RigidFormer: Learning Rigid Dynamics using Transformers
RigidFormer: Learning Rigid Dynamics using Transformers
This paper introduces RigidFormer, an object-centric Transformer model that learns high-fidelity rigid-body dynamics from mesh-free representations such as point clouds. The architecture uses compact anchors enriched with local vertex features via Anchor-Vertex Pooling to retain contact-relevant geometry without dense vertex-level interaction. It employs Anchor-based RoPE for spatial awareness while respecting the unordered nature of objects and enforces rigidity through 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. The authors also demonstrate a preliminary extension to command-conditioned articulated bodies by treating body parts as interacting object-level components.
The authors present RigidFormer, a Transformer-based model designed to simulate multi-object rigid-body dynamics using mesh-free inputs like point clouds, addressing limitations in existing mesh-dependent methods.
Source: arXiv Robotics — research abstracts · Read original article ↗
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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. Project page: htt
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Source：arXiv Robotics — research abstracts · arxiv.org