Education

• Ph.D. in Robotics (2023 – 2026) — Inria Paris, ENS
  • Thesis: Samples That Matter: Principled Policy Learning for Robotics
• M.Sc. in Robotics, Cognition, Intelligence (2018 – 2021) — TUM
  • Graduated with distinction
• B.Sc. in Mechanical Engineering (2014 – 2018) — TUM

Work Experience

• Postdoctoral Researcher (2026 – present) — Inria Paris
  • Willow Team
• Research Engineer (Jan 2022 – Jan 2023) — Inria Paris
  • Willow Team
  • Core developer for Pinocchio and Proxsuite.
• Research Engineer (Jan 2021 – Jan 2022) — Wandercraft
  • Reinforcement learning for self-balancing exoskeleton.
  • Output: Patent US20250162141A1.

Research Internships

• Student Researcher (Apr 2020 – Dec 2020) — TUM
  • Dynamic Vision and Learning Group
  • Supervised by Prof. Laura Leal-Taixé.
• Visiting Researcher (Sep 2019 – Mar 2020) — University of Tokyo
  • Nakamura Lab
  • Supervised by Prof. Yoshihiko Nakamura.

Publications

OTRetarget: Joint Robot and Object Motion Retargeting via Optimal Transport

Guillaume Besset, Erwann Carn, Timothée Carecchio, Valentin Tordjman-Levavasseur, Fabian Schramm, Yann de Mont-Marin, Justin Carpentier, Ajay Suresha Sathya — arXiv preprint, 2026

Survival Reinforcement Learning: Toward Scalable Self-Supervised RL

Franki Nguimatsia-Tiofack, Fabian Schramm, Théotime Le Hellard, Justin Carpentier — arXiv preprint, 2026

SVL: Goal-Conditioned Reinforcement Learning as Survival Learning

Franki Nguimatsia Tiofack, Fabian Schramm, Théotime Le Hellard, Justin Carpentier — International Conference on Machine Learning (ICML), 2026

Variance-Reduced Model Predictive Path Integral via Quadratic Model Approximation

Fabian Schramm, Franki Nguimatsia Tiofack, Nicolas Perrin-Gilbert, Marc Toussaint, Justin Carpentier — Robotics: Science and Systems (RSS), 2026

Guided Flow Policy: Learning from High-Value Actions in Offline Reinforcement Learning

Franki Nguimatsia Tiofack*, Théotime Le Hellard*, Fabian Schramm*, Nicolas Perrin-Gilbert, Justin Carpentier
* Equal contribution — Fourteenth International Conference on Learning Representations (ICLR), 2026

First-order Sobolev Reinforcement Learning

Fabian Schramm, Nicolas Perrin-Gilbert, Justin Carpentier — Differentiable Systems and Scientific Machine Learning, EurIPS 2025, 2025

Reference-Free Sampling-Based Model Predictive Control

Fabian Schramm, Pierre Fabre, Nicolas Perrin-Gilbert, Justin Carpentier — IEEE International Conference on Robotics and Automation (ICRA), 2025

Sampling-Based Global Optimal Control and Estimation via Semidefinite Programming

Antoine Groudiev, Fabian Schramm, Éloïse Berthier, Justin Carpentier, Frederike Dümbgen — American Control Conference (ACC), 2025

PROXQP: an Efficient and Versatile Quadratic Programming Solver for Real-Time Robotics Applications and Beyond

Antoine Bambade, Fabian Schramm, Sarah El Kazdadi, Stéphane Caron, Adrien Taylor, Justin Carpentier — IEEE Transactions on Robotics, 2025

Highly-Efficient Differentiable Simulation for Robotics

Quentin Le Lidec, Louis Montaut, Yann de Mont-Marin, Fabian Schramm, Emilien Biré, Justin Carpentier — arXiv, 2024

Leveraging augmented-Lagrangian techniques for differentiating over infeasible quadratic programs in machine learning (Spotlight ICLR)

Antoine Bambade, Fabian Schramm, Adrien Taylor, Justin Carpentier — Twelfth International Conference on Learning Representations (ICLR), 2024

Leveraging Randomized Smoothing for Optimal Control of Nonsmooth Dynamical Systems

Quentin Le Lidec, Fabian Schramm, Louis Montaut, Cordelia Schmid, Ivan Laptev, Justin Carpentier — Nonlinear Analysis: Hybrid Systems, International Federation of Automatic Control (IFAC) journal, 2024

Reactive Stepping for Humanoid Robots using Reinforcement Learning: Application to Standing Push Recovery on the Exoskeleton Atalante

Alexis Duburcq, Fabian Schramm, Guilhem Boéris, Nicolas Bredeche, Yann Chevaleyre — IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2022