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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
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Posts
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Blog Post number 4
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Blog Post number 1
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publications
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
Published in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2022
This paper presents a reinforcement learning framework capable of learning robust standing push recovery for bipedal robots with a smooth out-of-the-box transfer to reality, requiring only instantaneous proprioceptive observations.
Leveraging Randomized Smoothing for Optimal Control of Nonsmooth Dynamical Systems
Quentin Le Lidec, Fabian Schramm, Louis Montaut, Cordelia Schmid, Ivan Laptev, Justin Carpentier
Published in Nonlinear Analysis: Hybrid Systems, International Federation of Automatic Control (IFAC) journal, 2024
This paper presents randomized smoothing to tackle non-smoothness issues commonly encountered in optimal control and provides key insights on the interplay between Reinforcement Learning and Optimal Control.
Leveraging augmented-Lagrangian techniques for differentiating over infeasible quadratic programs in machine learning (Spotlight ICLR)
Antoine Bambade, Fabian Schramm, Adrien Taylor, Justin Carpentier
Published in Twelfth International Conference on Learning Representations (ICLR), 2024
This paper presents primal-dual augmented Lagrangian techniques for computing derivatives of both feasible and infeasible QPs.
End-to-End and Highly-Efficient Differentiable Simulation for Robotics
Quentin Le Lidec, Louis Montaut, Yann de Mont-Marin, Fabian Schramm, Justin Carpentier
Published in arXiv, 2024
This paper introduces a unified and efficient method for computing analytical derivatives in robotics simulators.
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
Published in IEEE Transactions on Robotics, 2025
This paper presents ProxQP, a new and efficient QP solver for robotics and beyond.
KernelSOS for Global Sampling-Based Optimal Control and Estimation via Semidefinite Programming
Antoine Groudiev, Fabian Schramm, Éloïse Berthier, Justin Carpentier, Frederike Dümbgen
Published in American Control Conference (ACC), 2025
This paper applies the Kernel Sum of Squares framework for global sampling-based optimal control and estimation via semidefinite programming.
Reference-Free Sampling-Based Model Predictive Control
Fabian Schramm, Pierre Fabre, Nicolas Perrin-Gilbert, Justin Carpentier
Published in IEEE International Conference on Robotics and Automation (ICRA), 2025
We present a sampling-based model predictive control framework that enables emergent locomotion without relying on handcrafted gait patterns or predefined contact sequences.
First-order Sobolev Reinforcement Learning
Fabian Schramm, Nicolas Perrin-Gilbert, Justin Carpentier
Published in Differentiable Systems and Scientific Machine Learning, EurIPS 2025, 2025
We propose a refinement of temporal-difference learning that enforces first-order Bellman consistency by training the value function to match both Bellman targets and their derivatives.
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
Published in Fourteenth International Conference on Learning Representations (ICLR), 2026
We introduce Guided Flow Policy (GFP), which couples a multi-step flow-matching policy with a distilled one-step actor to focus on learning from high-value actions in offline reinforcement learning.