等号制約マニフォールドの学習による運動計画
Learning Equality Constraints for Motion Planning on Manifolds
実演から深層ニューラルネットで等号制約のレベルセット関数を学習し、制約付きサンプリングベース運動計画に統合する手法ECoMaNNを提案した論文。
著者: Giovanni Sutanto, Isabel M. Rayas Fernández, Peter Englert, Ragesh K. Ramachandran, Gaurav S. Sukhatme
分類: cs.RO, cs.CG, cs.LG
原文アブストラクト
Constrained robot motion planning is a widely used technique to solve complex robot tasks. We consider the problem of learning representations of constraints from demonstrations with a deep neural network, which we call Equality Constraint Manifold Neural Network (ECoMaNN). The key idea is to learn a level-set function of the constraint suitable for integration into a constrained sampling-based motion planner. Learning proceeds by aligning subspaces in the network with subspaces of the data. We combine both learned constraints and analytically described constraints into the planner and use a projection-based strategy to find valid points. We evaluate ECoMaNN on its representation capabilities of constraint manifolds, the impact of its individual loss terms, and the motions produced when incorporated into a planner.
