Showing 2026Show all
3 papers · 1 filter
cs.RO2026
Estimate, Don't Imitate: Reusing Differentiable State-Based Policies for Visuomotor Control
Denis Shcherba, Adrian Abel, Eckart Cobo-Briesewitz +3
Simulation-trained manipulation policies can exploit privileged state information to learn effective contact-rich behaviours, but deployment requires acting from partial observatio…
cs.RO2026
Combined Constrained Sampling and Reinforcement Learning for Robotic Manipulation
Marc Toussaint, Cornelius V. Braun, Armand Jordana +5
Training non-prehensile manipulation policies in contact-rich settings is a core challenge in robotics. While Reinforcement Learning (RL) has demonstrated its strength in such sett…
cs.RO2026
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…