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arXiv 2409.04882cs.ROcs.AIcs.LG
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学习使用腿式操作机器人开门并穿过门

Learning to Open and Traverse Doors with a Legged Manipulator

Robotic Systems Lab, ETH Zurich(苏黎世联邦理工学院机器人系统实验室)

Mike Zhang, Yuntao Ma, Takahiro Miki, Marco Hutter

AI总结:

本文提出一种基于学习的单一控制策略,使带机械臂的 ANYmal 腿式机器人无需先验知识即可推断门的开启方向,完成推拉门并穿行,实验成功率达 95.0%。

摘要:

使用门是机器人领域长期存在的挑战,并且在让机器人更自由地进入以人为中心的空间方面具有重要的实际意义。该任务颇具难度,因为机器人需要在线适应不同的门属性,并且在操控门板和穿过狭窄门口时实现精确控制。为解决这一问题,我们提出了一种面向腿式操作机器人的基于学习的控制器,用于开门并穿过门。该控制器在仿真中采用师生方法进行训练,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。与以往工作不同,我们的方法是一个单一控制策略,能够通过学习到的行为同时处理推门和拉门;该行为在部署期间推断开门方向,而无需先验知识。该策略被部署在搭载机械臂的 ANYmal 腿式机器人上,并在实验环境中进行的重复试验中取得了 95.0% 的成功率。额外实验验证了该策略对各种门和扰动的有效性与鲁棒性。有关该方法和实验的视频概述可在 youtu.be/tQDZXN_k5NU 查看。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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