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arXiv · 2006.07746

Learning Manifolds for Sequential Motion Planning

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

Motion planning with constraints is an important part of many real-world robotic systems. In this work, we study manifold learning methods to learn such constraints from data. We explore two methods for learning implicit constraint manifolds from data: Variational Autoencoders (VAE), and a new method, Equality Constraint Manifold Neural Network (ECoMaNN). With the aim of incorporating learned constraints into a sampling-based motion planning framework, we evaluate the approaches on their ability to learn representations of constraints from various datasets and on the quality of paths produced during planning.

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Isabel M. Rayas Fernández, Giovanni Sutanto, Peter Englert, Ragesh K. Ramachandran, Gaurav S. Sukhatme. 2020-06-13. Learning Manifolds for Sequential Motion Planning. https://arxiv.org/abs/2006.07746

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