发表机构
The Chinese University of Hong Kong, Shenzhen; DexForce Co., Ltd.; South China University of Technology; Shenzhen Loop Area Institute（香港中文大学（深圳）; 德矢力科技有限公司; 华南理工大学; 深圳河套学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对刚体交互模拟的局部接触特性，提出RiCo模型，通过接触表面点稀疏邻域建模交互，在MOVi基准上降低位置与方向误差，实现高接触保真度并具备零样本泛化能力。
AI 中文摘要
刚体交互的精确模拟对于预测物理世界模型至关重要。尽管近期在物体动力学建模方面取得了进展，但捕捉表面间的局部接触如何塑造物体运动仍然具有挑战性。端到端世界模型可预测整个场景或物体间的交互，但实际上，刚体接触本质上是局部的，只有附近的表面才能直接传递接触力。受这一观察结果的启发，我们提出了刚体接触推理（Rigid-body Contact Reasoning，RiCo），该模型通过接触表面点的稀疏邻域表示物体间的交互。RiCo将每个点的状态与附近表面的相对几何、运动和物理属性相结合，然后跨物体的点进行推理，以确定这些局部接触如何共同影响其运动。通过将跨物体推理限制在附近表面，同时在每个刚体内传播接触信息，RiCo在保留细粒度交互细节的同时，无需对场景中每一对点进行建模。这些特性使RiCo具有更高的精度和接触保真度。在MOVi基准测试上的实验表明，与基线方法相比，RiCo将100帧的位置误差降低了31%-35%，方向误差降低了约38%。此外，RiCo实现了高接触保真度，与真实值相比，穿透时间差异为11.0%，平均深度差异为2.22毫米。RiCo还能从小规模训练场景零样本泛化到包含270个物体的场景，我们的真实世界多球碰撞实验进一步为模拟到现实的迁移提供了初步证据。
英文摘要
Accurate simulation of rigid-body interactions is essential for predictive physical world models. Despite recent progress in modeling object dynamics, capturing how local contacts between surfaces shape object motion remains challenging. While end-to-end world models predict interactions across entire scenes or objects, in practice, rigid-body contact is inherently local, and only nearby surfaces can directly exchange contact forces. Motivated by this observation, we introduce Rigid-body Contact Reasoning (RiCo), which represents interactions between objects through sparse neighborhoods of contact surface points. RiCo combines each point's state with the relative geometry, motion, and physical properties of nearby surfaces, then reasons across the object's points to determine how these local contacts jointly affect its motion. By confining cross-object reasoning to nearby surfaces while propagating contact information within each rigid body, RiCo retains fine-grained interaction details without the cost of modeling every pair of scene points. Such properties enable RiCo a higher accuracy and contact fidelity. Experiments on MOVi-benchmark demonstrate that RiCo reduces 100-frame position and orientation errors by 31-35% and approximately 38%, respectively, compared with baselines. Moreover, RiCo achieves high contact fidelity, with ground-truth-relative penetration-time and mean-depth differences of 11.0% and 2.22 mm, respectively. RiCo further generalizes zero-shot from small-scale training scenarios to scenes containing 270 objects. Our real-world multi-ball collision experiments further provide preliminary evidence of sim-to-real transfer.
