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.

中文翻译：

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顺序运动规划的学习流形

带有约束的运动规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，我们研究了多种学习方法以从数据中学习此类约束。我们探索了两种从数据中学习隐式约束流形的方法：变分自编码器 (VAE) 和一种新方法，即等式约束流形神经网络 (ECoMaNN)。为了将学习到的约束合并到基于采样的运动规划框架中，我们评估了这些方法从各种数据集中学习约束表示的能力以及规划过程中产生的路径质量。