Using doors is a longstanding challenge in robotics and is of significant practical interest in giving robots greater access to human-centric spaces. The task is challenging due to the need for online adaptation to varying door properties and precise control in manipulating the door panel and navigating through the confined doorway. To address this, we propose a learning-based controller for a legged manipulator to open and traverse through doors. The controller is trained using a teacher-student approach in simulation to learn robust task behaviors as well as estimate crucial door properties during the interaction. Unlike previous works, our approach is a single control policy that can handle both push and pull doors through learned behaviour which infers the opening direction during deployment without prior knowledge. The policy was deployed on the ANYmal legged robot with an arm and achieved a success rate of 95.0% in repeated trials conducted in an experimental setting. Additional experiments validate the policy's effectiveness and robustness to various doors and disturbances. A video overview of the method and experiments can be found at youtu.be/tQDZXN_k5NU.

翻译：使用门道是机器人学中长期存在的挑战，在赋予机器人进入以人为中心的空间方面具有重要的实际意义。该任务具有挑战性，原因在于需要在线适应变化的门属性，并在操纵门板和穿越有限的门道空间时进行精确控制。为此，我们提出了一种基于学习的控制器，用于使腿式机械臂能够开启并穿越门道。该控制器在仿真环境中通过师生方法进行训练，以学习鲁棒的任务行为，并在交互过程中估计关键的门属性。与先前工作不同，我们的方法采用单一控制策略，能够通过在学习到的行为中推断开启方向（无需先验知识）来处理推门和拉门两种情况。该策略部署在配备机械臂的ANYmal腿式机器人上，在实验环境中进行的重复试验中达到了95.0%的成功率。额外的实验验证了该策略对各种门型和干扰的有效性与鲁棒性。该方法及实验的视频概述可在 youtu.be/tQDZXN_k5NU 查看。