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Size Doesn't Matter: Material-State Reinforcement Learning for Excavator Transferable Soil Manipulation

arXiv · AI, language, vision and robotics · article · Sep 11, 2026 · UTC

Earthmoving tasks such as excavation, backfilling, or embankment construction require deliberate repositioning of deformable soil. For these tasks, human operators use all shovel faces, while autonomous systems so far are limited to excavation and dumping. Current methods often rely on heuristic models but do not incorporate soil mechanics. We address this shortcoming by using Reinforcement Learning in a GPU-parallelized Material Point Method particle simulation. Our controllers are conditioned on material state such as shape and compactness, enabling skills that use multiple contact faces of

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