Learning Manifolds for Sequential Motion Planning
This work addresses motion planning with constraints for robotic systems, but appears incremental as it builds on existing manifold learning methods.
The paper tackled the problem of learning constraint manifolds from data for motion planning in robotics, introducing a new method called ECoMaNN and evaluating it alongside VAEs on dataset representation and path quality.
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.