News
- Jun 2026 Invited talk at ICRA 2026 Workshop on Robot Architecture on tensor search methods for motion planning.
- May 2026 Rarity of rocket-driven Penrose extraction in Kerr spacetime accepted at Physical Review D.
- Oct 2025 Started as Assistant Professor at VinUniversity and Visiting Professor at TU Darmstadt.
- Aug 2025 Joined VinRobotics as Director of Foundation AI, leading RL-for-locomotion, humanoid VLA architecture, and edge-deployment efforts.
- Aug 2025 Successfully defended my Ph.D. thesis on Tensor Search Methods for Vectorizing Motion Planning at TU Darmstadt! You can find the full thesis here. Endless thanks to Prof. Jan Peters for tolerating my mischief and supporting me throughout - I could not have asked for a better advisor. Huge gratitude to Prof. Siddhartha Srinivasa for immediately accepting to serve as external examiner, and to the rest of the committee for their time and feedback.
- Jul 2025 Model Tensor Planning accepted at TMLR 2025 and ICLR 2026 (J2C track). Thanks to the co-authors and reviewers for getting it there.
- Jul 2025 Global Tensor Motion Planning published in IEEE RA-L 2025 and accepted at ICRA 2026.
- Jun 2025 Motion Planning Diffusion published in IEEE T-RO 2025.
- May 2025 Invited talks at RMIT University, Rice University, and HUST on tensor search methods for motion planning.
- 2022 Joined the Intelligent Autonomous Systems (IAS) lab at TU Darmstadt as a Ph.D. student, supervised by Prof. Jan Peters.
Research
I try to scale planning to settings classical methods struggle with - long horizons, high-dimensional state spaces, large plan sets, multi-agent - by treating search as a batched tensor operation and by leaning on generative models where structure runs out. Most current work targets humanoid loco-manipulation.
Tensor Search & Batched Planning
Casting search and trajectory optimization as batched tensor operations on the GPU - the spine of my thesis and most of my recent planners.
Diffusion & Flow Matching for Motion
Using diffusion and flow matching as priors over trajectories and policies, especially when the solution landscape is multimodal and gradients alone are not enough.
Humanoid Loco-manipulation
Whole-body RL and model-based control for humanoids in contact-rich tasks. Ongoing - many things still fall over.
VLA / VLM for Robotics
Data-efficient adaptation of vision-language(-action) models for grasping and manipulation - currently chasing better grounding from fewer demonstrations.
Optimal Transport & Gradient Flows
Borrowing entropic OT and gradient-flow machinery to design planners, blend policies, and train networks where standard gradients break down.
Numerical General Relativity
A weekend hobby: JAX/PyTorch CUDA simulators for processes in curved spacetime - Kerr orbits, Penrose extraction, and warp-drive energy conditions.
Selected Publications
* indicates co-first or co-last authors. See also my Google Scholar profile.
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★ FeaturedTraining Non-differentiable Networks via Optimal Transport -
★ FeaturedAAC: Admissible-by-Architecture Differentiable Landmark Compression for ALT -
CLOT: Multi-Robot Motion Planning Via Collaborative Optimal Transport under Signal Temporal Logic Tasks -
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★ FeaturedObserver-robust energy condition verification for warp drive spacetimes -
Rarity of rocket-driven Penrose extraction in Kerr spacetime -
★ Featured -
★ Featured -
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DoublyAware: Dual Planning and Policy Awareness for Temporal Difference Learning in Humanoid Locomotion -
Machine Learning with Physics Knowledge for Prediction: A Survey -
FlowMP: Learning Motion Fields for Robot Planning with Conditional Flow Matching -
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Structure-Aware E(3)-Invariant Molecular Conformer Aggregation Networks -
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Learning to reason over scene graphs: a case study of finetuning GPT-2 into a robot language model for grounded task planning -
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Learning forceful manipulation skills from multi-modal human demonstrations -
Hierarchical Human-Motion Prediction and Logic-Geometric Programming for Minimal Interference Human-Robot Tasks
Experience
Visiting Professor
Co-advising MSc and PhD students at IAS on robot learning research.
Assistant Professor
Building a research group on efficient learning and planning for robotics loco-manipulation, designing fundamental algorithms and methods.
Director of Foundation AI
- RL stack for high-payload humanoid locomotion
- Humanoid VLA architecture and training recipe
- Model optimization and edge-deployment toolchain
Ph.D. in Computer Science
Thesis: Tensor Search Methods for Vectorizing Motion Planning - supervised by Prof. Jan Peters.
Research Intern
Worked on forceful imitation learning applied to E-bike assembly tasks, hosted by Dr. Meng Guo in the robotics team.
M.Sc. Information Technology
Thesis: Learning task-parameterized Riemannian motion policies - supervised by Dr. Jim Mainprice & Dr. Meng Guo. Graduated First class. Info-Preis for Best Diploma Award. Sony Research Award. Deutschlandstipendium.
Research Assistant
Implemented back-end functionalities in the DASH project; maintained and configured HPC systems.
B.Eng. Electrical Engineering and Information Technology
Thesis: Approaches to solve kidnapped robot problem. Graduated First class. DAAD Scholarship. AmCham Scholarship. eSilicon Scholarship.
Engineer Intern
Designed data analysis systems for high-volume manufacturing unit-test data; validated and reported quality of Intel Thunderbolt product manufacturing line.
Teaching
- Reinforcement Learning
- Statistical Machine Learning
- Probabilistic Methods for Computer Science
- Robot Learning Integrated Project / Expert Lab / Mechatronics
Current Collaborators
Academic Service
Reviewer - Conferences & Area Chair
Area Chair: RLC
Reviewer: IROS, ICRA, R:SS, CoRL, L4DC, NeurIPS, ICML, ICLR, AAAI
Reviewer - Journals
IEEE RA-L, IEEE T-RO, Neurocomputing, TMLR, Frontiers in Robotics and AI