4 papers · 1 filter
Variance-Reduced Model Predictive Path Integral via Quadratic Model Approximation
Fabian Schramm, Franki Nguimatsia Tiofack, Nicolas Perrin-Gilbert +2
Sampling-based controllers, such as Model Predictive Path Integral (MPPI) methods, offer substantial flexibility but often suffer from high variance and low sample efficiency. To a…
Reference-Free Sampling-Based Model Predictive Control
Fabian Schramm, Pierre Fabre, Nicolas Perrin-Gilbert +1
We present a sampling-based model predictive control (MPC) framework that enables emergent locomotion without relying on handcrafted gait patterns or predefined contact sequences.…
Sampling-Based Global Optimal Control and Estimation via Semidefinite Programming
Antoine Groudiev, Fabian Schramm, Éloïse Berthier +2
Global optimization has gained attraction over the past decades, thanks to the development of both theoretical foundations and efficient numerical routines. Among recent advances,…
End-to-End and Highly-Efficient Differentiable Simulation for Robotics
Quentin Le Lidec, Louis Montaut, Yann de Mont-Marin +2
Over the past few years, robotics simulators have largely improved in efficiency and scalability, enabling them to generate years of simulated data in a few hours. Yet, efficiently…