ステアラブル畳み込みによる微分可能計画への対称性の統合
Integrating Symmetry into Differentiable Planning with Steerable Convolutions
経路計画をグリッド上の信号として扱い、価値反復が回転・反射対称性を持つ線形等変演算子（ステアラブル畳み込み）であることを示し、VINを拡張して訓練効率と汎化性能を大幅に改善した。
著者: Linfeng Zhao, Xupeng Zhu, Lingzhi Kong, Robin Walters, Lawson L. S. Wong
分類: cs.LG, cs.AI, cs.RO
原文アブストラクト
We study how group symmetry helps improve data efficiency and generalization for end-to-end differentiable planning algorithms when symmetry appears in decision-making tasks. Motivated by equivariant convolution networks, we treat the path planning problem as \textit{signals} over grids. We show that value iteration in this case is a linear equivariant operator, which is a (steerable) convolution. This extends Value Iteration Networks (VINs) on using convolutional networks for path planning with additional rotation and reflection symmetry. Our implementation is based on VINs and uses steerable convolution networks to incorporate symmetry. The experiments are performed on four tasks: 2D navigation, visual navigation, and 2 degrees of freedom (2DOFs) configuration space and workspace manipulation. Our symmetric planning algorithms improve training efficiency and generalization by large margins compared to non-equivariant counterparts, VIN and GPPN.
