Author

Nicolas Mansard

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#artificial intelligence Preprint Oct 2026

VAMPS: Visual and Motor Policies from Sampling-Based Planning

Learning robot policies directly on physical systems remains difficult because data collection is costly and policy exploration can be unsafe. We introduce Visual and Motor Policies from Sampling-Based Planning (VAMPS), a framework that uses Model Predictive Path Integral (MPPI) control to train reusable policies witho...

Mohamed Yassine Kabouri, Pietro Noah Crestaz, Q. Nguyen et al. · 0 citations

Preprint Sep 2026

CAST: Alternating State-Value Targets and Expanded Policy Gradients for Model-Based Reinforcement Learning

Model-based reinforcement learning (MBRL) is a family of RL methods that learn a model of the environment and use it for action selection, making it well suited to robotics due to its sample efficiency. Combining learned models with online planning can further improve action selection, as the planner can exploit the mo...

Pietro Noah Crestaz, Mohamed Yassine Kabouri, Nicolas Mansard et al. · 1 citation