Learning Manifolds for Sequential Motion Planning

Understand

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.