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arXiv · 2009.11852

Learning Equality Constraints for Motion Planning on Manifolds

Giovanni Sutanto·Isabel M. Rayas Fernández·Peter Englert·Ragesh K. Ramachandran·Gaurav S. Sukhatme
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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.

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Giovanni Sutanto, Isabel M. Rayas Fernández, Peter Englert, Ragesh K. Ramachandran, Gaurav S. Sukhatme. 2020-09-24. Learning Equality Constraints for Motion Planning on Manifolds. https://arxiv.org/abs/2009.11852

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