RigidFormer: Learning Rigid Dynamics using Transformers

• URL: http://arxiv.org/abs/2605.09196v1
• Date: Sat, 09 May 2026 22:31:09 GMT
• Title: RigidFormer: Learning Rigid Dynamics using Transformers
• Abstract summary: We introduce an object-centric Transformer-based model that learns mesh-free rigid-body dynamics with controllable integration step sizes.<n>We propose Anchor-based RoPE to inject anchor geometry into attention while respecting the unordered nature of objects and anchors.<n>We also show a preliminary extension to command-conditioned articulated bodies by treating body parts as interacting object-level components.
• Score: 46.435066827926825
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Learning-based simulation of multi-object rigid-body dynamics remains difficult because contact is discontinuous and errors compound over long horizons. Most existing methods remain tied to mesh connectivity and vertex-level message passing, which limits their applicability to mesh-free inputs such as point clouds and leads to high computational cost. Efficiently modeling high-fidelity rigid-body dynamics from mesh-free representations, therefore, remains challenging. We introduce RigidFormer, an object-centric Transformer-based model that learns mesh-free rigid-body dynamics with controllable integration step sizes. RigidFormer reasons at the object level and advances each object through compact anchors; Anchor-Vertex Pooling enriches these anchors with local vertex features, retaining contact-relevant geometry without dense vertex-level interaction. We propose Anchor-based RoPE to inject anchor geometry into attention while respecting the unordered nature of objects and anchors: object-token processing is permutation-equivariant, and the mean-pooled anchor descriptor is invariant to anchor reindexing while preserving shape extent. RigidFormer further enforces rigidity by projecting updates onto the rigid-body manifold using differentiable Kabsch alignment. On standard benchmarks, RigidFormer outperforms or matches mesh-based baselines using point inputs, runs faster, generalizes to unseen point resolutions and across datasets, and scales to 200+ objects; we also show a preliminary extension to command-conditioned articulated bodies by treating body parts as interacting object-level components.

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