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Constrained robot motion planning is a widely used technique to solve complexrobot tasks. We consider the problem of learning representations of constraintsfrom demonstrations with a deep neural network, which we call EqualityConstraint Manifold Neural Network (ECoMaNN). The key idea is to learn alevel-set function of the constraint suitable for integration into aconstrained sampling-based motion planner. Learning proceeds by aligningsubspaces in the network with subspaces of the data. We combine both learnedconstraints and analytically described constraints into the planner and use aprojection-based strategy to find valid points. We evaluate ECoMaNN on itsrepresentation capabilities of constraint manifolds, the impact of itsindividual loss terms, and the motions produced when incorporated into aplanner.

Learning Equality Constraints for Motion Planning on Manifolds