Logic Tasks},
      author    = {Zhang, Ying and Zhang, Yunyi and Le, An T. and Guo, Meng},
      booktitle = {IEEE International Conference on Robotics and Automation (ICRA)},
      year      = {2026},
      url       = {https://mengguo.github.io/personal_site/papers/pdf/zhang2026clot.pdf}
    }
  17. StructSAM: Structure- and Spectrum-Preserving Token Merging for Segment Anything Models

    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

    StructSAM merges redundant tokens in Segment Anything's encoder while preserving boundaries and prompt regions. We measure the tradeoff between computation and segmentation accuracy.

    ICML 2026 Workshop (AdaptFM)arXivOpenReviewWorkshop
    BibTeX
    @inproceedings{nguyen2026structsam,
      title         = {{StructSAM}: Structure- and Spectrum-Preserving Token Merging for Segment Anything Models},
      author        = {Nguyen, Duy M. H. and Tran, Tuan A. and Nguyen, Duong and Xie, Siwei and Nguyen, Trung Q. and Truong, Mai T. N. and Palenicek, Daniel and Le, An T. and Barz, Michael and Hannus, Eric and Nguyen, TrungTin and Dam, Tuan and Le, Tran Nguyen and Le, Ngan and Vu, Minh and Doan, Khoa and Ngo, Vien and Xie, Pengtao and Zou, James and Sonntag, Daniel and Peters, Jan and Niepert, Mathias},
      booktitle     = {ICML 2026 Workshop on Resource-Adaptive Foundation Model Inference (AdaptFM)},
      year          = {2026},
      url           = {https://openreview.net/forum?id=K41rNB0F8j},
      eprint        = {2603.07307},
      archivePrefix = {arXiv},
      primaryClass  = {cs.CV}
    }
  18. Rarity of rocket-driven Penrose extraction in Kerr spacetime Penrose rocket

    An Thai Le

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

    Phys. Rev. D 2026DOIarXivCode
    BibTeX
    @article{le2026rarity,
      title         = {Rarity of rocket-driven {Penrose} extraction in {Kerr} spacetime},
      author        = {Le, An T.},
      journal       = {Physical Review D},
      volume        = {113},
      number        = {12},
      pages         = {124036},
      year          = {2026},
      month         = jun,
      doi           = {10.1103/f3jn-7wcp},
      eprint        = {2601.19616},
      archivePrefix = {arXiv},
      primaryClass  = {astro-ph.HE}
    }
  19. Anytime Global Tensor Motion Planning Anytime GTMP

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

    Anytime GTMP uses black-box local planners to find more paths and reduce their cost as computation continues. Asymptotic optimality holds under stated clearance and sampling assumptions.

    Under review · 2026arXivCode
    BibTeX
    @misc{coumar2026anytime,
      title         = {Anytime Global Tensor Motion Planning},
      author        = {Coumar, Sai and Le, An T. and Kingston, Zachary},
      year          = {2026},
      eprint        = {2608.25830},
      archivePrefix = {arXiv},
      primaryClass  = {cs.RO},
      url           = {https://arxiv.org/abs/2608.25830}
    }
  20. TD-MTP: Coupling Structured Planning with Multi-Step Value Learning

    We test how tensor-graph planning interacts with one-step and TD(λ) value targets across ten paired tasks. On humanoid stairs, each change alone lowers return, but the combination improves on the baseline. Clear evidence of this interaction appears on two of the ten tasks.

    Under review · 2026Project page
    BibTeX
    @misc{tdmtp2026,
      title        = {{TD-MTP}: Coupling Structured Planning with Multi-Step Value Learning},
      author       = {Anonymous},
      year         = {2026},
      howpublished = {\url{https://tdmtp.github.io/}},
      note         = {Under review}
    }
  21. EquiVLA: A General Framework for Rotationally Equivariant Vision-Language-Action Models

    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

    EquiVLA makes actions rotate with the input in pretrained VLA models. The perception module is approximately equivariant; the action head is exactly equivariant. We test it with GR00T N1.5 on LIBERO.

    Under review · 2026arXivProject page
    BibTeX
    @misc{ha2026equivla,
      title         = {{EquiVLA}: A General Framework for Rotationally Equivariant Vision-Language-Action Models},
      author        = {Ha, Thien-Loc and Nguyen, Quang-Tan and Ho, Trong-Bao and Dinh, Long and Nguyen, Minh Duc and Nguyen, Gia-Binh and Pham, Tri Quang and Vu, Minh N. and Nguyen, Duy M. H. and Le, An Thai and Ngo, Vien Anh},
      year          = {2026},
      eprint        = {2606.19784},
      archivePrefix = {arXiv},
      primaryClass  = {cs.RO},
      url           = {https://arxiv.org/abs/2606.19784}
    }
  22. vla.cpp: A Unified Inference Runtime for Vision-Language-Action Models

    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

    vla.cpp runs eleven VLA models in a C++ runtime for onboard inference. In the reported test, BitVLA completes all 200 simulated LIBERO-Object episodes with inference on an 8 GB Jetson Orin Nano.

    Under review · 2026arXivProject pageCode
    BibTeX
    @misc{nguyen2026vlacpp,
      title         = {{vla.cpp}: A Unified Inference Runtime for Vision-Language-Action Models},
      author        = {Nguyen, Khanh D. and Ho, Hung T. and Nguyen, Chinh T. and Duong, Thanh Q. and Le, Linh D. and Nguyen, Duy M. H. and Ngo, Vien A. and Le, An T.},
      year          = {2026},
      eprint        = {2606.08094},
      archivePrefix = {arXiv},
      primaryClass  = {cs.RO},
      url           = {https://arxiv.org/abs/2606.08094}
    }
  23. Finetuning Vision-Language-Action Models Requires Fewer Layers Than You Think CLP

    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

    CLP removes redundant VLA layers before fine-tuning, using one pass to measure representation similarity. We study how model depth and training time affect task performance.

    Under review · 2026arXivProject pageCode
    BibTeX
    @misc{nguyen2026finetuning,
      title         = {Finetuning Vision-Language-Action Models Requires Fewer Layers Than You Think},
      author        = {Nguyen, Gia-Binh and Ho, Trong-Bao and Ha, Thien-Loc and Vo, Khoa and M{\o}ller, Philip Lund and Nguyen, Quang T. and Dinh, Long and Luu, Tung M. and Dam, Tuan and Duong, Vu and Le, Trung and Bui, Nghi D. Q. and Vu, Minh and Le, Tran Nguyen and Le, An Thai and Le, Ngan and Sonntag, Daniel and Zou, James and Peters, Jan and Nguyen, Duy M. H. and Ngo, Vien Anh},
      year          = {2026},
      eprint        = {2606.20246},
      archivePrefix = {arXiv},
      primaryClass  = {cs.RO},
      url           = {https://arxiv.org/abs/2606.20246}
    }
  24. Self-Improving VLA Policies: Selected Diffusion Noise for Spurious-Robust Action Smoothing SDN

    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

    SDN selects initial noise for a frozen VLA. It filters for action candidates grounded in objects, then executes the smoothest candidate. Tests cover simulation and real robots, without policy retraining.

    Under review · 2026arXiv
    BibTeX
    @misc{nguyen2026self,
      title         = {Self-Improving {VLA} Policies: Selected Diffusion Noise for Spurious-Robust Action Smoothing},
      author        = {Nguyen, Duc Minh and Dao, Bao-Ngoc and Luu, Tung M. and Nguyen, Binh Gia and Tong, Vinh and Liu, Anji and Duong, Vu N. and Le, Dung D. and Sonntag, Daniel and Le, Trung and Le, Ngan and Peters, Jan and Le, An Thai and Vu, Minh Nhat and Niepert, Mathias and Doan, Khoa D. and Nguyen, Duy M. H. and Ngo, Vien Anh},
      year          = {2026},
      eprint        = {2606.14084},
      archivePrefix = {arXiv},
      primaryClass  = {cs.RO},
      url           = {https://arxiv.org/abs/2606.14084}
    }
  25. RoboGaze: Evaluating Robot World Models via Structured Vision-Language Analysis

    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

    RoboGaze uses structured vision-language analysis to classify errors in generated robot videos and identify when they occur. It requires no evaluator training.

    Under review · 2026arXivProject pageCode
    BibTeX
    @misc{nguyen2026robogaze,
      title         = {{RoboGaze}: Evaluating Robot World Models via Structured Vision-Language Analysis},
      author        = {Nguyen, Minh-Loi and Diep, Nghiem Tuong and Nguyen, Hung Khang and Le, Minh and Le, Doanh Thien and Tran, Hoang H. and Le, Dung D. and Duong, Vu N. and Sonntag, Daniel and Le, An Thai and Nguyen, Duy Minh Ho and Ngo, Vien Anh and Tran, Van Nhiem},
      year          = {2026},
      eprint        = {2606.28385},
      archivePrefix = {arXiv},
      primaryClass  = {cs.RO},
      url           = {https://arxiv.org/abs/2606.28385}
    }
  26. FINE: Future-Informed Navigation Encoding for Data-Efficient Vision-Language Navigation

    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

    FINE learns navigation from future observations in demonstrations. Tokens predict upcoming landmarks' appearance and geometry, and distinguish reached states from world-model alternatives. Gains on R2R-CE and RxR-CE grow with fewer demonstrations. This supervision adds no computation at deployment.

    Preprint · 2026arXivProject page
    BibTeX
    @misc{nguyen2026fine,
      title         = {{FINE}: Future-Informed Navigation Encoding for Data-Efficient Vision-Language Navigation},
      author        = {Nguyen, Khang H. and Nguyen, Hoang Pham Quang and Nguyen, Ha Phuong and Binh, Khanh Dinh and Nguyen, Xuan Ha and Ngo, Vien Anh and Nguyen, Duy M. H. and Nguyen, Huan and Le, An Thai},
      year          = {2026},
      eprint        = {2609.32855},
      archivePrefix = {arXiv},
      primaryClass  = {cs.RO},
      url           = {https://arxiv.org/abs/2609.32855}
    }
  27. Retrieve to Act: Motion Primitive Graph Retrieval for Robust VLA Execution R2A

    Pham Tri Quang, Bao-Ngoc Dao, Minh Duc Nguyen, Duc-Duy Nguyen, Hung T. Ho, Linh Dang Le, Trong-Bao Ho, Thien-Loc Ha, Tan Q. Nguyen, Tuan Dam, Vu N. Duong, Daniel Sonntag, Ngan Le, Khoa D. Doan, Jan Peters, An Thai Le, Duy M. H. Nguyen, Vien Anh Ngo

    R2A retrieves motions from successful demonstrations and blends them with a frozen VLA's actions. Tests use GR00T N1.7, π0.5, and StableVLA on LIBERO-Plus, RoboTwin2.0-Plus, and a real Aloha robot. Reported success gains reach 12 percentage points in simulation and 40 on hardware, without fine-tuning the base policies.

    Preprint · 2026arXivProject page
    BibTeX
    @misc{r2a2026,
      title         = {Retrieve to Act: Motion Primitive Graph Retrieval for Robust {VLA} Execution},
      author        = {Pham, Tri Quang and Dao, Bao-Ngoc and Nguyen, Minh Duc and Nguyen, Duc-Duy and Ho, Hung T. and Le, Linh Dang and Ho, Trong-Bao and Ha, Thien-Loc and Nguyen, Tan Q. and Dam, Tuan and Duong, Vu N. and Sonntag, Daniel and Le, Ngan and Doan, Khoa D. and Peters, Jan and Le, An Thai and Nguyen, Duy M. H. and Ngo, Vien Anh},
      year          = {2026},
      eprint        = {2610.37215},
      archivePrefix = {arXiv},
      primaryClass  = {cs.RO},
      url           = {https://arxiv.org/abs/2610.37215}
    }
  28. AAC: Admissible-by-Architecture Differentiable Landmark Compression for ALT

    An Thai Le, V.A. Ngo

    AAC compresses landmark heuristics for ALT graph search while preserving admissibility: the heuristic never overestimates the remaining path cost. We test it on road networks against fixed landmark-selection baselines.

    TMLR 2026arXivCode
    BibTeX
    @article{le2026aac,
      title         = {{AAC}: Admissible-by-Architecture Differentiable Landmark Compression for {ALT}},
      author        = {Le, An T. and Ngo, Vien},
      journal       = {Transactions on Machine Learning Research},
      year          = {2026},
      eprint        = {2604.20744},
      archivePrefix = {arXiv},
      primaryClass  = {cs.AI},
      url           = {https://arxiv.org/abs/2604.20744}
    }

2025

  1. Representative paper: Model Tensor Planning MTP

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

    MTP combines global candidates from layered graphs with local trajectory sampling for model predictive control. It batches simulator rollouts in MuJoCo XLA and is available in hydrax.

    TMLR 2025, ICLR 2026 (J2C) · ICRA 2025 RoboARCH Workshop (lightning talk)OpenReviewCodeWorkshop
    BibTeX
    @article{le2025model,
      title         = {Model Tensor Planning},
      author        = {Le, An Thai and Nguyen, Khai and Vu, Minh Nhat and Carvalho, Jo{\~a}o and Peters, Jan},
      journal       = {Transactions on Machine Learning Research},
      issn          = {2835-8856},
      year          = {2025},
      url           = {https://openreview.net/forum?id=fk1ZZdXCE3},
      eprint        = {2505.01059},
      archivePrefix = {arXiv},
      primaryClass  = {cs.RO}
    }
  2. Representative paper: Global Tensor Motion Planning GTMP

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

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

    IEEE RA-L 2025, ICRA 2026DOIarXivCode
    BibTeX
    @article{le2025global,
      title         = {Global Tensor Motion Planning},
      author        = {Le, An Thai and Pompetzki, Kay and Carvalho, Jo{\~a}o and Watson, Joe and Urain, Julen and Biess, Armin and Chalvatzaki, Georgia and Peters, Jan},
      journal       = {IEEE Robotics and Automation Letters},
      volume        = {10},
      number        = {7},
      pages         = {7302--7309},
      year          = {2025},
      doi           = {10.1109/LRA.2025.3575307},
      eprint        = {2411.19393},
      archivePrefix = {arXiv},
      primaryClass  = {cs.RO}
    }
  3. Representative paper: Motion Planning Diffusion: Learning and Adapting Robot Motion Planning with Diffusion Models MPD

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

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

    IEEE T-RO 2025, AAAI 2026 (journal track)DOIarXivCode
    BibTeX
    @article{carvalho2025motion,
      title         = {Motion Planning Diffusion: Learning and Adapting Robot Motion Planning With Diffusion Models},
      author        = {Carvalho, Jo{\~a}o and Le, An Thai and Kicki, Piotr and Koert, Dorothea and Peters, Jan},
      journal       = {IEEE Transactions on Robotics},
      volume        = {41},
      pages         = {4881--4901},
      year          = {2025},
      doi           = {10.1109/TRO.2025.3593109},
      eprint        = {2412.19948},
      archivePrefix = {arXiv},
      primaryClass  = {cs.RO}
    }
  4. FlowMP: Learning Motion Fields for Robot Planning with Conditional Flow Matching

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

    FlowMP uses second-order conditional flow matching to learn acceleration-aware motion fields for smooth robot trajectories.

    IROS 2025arXivCode
    BibTeX
    @inproceedings{nguyen2025flowmp,
      title         = {{FlowMP}: Learning Motion Fields for Robot Planning with Conditional Flow Matching},
      author        = {Nguyen, Khang and Le, An Thai and Pham, Tien and Huber, Manfred and Peters, Jan and Vu, Minh Nhat},
      booktitle     = {IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
      pages         = {11291--11297},
      year          = {2025},
      doi           = {10.1109/IROS60139.2025.11246537},
      eprint        = {2503.06135},
      archivePrefix = {arXiv},
      primaryClass  = {cs.RO}
    }
  5. Machine Learning with Physics Knowledge for Prediction: A Survey

    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

    We survey how physical knowledge informs model architectures, training objectives, and data. The review focuses on partial differential equations and open-source tools.

    TMLR 2025arXiv
    BibTeX
    @article{watson2025machine,
      title         = {Machine Learning with Physics Knowledge for Prediction: A Survey},
      author        = {Watson, Joe and Song, Chen and Weeger, Oliver and Gruner, Theo and Le, An Thai and Hansel, Kay and Hendawy, Ahmed and Arenz, Oleg and Trojak, Will and Cranmer, Miles and D'Eramo, Carlo and B{\"u}low, Fabian and Goyal, Tanmay and Peters, Jan and Hoffmann, Martin W.},
      journal       = {Transactions on Machine Learning Research},
      issn          = {2835-8856},
      year          = {2025},
      url           = {https://openreview.net/forum?id=ZiJYahyXLU},
      note          = {Survey Certification},
      eprint        = {2408.09840},
      archivePrefix = {arXiv},
      primaryClass  = {cs.LG}
    }

2024

  1. Structure-Aware E(3)-Invariant Molecular Conformer Aggregation Networks ConAN

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

    ConAN combines a 2D molecular graph with multiple 3D conformers through a differentiable fused Gromov–Wasserstein barycenter. Its molecular property predictions are invariant to rotations, translations, and reflections.

    ICML 2024arXivCode
    BibTeX
    @inproceedings{nguyen2024structure,
      title         = {Structure-Aware {E(3)}-Invariant Molecular Conformer Aggregation Networks},
      author        = {Nguyen, Duy Minh Ho and Lukashina, Nina and Nguyen, Tai and Le, An Thai and Nguyen, TrungTin and Ho, Nhat and Peters, Jan and Sonntag, Daniel and Zaverkin, Viktor and Niepert, Mathias},
      booktitle     = {International Conference on Machine Learning (ICML)},
      series        = {Proceedings of Machine Learning Research},
      volume        = {235},
      pages         = {37736--37760},
      publisher     = {PMLR},
      year          = {2024},
      url           = {https://proceedings.mlr.press/v235/nguyen24g.html},
      eprint        = {2402.01975},
      archivePrefix = {arXiv},
      primaryClass  = {cs.LG}
    }
  2. Dude: Dual Distribution-Aware Context Prompt Learning For Large Vision-Language Model

    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

    Dude adapts vision-language models from few examples. Unbalanced optimal transport aligns shared and LLM-generated class prompts with visual tokens.

    ACML 2024PMLRarXiv
    BibTeX
    @inproceedings{nguyen2024dude,
      title         = {{Dude}: Dual Distribution-Aware Context Prompt Learning For Large Vision-Language Model},
      author        = {Nguyen, Duy Minh Ho and Le, An Thai and Nguyen, Trung Quoc and Diep, Nghiem Tuong and Nguyen, Tai and Duong-Tran, Duy and Peters, Jan and Shen, Li and Niepert, Mathias and Sonntag, Daniel},
      booktitle     = {Asian Conference on Machine Learning (ACML)},
      series        = {Proceedings of Machine Learning Research},
      volume        = {260},
      pages         = {687--702},
      publisher     = {PMLR},
      year          = {2024},
      url           = {https://proceedings.mlr.press/v260/nguyen25c.html},
      eprint        = {2407.04489},
      archivePrefix = {arXiv},
      primaryClass  = {cs.CV}
    }
  3. Grasp Diffusion Network: Learning Grasp Generators from Partial Point Clouds with Diffusion Models in SO(3)×R³ GDN

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

    GDN generates grasp poses from partial point clouds using diffusion on SO(3)×R³ and collision-cost guidance. We test grasping from raw depth observations.

    Preprint · 2024arXiv
    BibTeX
    @misc{carvalho2024grasp,
      title         = {Grasp Diffusion Network: Learning Grasp Generators from Partial Point Clouds with Diffusion Models in {SO(3)}$\times${R}$^3$},
      author        = {Carvalho, Jo{\~a}o and Le, An Thai and Jahr, Philipp and Sun, Qiao and Urain, Julen and Koert, Dorothea and Peters, Jan},
      year          = {2024},
      eprint        = {2412.08398},
      archivePrefix = {arXiv},
      primaryClass  = {cs.RO}
    }

2023

  1. Representative paper: Accelerating Motion Planning via Optimal Transport MPOT

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

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

    NeurIPS 2023arXivCode
    BibTeX
    @inproceedings{le2023accelerating,
      title         = {Accelerating Motion Planning via Optimal Transport},
      author        = {Le, An Thai and Chalvatzaki, Georgia and Biess, Armin and Peters, Jan},
      booktitle     = {Advances in Neural Information Processing Systems (NeurIPS)},
      volume        = {36},
      pages         = {78453--78482},
      publisher     = {Curran Associates, Inc.},
      year          = {2023},
      doi           = {10.52202/075280-3430},
      eprint        = {2309.15970},
      archivePrefix = {arXiv},
      primaryClass  = {cs.RO}
    }
  2. Hierarchical Policy Blending As Optimal Transport HiPBOT

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

    HiPBOT uses unbalanced optimal transport to blend reactive Riemannian motion policies. A look-ahead layer selects the blending weights.

    L4DC 2023Proceedings
    BibTeX
    @inproceedings{le2023hierarchical,
      title         = {Hierarchical Policy Blending As Optimal Transport},
      author        = {Le, An Thai and Hansel, Kay and Peters, Jan and Chalvatzaki, Georgia},
      booktitle     = {Learning for Dynamics and Control Conference (L4DC)},
      series        = {Proceedings of Machine Learning Research},
      volume        = {211},
      pages         = {797--812},
      publisher     = {PMLR},
      year          = {2023},
      url           = {https://proceedings.mlr.press/v211/le23a.html},
      eprint        = {2212.01938},
      archivePrefix = {arXiv},
      primaryClass  = {cs.RO}
    }
  3. Motion Planning Diffusion: Learning and Planning of Robot Motions with Diffusion Models MPD

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

    This earlier version of MPD learns diffusion priors over robot trajectories. Denoising generates motion plans conditioned on a goal.

    IROS 2023arXiv
    BibTeX
    @inproceedings{carvalho2023motion,
      title         = {Motion Planning Diffusion: Learning and Planning of Robot Motions with Diffusion Models},
      author        = {Carvalho, Jo{\~a}o and Le, An Thai and Baierl, Mark and Koert, Dorothea and Peters, Jan},
      booktitle     = {IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
      pages         = {1916--1923},
      year          = {2023},
      doi           = {10.1109/IROS55552.2023.10342382},
      eprint        = {2308.01557},
      archivePrefix = {arXiv},
      primaryClass  = {cs.RO}
    }
  4. Learning to reason over scene graphs: a case study of finetuning GPT-2 into a robot language model for grounded task planning

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

    We fine-tune GPT-2 on scene graphs to turn long-horizon requests into subgoals for a planner. Tests use the ALFRED benchmark.

    Frontiers in Robotics and AI 2023arXiv
    BibTeX
    @article{chalvatzaki2023learning,
      title         = {Learning to Reason over Scene Graphs: A Case Study of Finetuning {GPT-2} into a Robot Language Model for Grounded Task Planning},
      author        = {Chalvatzaki, Georgia and Younes, Ali and Nandha, Daljeet and Le, An Thai and Ribeiro, Leonardo F. R. and Gurevych, Iryna},
      journal       = {Frontiers in Robotics and AI},
      volume        = {10},
      pages         = {1221739},
      year          = {2023},
      doi           = {10.3389/frobt.2023.1221739},
      eprint        = {2305.07716},
      archivePrefix = {arXiv},
      primaryClass  = {cs.RO}
    }

2022

  1. Learning Implicit Priors for Motion Optimization StochGPMP

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

    We learn energy-based motion priors to initialize trajectory optimization or guide it through cost factors. Tests cover simulation and a real robot.

    BibTeX
    @inproceedings{urain2022learning,
      title         = {Learning Implicit Priors for Motion Optimization},
      author        = {Urain, Julen and Le, An Thai and Lambert, Alexander and Chalvatzaki, Georgia and Boots, Byron and Peters, Jan},
      booktitle     = {IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
      pages         = {7672--7679},
      year          = {2022},
      doi           = {10.1109/IROS47612.2022.9981264},
      eprint        = {2204.05369},
      archivePrefix = {arXiv},
      primaryClass  = {cs.RO}
    }

2021

  1. Learning forceful manipulation skills from multi-modal human demonstrations

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

    The method learns insertion, sliding, and twisting from demonstrations of pose and force, using attractor-based impedance control. We demonstrate it in e-bike motor assembly.

    IROS 2021arXiv
    BibTeX
    @inproceedings{le2021learning,
      title         = {Learning Forceful Manipulation Skills from Multi-modal Human Demonstrations},
      author        = {Le, An Thai and Guo, Meng and van Duijkeren, Niels and Rozo, Leonel and Krug, Robert and Kupcsik, Andras G. and B{\"u}rger, Mathias},
      booktitle     = {IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
      pages         = {7770--7777},
      year          = {2021},
      doi           = {10.1109/IROS51168.2021.9636828},
      eprint        = {2109.04222},
      archivePrefix = {arXiv},
      primaryClass  = {cs.RO}
    }
  2. Hierarchical Human-Motion Prediction and Logic-Geometric Programming for Minimal Interference Human-Robot Tasks

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

    We combine hierarchical human-motion prediction with logic-geometric replanning to reduce interference in shared workspaces. Tests use the MoGaze dataset.

    IEEE RO-MAN 2021arXiv
    BibTeX
    @inproceedings{le2021hierarchical,
      title         = {Hierarchical Human-Motion Prediction and Logic-Geometric Programming for Minimal Interference Human-Robot Tasks},
      author        = {Le, An Thai and Kratzer, Philipp and Hagenmayer, Simon and Toussaint, Marc and Mainprice, Jim},
      booktitle     = {IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)},
      pages         = {7--14},
      year          = {2021},
      doi           = {10.1109/RO-MAN50785.2021.9515539},
      eprint        = {2104.08137},
      archivePrefix = {arXiv},
      primaryClass  = {cs.RO}
    }

Humanoid demos

At VinRobotics, our researchers and engineers test learned locomotion, whole-body control, and physical interaction on real humanoids.

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

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

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

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

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

Join us

Our group is based at VinUniversity in Hanoi, with close ties to VinRobotics and IAS at TU Darmstadt. If you enjoy understanding algorithms and testing them on robots, 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, then design experiments that test our assumptions. There is room to make mistakes, rethink an idea, and pursue your own direction. I encourage students to question mine, too.

Open positions and projects

How to apply

  • Email an@robot-learning.de with the subject [Join][<track>] <Your Name>. Use PhD, MSc, residency, or internship as the track.
  • Attach a CV and a transcript, and link to code you wrote.
  • Add a short paragraph about one of our papers (under 300 words). Describe an idea, a question, 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.

MSc students

VinRobotics residents

Trinh Thi Cuc Whole-body compliance
Dang Truong Duy Payload-robust locomotion
Nguyen Viet Duong Neuromorphic learning
Nguyen Dang Khanh Efficient VLA inference
Dao Duc Thinh Egocentric VLA learning
Truong Minh Hoang CPU inference & SIMD kernels
Nguyen Truong Chinh VLA inference runtimes

Alumni

Collaborators

Zachary Kingston Purdue University
Meng Guo Peking University
Ngo Anh Vien VinRobotics
Jan Peters TU Darmstadt
Georgia Chalvatzaki TU Darmstadt
Viet T. Nguyen University of Würzburg

Open source

Research code and deployment tools developed with students and collaborators, including our releases at CAIR, VinUniversity. Start with GTMP or MPOT for planning, vla.simd for CPU policy inference, or warpax for numerical relativity. Questions and bug reports are welcome; reproducible examples help us investigate.

cair-vinuni/vla.simd C++ inference for VLA policies with native SIMD kernels on x86, Apple Silicon, and Raspberry Pi 5. C++Stars: 24Forks: 0 cair-vinuni/FoldQuantVLA Native INT4 and INT8 inference for VLA models through consistent offline folding, with deployment on Jetson AGX Orin. PythonStars: 24Forks: 2 cair-vinuni/FOCA Adapts VLA policies from few demonstrations by learning from future interactions and goals. ICML 2026. PythonStars: 92Forks: 2 cair-vinuni/CLP_VLA Prunes redundant VLA layers before fine-tuning, with training and evaluation scripts for robot manipulation. Jupyter NotebookStars: 99Forks: 1 cair-vinuni/RoboGaze Diagnoses failures in videos generated by robot world models using structured vision-language analysis. PythonStars: 151Forks: 1 cair-vinuni/paint-action-chunking Project materials for PAINT: connecting action chunks through initial noise selection without retraining. CoRL 2026. Stars: 14Forks: 0 VinRobotics/vla.cpp C++ runtime for onboard inference with eleven vision-language-action models. C++Stars: 198Forks: 34 anindex/mpot Gradient-free trajectory optimization with optimal transport in PyTorch. NeurIPS 2023. PythonStars: 71Forks: 11 anindex/mtp Global and local trajectory sampling for model predictive control in JAX. TMLR 2025 & ICLR 2026. PythonStars: 44Forks: 2 anindex/gtmp Parallel motion planning with layered graphs and tensor operations in JAX. RA-L 2025 & ICRA 2026. PythonStars: 40Forks: 5 anindex/polystep Optimal-transport training for non-differentiable networks using only forward evaluations. TMLR 2026. PythonStars: 33Forks: 1 anindex/warpax Vectorized auto-diff in JAX for checking energy conditions in warp drive spacetimes. CQG 2026. PythonStars: 3Forks: 1 anindex/penrose_process Rocket-flyby simulations testing energy extraction and escape around rotating black holes. Phys. Rev. D 2026. PythonStars: 2Forks: 0 anindex/aac Learned compression of landmark heuristics for A* search, preserving admissibility by construction. PythonStars: 4Forks: 0 VinRobotics/vinrobotics_mjlab RL training pipeline for high-payload humanoid locomotion, built on MuJoCo Warp. PythonStars: 44Forks: 9 VinRobotics/model-quantization-recipes Quantization recipes for language and speech models, with deployment checks. PythonStars: 35Forks: 6 anindex/note_model_opt Notes and runnable examples on model deployment. Stars: 27Forks: 2 vincekurtz/hydrax (contributor) Sampling-based model predictive control on the GPU with JAX/MJX. Includes MTP. PythonStars: 304Forks: 53

Teaching & mentoring

I start with a small example, work through the mathematics, then implement it to test the assumptions. Our student projects and mentoring follow the same approach: learn to ask precise questions and judge the evidence for yourself.

Courses at TU Darmstadt

Open learning resources

Community & service

Editorial and reviewing roles

Associate Editor
IEEE RA-L, ICRA 2027
Area Chair
ICML 2027, ICLR 2027, CoRL 2026, RLC 2024
Conference reviewer
IROS, ICRA, RSS, L4DC, NeurIPS (Top Reviewer 2026), ICML, ICLR, AAAI
Journal reviewer
IEEE RA-L, IEEE T-RO, Robotics and Autonomous Systems, Neurocomputing, TMLR, Frontiers in Robotics and AI

Selected invited talks

Experience & education

  1. Oct 2025–present
    Hanoi, Vietnam

    Assistant Professor

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

  2. Oct 2025–present
    Darmstadt, Germany

    Visiting Professor

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

  3. Aug 2025–present
    Hanoi, Vietnam

    Director of Foundation AI

    • 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

    Thesis: Tensor Search Methods for Vectorizing Motion Planning, supervised by Prof. Jan Peters.

  5. 2019–2021
    Stuttgart, Germany

    MSc in Information Technology

    Thesis: Learning task-parameterized Riemannian motion policies, supervised by Dr. Jim Mainprice and Dr. Meng Guo. Graduated first class. Info-Preis for the best diploma, Sony Research Award, Deutschlandstipendium.

  6. May–Dec 2020
    Renningen, Germany

    Research Intern

    Forceful imitation learning for e-bike assembly, hosted by Dr. Meng Guo in the robotics team.

  7. Nov 2019–Apr 2020
    Stuttgart, Germany

    Research Assistant

    Configured and maintained HPC systems.

  8. 2015–2019
    Frankfurt, Germany

    BEng in Electrical Engineering and Information Technology

    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

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