论文标题
顺序运动计划的学习歧管
Learning Manifolds for Sequential Motion Planning
论文作者
论文摘要
具有约束的运动计划是许多现实世界机器人系统的重要组成部分。在这项工作中，我们研究了从数据中学习此类约束的多种学习方法。我们探索了从数据中学习隐式约束歧管的两种方法：变异自动编码器（VAE）和一种新方法，即相等性约束歧管神经网络（Ecomann）。为了将学习的约束纳入基于抽样的运动计划框架中，我们评估了他们从各个数据集中学习约束以及计划过程中产生的路径质量的能力的方法。
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.
