Due to the rapid development of the quadruped robot industry in the past decade, various commercial quadruped robots have emerged with distinct physical attributes. Different from the previous work in which the designed controller is robot-specific, this article proposes a learning-based control framework – MorAL, which is adaptive to different morphologies of quadruped robots and challenging terrains. Our framework concurrently trains the control policy and an adaptive module, which considers the temporal robot states. This module empowers the control policy to implicitly online identify different robot platforms' properties and estimate body velocity. Extensive experiments in the real world and simulation demonstrate that our controller enables robots with significantly different morphology to overcome various indoor and outdoor harsh terrains.

中文翻译：

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MorAL：在具有挑战性的地形上为四足机器人学习形态自适应运动控制器

由于近十年来四足机器人产业的快速发展，各种具有鲜明物理属性的商用四足机器人应运而生。与之前设计的控制器是针对机器人的工作不同，本文提出了一种基于学习的控制框架——MorAL，它能够适应四足机器人的不同形态和具有挑战性的地形。我们的框架同时训练控制策略和自适应模块，该模块考虑时间机器人状态。该模块使控制策略能够隐式在线识别不同机器人平台的属性并估计车身速度。现实世界和模拟中的大量实验表明，我们的控制器能够使具有显着不同形态的机器人克服各种室内和室外恶劣地形。