ChainSplat：一种基于物理的螺旋理论模型，用于从多视角RGB视频中学习可变形线性物体的动力学
ChainSplat: A Physics-Inspired Screw-Theoretic Model for Learning Deformable Linear Object Dynamics from Multi-View RGB Videos
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中文总结 AI 辅助
ChainSplat是一种物理启发式框架，仅从多视角RGB视频中联合学习可变形线性物体的3D几何、外观、运动学和动力学，在相关任务上达SOTA性能，支持实时估计与轨迹优化，具实用价值。
中文摘要 AI 辅助
识别电缆、绳索、软管等可变形线性物体（DLO）的底层动力学和3D几何，对精确的机器人操作至关重要，但由于其高维配置空间以及材料属性变化带来的多样行为，该任务仍具挑战性。现有方法常依赖多阶段流水线和辅助深度输入，在动态交互下易出错，且其高维状态表示使基于模型的控制计算成本高昂。本文提出ChainSplat，一种仅从多视角RGB视频中联合学习DLO的3D几何、外观、运动学和动力学的物理启发式框架。ChainSplat将DLO表示为通过旋转关节连接的刚性连杆开链结构，得到以关节配置为参数的紧凑状态表示的解析螺旋理论模型。通过将该公式与高斯溅射（Gaussian splatting）结合，ChainSplat可联合恢复DLO动力学、感知运动学的3D几何和外观，同时支持从任意状态生成高保真RGB渲染。通过真实世界实验，我们证明ChainSplat在动态交互下的动力学预测、3D几何重建和RGB渲染方面达到了最先进的性能。ChainSplat还支持实时状态和力估计，以及精确的基于模型的轨迹优化，凸显了其在真实世界DLO机器人操作中的实用价值。配套的源代码和视频可在该URL获取。
英文摘要
Identifying the underlying dynamics and 3D geometry of deformable linear objects (DLOs), such as cables, ropes, and hoses, is essential for accurate robotic manipulation, but remains challenging due to their high-dimensional configuration spaces and diverse behaviors arising from varying material properties. Existing methods often rely on multi-stage pipelines and auxiliary depth inputs, which are prone to errors under dynamic interactions, while their high-dimensional state representations make model-based control computationally expensive. In this paper, we introduce ChainSplat, a physics-inspired framework that jointly learns the 3D geometry, appearance, kinematics, and dynamics of DLOs solely from multi-view RGB videos. ChainSplat represents a DLO as an open-chain structure of rigid links connected by revolute joints, yielding an analytic, screw-theoretic model with a compact state representation parameterized by joint configurations. By integrating this formulation with Gaussian splatting, ChainSplat jointly recovers DLO dynamics, kinematics-aware 3D geometry, and appearance, while enabling high-fidelity RGB rendering from arbitrary states. Through real-world experiments, we demonstrate that ChainSplat achieves state-of-the-art performance in dynamics predictions, 3D geometry reconstruction, and RGB rendering across dynamic interactions. ChainSplat further enables real-time state and force estimation, as well as accurate model-based trajectory optimization, highlighting its practical utility for real-world robotic manipulation of DLOs. Accompanying source code and video are available at: https://chainsplat.github.io.
发表机构
- KTH Royal Institute of Technology（瑞典皇家理工学院）
机构由 AI 辅助整理，请以论文原文为准。
