DiffeoMorph: Learning Distributed 3D Morphogenesis with Rotation-Invariant Spectral Shape Matching
DiffeoMorph: Learning Distributed 3D Morphogenesis with Rotation-Invariant Spectral Shape Matching
This research introduces DiffeoMorph, an end-to-end differentiable framework that enables populations of agents to collectively morph into complex 3D shapes through distributed control.
The paper presents DiffeoMorph, a differentiable framework for learning distributed morphogenesis protocols in multi-agent systems using SE(3)-equivariant graph neural networks and a rotation-invariant spectral shape-matching loss based on 3D Zernike polynomials.
Source: arXiv Robotics — research abstracts · Read original article ↗
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Source：arXiv Robotics — research abstracts · arxiv.org