← 研踪 PaperTrailarXiv:2611.16673 · v1
PIM: A Physical Intuition Model for Low-Latency Prediction of Object Interaction Outcomes
首次提交 2026-11-12 19:49
版本更新 2026-11-12 19:49
雷达观测 2026-11-13 14:15
摘要
Predicting object interaction outcomes from initial conditions is a central problem in data-driven physical reasoning. Existing methods often use rigid-body-level states or predict the evolution through autoregressive rollouts. Coarse representations cannot represent local deformation, while rollout errors may compound over long prediction horizons. Instead, we introduce PIM, a Physical Intuition Model that directly predicts a physical system's equilibrium state from its initial mesh geometry, kinematics, and physical attributes. PIM combines local k-nearest-neighbor (KNN) attention to encode intra-object geometry, anchor-based cross-object attention to model interactions, and a dual-branch decoder that predicts object-level translations and per-vertex residual displacements. Across MuJoCo scenarios involving both rigid and deformable interactions, PIM achieves lower vertex and centroid errors than the evaluated baselines. By predicting complete equilibrium states in a single forward pass, PIM substantially reduces latency relative to autoregressive learned models and MuJoCo rollouts under the evaluated protocols, enabling low-latency inference when only the final outcome is required.
作者备注：22 pages, 7 figures
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v1 · 2026-11-12 19:49 · 最新观测版本
PIM: A Physical Intuition Model for Low-Latency Prediction of Object Interaction Outcomes
Predicting object interaction outcomes from initial conditions is a central problem in data-driven physical reasoning. Existing methods often use rigid-body-level states or predict the evolution through autoregressive rollouts. Coarse representations cannot represent local deformation, while rollout errors may compound over long prediction horizons. Instead, we introduce PIM, a Physical Intuition Model that directly predicts a physical system's equilibrium state from its initial mesh geometry, kinematics, and physical attributes. PIM combines local k-nearest-neighbor (KNN) attention to encode intra-object geometry, anchor-based cross-object attention to model interactions, and a dual-branch decoder that predicts object-level translations and per-vertex residual displacements. Across MuJoCo scenarios involving both rigid and deformable interactions, PIM achieves lower vertex and centroid errors than the evaluated baselines. By predicting complete equilibrium states in a single forward pass, PIM substantially reduces latency relative to autoregressive learned models and MuJoCo rollouts under the evaluated protocols, enabling low-latency inference when only the final outcome is required.
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