VAMPS: Visual and Motor Policies from Sampling-Based Planning

Excerpt

arXiv:2610.05331v1 Announce Type: cross Abstract: 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 without human demonstrations. VAMPS supports two training modes. For one-step proprioceptive policies, it operates iteratively in simul