逐次動作計画のための多様体学習
Learning Manifolds for Sequential Motion Planning
VAEと新手法ECoMaNNを用いてデータから暗黙の制約多様体を学習し、サンプリングベース動作計画に組み込んで経路品質を評価した論文。
著者: Isabel M. Rayas Fernández, Giovanni Sutanto, Peter Englert, Ragesh K. Ramachandran, Gaurav S. Sukhatme
分類: cs.RO, cs.CG
原文アブストラクト
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.
