让机器人用腿和机械臂自动开任意方向的门，成功率95%。
Learning to Open and Traverse Doors with a Legged Manipulator
- 基于强化学习训练单策略控制器，自适应判断开门方向。
- 在真实环境中对多种门实现95%成功率，抗干扰能力强。
- 适合需要自主穿行复杂环境的移动机器人研究者。
使用门是机器人领域长期存在的挑战，具有重要的实际意义，可提升机器人在人类空间中的可达性。该任务困难在于需在线适应不同门的属性，并精确控制门板操作及通过狭窄门道。为此，我们提出一种基于学习的控制器，用于腿式机械臂开闭门并穿越。控制器在仿真中采用教师-学生框架训练，学习鲁棒的任务行为，并在交互过程中估计关键门属性。与以往工作不同，本方法为单一控制策略，可通过学习行为在部署时推断开门方向，无需预先知道门的类型。该策略在搭载机械臂的ANYmal腿式机器人上实现，在实验环境中重复测试成功率达95.0%。额外实验验证了策略在多种门型和扰动下的有效性与鲁棒性。方法与实验视频见：youtu.be/tQDZXN_k5NU。
原文摘要 · Abstract (English)
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.
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