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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.
Recommended citation: Duburcq et al. (2022). "Reactive Stepping for Humanoid Robots using Reinforcement Learning: Application to Standing Push Recovery on the Exoskeleton Atalante." IROS22. https://arxiv.org/abs/2203.01148
Antoine Bambade, Fabian Schramm, Adrien Taylor, Justin Carpentier
Published in Twelfth International Conference on Learning Representations (ICLR), 2023
This paper presents primal-dual augmented Lagrangian techniques for computing derivatives of both feasible and infeasible QPs.
Recommended citation: Bambade et al. (2023). "Leveraging augmented-Lagrangian techniques for differentiating over infeasible quadratic programs in machine learning." ICLR24. https://hal.laas.fr/PRAIRIE-IA/hal-04133055v1
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.
Recommended citation: Le Lidec et al. (2024). "Leveraging Randomized Smoothing for Optimal Control of Nonsmooth Dynamical Systems." NAHS24. https://arxiv.org/abs/2203.03986
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.
Recommended citation: Le Lidec et al. (2024). "End-to-End and Highly-Efficient Differentiable Simulation for Robotics." arXiv preprint arXiv:2409.07107. https://arxiv.org/pdf/2409.07107
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.
Recommended citation: Bambade et al. (2025). "PROXQP: an Efficient and Versatile Quadratic Programming Solver for Real-Time Robotics Applications and Beyond." IEEE Transactions on Robotics. https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=11027562
Antoine Groudiev, Fabian Schramm, Éloïse Berthier, Justin Carpentier, Frederike Dümbgen
Published in arXiv, 2025
This paper applies the Kernel Sum of Squares framework for global sampling-based optimal control and estimation via semidefinite programming.
Recommended citation: Groudiev et al. (2025). "KernelSOS for Global Sampling-Based Optimal Control and Estimation via Semidefinite Programming." arXiv preprint arXiv:2507.17572. https://arxiv.org/abs/2507.17572