Localized Graph-Based Neural Dynamics Models for Terrain Manipulation

2025-03-30

AI Summary

To address the challenge of dynamical modeling for high-dimensional, unbounded, fine-grained terrains in construction and extraterrestrial scenarios, this paper proposes a lightweight graph neural dynamics (GND) modeling approach. The method employs a learning-driven, adaptive region-of-interest (RoI) identification mechanism that selectively predicts dynamics only for ~100 particles within the robot–terrain interaction zone. It further incorporates domain-boundary feature encoding and geometry-aware sparse subgraph activation to suppress particle penetration and preserve local dynamical fidelity. Compared to full-graph GND models, the proposed method achieves several orders-of-magnitude speedup in prediction latency while improving accuracy. Extensive evaluation on multi-granularity terrain excavation and shaping tasks demonstrates its strong generalizability, real-time performance, and physical consistency.

Technology Category

• Intelligent Robots: State Estimation
• Machine Learning: Graph-based Machine Learning
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

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.

Problem

• Modeling high-dimensional terrain deformation for robot manipulation
• Reducing computation by localizing active subgraphs in large terrains
• Preventing particle penetration at domain boundaries during prediction

Innovation

• Graph-based Neural Dynamics for terrain modeling
• Localized active subgraph for efficient prediction
• Domain boundary feature encoding prevents penetration