Generating natural and physically feasible motions for legged robots has been a challenging problem due to its complex dynamics. In this work, we introduce a novel learning-based framework of autoregressive motion planner (ARMP) for quadruped locomotion and navigation. Our method can generate motion plans with an arbitrary length in an autoregressive fashion, unlike most offline trajectory optimization algorithms for a fixed trajectory length. To this end, we first construct the motion library by solving a dense set of trajectory optimization problems for diverse scenarios and parameter settings. Then we learn the motion manifold from the dataset in a supervised learning fashion. We show that the proposed ARMP can generate physically plausible motions for various tasks and situations. We also showcase that our method can be successfully integrated with the recent robot navigation frameworks as a low-level controller and unleash the full capability of legged robots for complex indoor navigation.

翻译：生成足式机器人自然且物理可行的运动因其复杂的动力学特性一直是一项具有挑战性的问题。本文提出一种基于学习的自回归运动规划器（ARMP）新型框架，用于四足机器人的运动生成与导航。与多数针对固定轨迹长度的离线轨迹优化算法不同，我们的方法能够以自回归方式生成任意长度的运动规划。为此，我们首先通过求解大量不同场景与参数配置下的密集轨迹优化问题构建运动库，随后以监督学习方式从数据集中学习运动流形。实验表明，所提出的ARMP能够为各类任务与情境生成物理合理的运动。我们还展示了该方法可作为底层控制器与现有机器人导航框架成功集成，充分发挥足式机器人在复杂室内导航中的完整能力。