Learning Equality Constraints for Motion Planning on Manifolds

Understand

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.