Predictive models can be particularly helpful for robots to effectively manipulate terrains in construction sites and extraterrestrial surfaces. However, terrain state representations become extremely high-dimensional especially to capture fine-resolution details and when depth is unknown or unbounded. This paper introduces a learning-based approach for terrain dynamics modeling and manipulation, leveraging the Graph-based Neural Dynamics (GBND) framework to represent terrain deformation as motion of a graph of particles. Based on the principle that the moving portion of a terrain is usually localized, our approach builds a large terrain graph (potentially millions of particles) but only identifies a very small active subgraph (hundreds of particles) for predicting the outcomes of robot-terrain interaction. To minimize the size of the active subgraph we introduce a learning-based approach that identifies a small region of interest (RoI) based on the robot's control inputs and the current scene. We also introduce a novel domain boundary feature encoding that allows GBNDs to perform accurate dynamics prediction in the RoI interior while avoiding particle penetration through RoI boundaries. Our proposed method is both orders of magnitude faster than naive GBND and it achieves better overall prediction accuracy. We further evaluated our framework on excavation and shaping tasks on terrain with different granularity.

翻译：预测模型对于机器人有效操控建筑工地和地外星体表面的地形具有重要价值。然而，地形状态表征的维度极易膨胀，尤其在需要捕捉高分辨率细节且深度信息未知或无界的情况下。本文提出一种基于学习的地形动力学建模与操控方法，利用图基神经动力学（GBND）框架将地形形变表征为粒子图的运动。基于地形运动部分通常具有局部性的原理，本方法构建大规模地形图（可能包含数百万粒子），但仅识别极小的活动子图（数百粒子）来预测机器人与地形的交互结果。为最小化活动子图规模，我们提出一种基于学习的方法，根据机器人控制输入与当前场景识别小型感兴趣区域（RoI）。同时，我们引入新颖的域边界特征编码方法，使GBND能够在RoI内部实现精确动力学预测，并避免粒子穿透RoI边界。所提方法相较原始GBND实现数量级加速，且获得更优的整体预测精度。我们进一步在具有不同粒度分布的地形上，对挖掘与塑形任务进行了框架评估。