Learning Manifolds for Sequential Motion Planning

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.

Accepted for presentation at the Robotics: Science and Systems (RSS) 2020 Workshop for Learning (in) Task and Motion Planning. Paper length is 4 pages (i.e. 3 pages of technical content and 1 page of the references)