Learning Equality Constraints for Motion Planning on Manifolds

• URL: http://arxiv.org/abs/2009.11852v1
• Date: Thu, 24 Sep 2020 17:54:28 GMT
• Title: Learning Equality Constraints for Motion Planning on Manifolds
• Authors: Giovanni Sutanto, Isabel M. Rayas Fern\'andez, Peter Englert, Ragesh K. Ramachandran, Gaurav S. Sukhatme
• Abstract summary: We consider the problem of learning representations of constraints from demonstrations with a deep neural network. The key idea is to learn a level-set function of the constraint suitable for integration into a constrained sampling-based motion planner. We combine both learned constraints and analytically described constraints into the planner and use a projection-based strategy to find valid points.
• Score: 10.65436139155865
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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