<ul><li>This paper introduces a learning-based approach for terrain dynamics modeling and manipulation, leveraging the Graph-based Neural Dynamics (GBND) framework.</li><li>The approach builds a large terrain graph but only identifies a small active subgraph for predicting the outcomes of robot-terrain interaction.</li><li>A learning-based approach is introduced to identify a small region of interest (RoI) based on the robot's control inputs and the current scene.</li><li>The proposed method is faster and achieves better overall prediction accuracy compared to the naive GBND.</li></ul>

Localized Graph-Based Neural Dynamics Models for Terrain Manipulation

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