Learning Equality Constraints for Motion Planning on Manifolds
Giovanni Sutanto, Isabel Rayas Fernández, Peter Englert, Ragesh Kumar Ramachandran, Gaurav Sukhatme
Abstract
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.
BibTeX
@inproceedings{corl2020_learningequality,
title = {Learning Equality Constraints for Motion Planning on Manifolds},
author = {Giovanni Sutanto and Isabel Rayas Fernández and Peter Englert and Ragesh Kumar Ramachandran and Gaurav Sukhatme},
booktitle = {CoRL 2020},
year = {2020}
}